# Coding properties in invertebrate sensory systems

Sylvia Anton, Anders Garm, Berthold G. Hedwig

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Sylvia Anton, Anders Garm, Berthold G. Hedwig. Coding properties in invertebrate sensory systems. Frontiers, pp.227, 2017, Frontiers Research Topics, 978-2-88945-106-7. 10.3389/978-2-88945-106-7. hal-02788562

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CODING PROPERTIES
IN INVERTEBRATE
SENSORY SYSTEMS

EDITED BY: Sylvia Anton, Anders Garm and Berthold G. Hedwig

PUBLISHED IN: Frontiers in Physiology

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ISBN 978-2-88945-106-7

DOI 10.3389/978-2-88945-106-7

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CODING PROPERTIES IN
INVERTEBRATE SENSORY SYSTEMS

Topic Editors:

**Sylvia Anton**, INRA-Agrocampus Ouest-Rennes 1 University, France

**Anders Garm**, University of Copenhagen, Denmark

**Berthold G. Hedwig**, University of Cambridge, UK

<figure><img src="image_4.png" /><figcaption>Foraging honey bee exposed to olfactory,
visual and mechanosensory cues while visiting
a flower (Copyright Antoine Abrieux)</figcaption></figure>

Animals rely on sensory input from their environ-
ment for survival and reproduction. Depending
on the importance of a signal for a given species,
accuracy of sensory coding might vary from pure
detection up to precise coding of intensity, qual-
ity and temporal features of the signal. Highly
sophisticated sense organs and related central
nervous sensory pathways can be of utmost
importance for animals in a complex environ-
ment and when using advanced communication
systems. In sensory systems different anatomical
and physiological features have evolved to opti-
mally encode behaviourally relevant signals at
the level of sense organs and central processing.
The wide range of organizational complexity,
in combination with their relatively simple and
accessible nervous systems, makes invertebrates
excellent models to study general sensory coding
principles. The contributions to this e-book illus-
trate on one hand particular features of specific
sensory systems, and on the other hand indicate
not only common features of sensory coding
across invertebrate phyla, but also similar pro-

cessing principles of complex stimuli between different sensory modalities. The chapters show that the extraction of behaviourally relevant signals from all environmental stimuli, as well as the detection of low intensity signals and the analysis of temporal features can be similar across sensory modalities, including olfaction, vision, mechanoreception, and heat perception.

**Citation:** Anton, S., Garm, A., Hedwig, B. G., eds. (2017). Coding Properties in Invertebrate Sensory Systems. Lausanne: Frontiers Media. doi: 10.3389/978-2-88945-106-7
# Table of Contents

**05 Editorial: Coding Properties in Invertebrate Sensory Systems**

Anders Garm, Berthold G. Hedwig and Sylvia Anton

**1. Olfaction**

**1.1 Coding of dynamic features of olfactory signals**

**08 Rising Background Odor Concentration Reduces Sensitivity of ON and OFF Olfactory Receptor Neurons for Changes in Concentration**

Maria Hellwig and Harald Tichy

**15 It takes two—coincidence coding within the dual olfactory pathway of the honeybee**

Martin F. Brill, Anneke Meyer and Wolfgang Rössler

**29 Intrinsic and Network Mechanisms Constrain Neural Synchrony in the Moth Antennal Lobe**

Hong Lei, Yanxue Yu, Shuifang Zhu and Aaditya V. Rangan

**1.2 Coding within a complex olfactory environment**

**53 Unexpected plant odor responses in a moth pheromone system**

Angéla Rouyar, Nina Deisig, Fabienne Dupuy, Denis Limousin, Marie-Anne Wycke, Michel Renou and Sylvia Anton

**70 A Challenge for a Male Noctuid Moth? Discerning the Female Sex Pheromone against the Background of Plant Volatiles**

Elisa Badeke, Alexander Haverkamp, Bill S. Hansson and Silke Sachse

**1.3 From olfactory input to motor output**

**82 Comparative Neuroanatomy of the Lateral Accessory Lobe in the Insect Brain**

Shigehiro Namiki and Ryohei Kanzaki

**1.4 Olfactory coding in an ecological and evolutionary context**

**96 The neural bases of host plant selection in a Neuroecology framework**

Carolina E. Reisenman and Jeffrey A. Riffell

**113 Neuroethology of Olfactory-Guided Behavior and Its Potential Application in the Control of Harmful Insects**

Carolina E. Reisenman, Hong Lei and Pablo G. Guerenstein

**1.5 Plasticity in the role of odorant binding proteins**

**134 BdorOBP83a-2 Mediates Responses of the Oriental Fruit Fly to Semiochemicals**

Zhongzhen Wu, Jintian Lin, He Zhang and Xinnian Zeng
**149 The Mouthparts Enriched Odorant Binding Protein 11 of the Alfalfa Plant Bug Adelphocoris lineolatus Displays a Preferential Binding Behavior to Host Plant Secondary Metabolites**

Liang Sun, Yu Wei, Dan-Dan Zhang, Xiao-Yu Ma, Yong Xiao, Ya-Nan Zhang, Xian-Ming Yang, Qiang Xiao, Yu-Yuan Guo and Yong-Jun Zhang

## 2. Vision

### 2.1 Coding of visual information in low light

**159 Flight control and landing precision in the nocturnal bee Megalopta is robust to large changes in light intensity**

Emily Baird, Diana C. Fernandez, William T. Wcislo and Eric J. Warrant

**166 Hunting in Bioluminescent Light: Vision in the Nocturnal Box Jellyfish Copula sivickisi**

Anders Garm, Jan Bielecki, Ronald Petie and Dan-Eric Nilsson

## 3. Temperature detection

### 3.1 Molecular actors of heat perception

**175 Heat Perception and Aversive Learning in Honey Bees: Putative Involvement of the Thermal/Chemical Sensor AmHsTRPA**

Pierre Junca and Jean-Christophe Sandoz

### 3.2 Long-range infrared sensing

**190 Concept of an Active Amplification Mechanism in the Infrared Organ of Pyrophilous Melanophila Beetles**

Erik S. Schneider, Anke Schmitz and Helmut Schmitz

## 4. Mechanoreception

### 4.1 Coding of mechanosensory information using quantitative and temporal elements

**198 Sequential Filtering Processes Shape Feature Detection in Crickets: A Framework for Song Pattern Recognition**

Berthold G. Hedwig

**213 Encoding of Tactile Stimuli by Mechanoreceptors and Interneurons of the Medicinal Leech**

Jutta Kretzberg, Friederice Pirschel, Elham Fathiazar and Gerrit Hilgen
<figure><img src="image_5.png" /></figure>

Check for
updates

# Editorial: Coding Properties in Invertebrate Sensory Systems

Anders Garm<fnref n="1" />, Berthold G. Hedwig² and Sylvia Anton³*

**Keywords:** sensory signal extraction, temporal coding, neuro-ethology, olfaction, vision, mechanoreception, heat detection

## Editorial on the Research Topic

### Coding Properties in Invertebrate Sensory Systems

Animals adapt their behavior according to the environment and their specific needs in a given situation. In order to do so in an appropriate way, they need to detect, analyze, and code the relevant sensory cues. This task is handled by sensory systems and their associated parts in the central nervous system. With few exceptions, the amount of information present in the environment and thus in principle available to sensory systems, is close to infinite. It is impossible and not desirable to encode and process all the information. Therefore, the first and most important task of any sensory system is to filter and select only the essential information—information, which potentially will improve the fitness of the bearer. Differences in sensory information processing occur between animals of different organization levels. Sensory coding in invertebrates and vertebrates relies on multiple stages of processing to extract information relevant to the survival of the individual. The wide range of organizational complexity, in combination with their relatively simple and accessible nervous systems, makes invertebrates excellent models to study general sensory coding principles. In addition, many invertebrate species are of socio-economic importance as pollinators, crop pests, or as disease vectors or elicitors. Therefore, understanding their communication systems and sensory biology is important for the development of insect management or plant protection strategies.

In the present Research Topic in Frontiers in Invertebrate Physiology we present a series of original papers and reviews illustrating the current directions of this field. The different contributions indicate not only common features of sensory coding across invertebrate phyla, but also similar processing features of complex stimuli between different sensory modalities. This is interesting, because the characteristics of the different types of sensory stimuli are inherently different, and require different types of detectors and potentially different ways of integration in nervous systems. The majority of the papers treat the coding of olfactory information with its multidimensionality, which was supposed to function under different operational constraints than other sensory modalities, and has been a field of high research activity over the past years. However, the articles of this Research Topic show that the extraction of behaviourally relevant signals from all incoming environmental stimuli, as well as the detection of low intensity signals and the analysis of temporal features can be similar across different sensory modalities, including olfaction, vision, mechanosensation, and heat perception.

The papers treating the coding of olfactory information include work on temporal processing and signal extraction in complex environments, and its behavioral outcomes as a function of physiological state, as well as potential applications of the findings to control harmful insects.

**OPEN ACCESS**

**Edited by:**

Xanthe Vafopoulou,
York University, Canada

**Reviewed by:**

Wolfgang Rössler,
University of Würzburg, Germany
Andrew Dacks,
West Virginia University, USA

**\*Correspondence:**

Sylvia Anton
sylvia.anton@inra.fr

**Specialty section:**

This article was submitted to
Invertebrate Physiology,
a section of the journal
Frontiers in Physiology

**Received:** 01 December 2016

**Accepted:** 23 December 2016

**Published:** 10 January 2017

**Citation:**

Garm A, Hedwig BG and Anton S
(2017) *Editorial: Coding Properties in
Invertebrate Sensory Systems*.
*Front. Physiol.* 7:688.
doi: 10.3389/fphys.2016.00688

<footnote n="1">¹ Marine Biological Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark, ² Department of Zoology, University of Cambridge, Cambridge, UK, ³ Neuroéthologie-RCIM, Institut National de la Recherche Agronomique-Université d'Angers, Beaucouzé, France</footnote>
One of the major challenges for olfactory systems is to extract behaviourally relevant information from highly complex and dynamic signals. Hellwig and Tichy propose in a perspective article that olfactory receptor neurons in cockroaches, which signal sudden changes in the concentration of olfactory stimuli as compared to a constant background stimulation, might be used for tracking behaviourally relevant odor plumes. The fact that OFF neurons code better for falling concentration changes than ON neurons suggests, that they play a role in alerting a loss of an odor plume. The paper by Brill et al. shows that parallel processing of olfactory information via two antennal lobe output tracts in the honey bee comprises coincident activation patterns of projection neurons within and across parallel tracts. The results from simultaneous recordings of olfactory projection neurons in both tracts support the role of spike timing in coding olfactory information (temporal code). Another physiological property of the insect antennal lobe, which is potentially important in temporal coding of insect olfaction, is the afterhyperpolarization-phase of the projection neurons. Lei et al. show in their paper, through pharmacological experiments and modeling, some of the control mechanisms of the afterhyperpolarization phase, and confirm the involvement in temporally resetting the system for further odor-specific responses.

Another challenge for olfactory systems is the extraction of a highly relevant signal from an odor background, such as detection of moth sex pheromones in a rich background of plant volatiles. Whereas the common belief was that sex pheromone and plant odors are processed and encoded by two distinct pathways in the insect brain, Rouyar et al. show for the first time that a structurally dissimilar plant-derived odorant is able to activate the pheromone specific pathway and thus might influence pheromone processing. Badeke et al. demonstrate, however, that even though high concentrations of single plant odors can influence female tracking in the male noctuid moth Heliothis virescens, sex pheromone-guided behavior is not influenced by plant odors at natural concentrations.

Sensory processing and integration of different stimuli in higher brain centers ultimately leads to adequate motor output. The review by Namiki and Kanzaki addresses the function of the lateral accessory lobe in the insect brain, which is at the interface between multimodal sensory input and locomotor output. This brain area is believed to be an important output region of the brain for the control of locomotion. In a comparative approach, structure and function of lateral accessory lobe neurons in different insect species are discussed.

Because olfaction is widely used by many insect species, it has become also an important model for sensory ecology and is exploited to develop alternative methods to control harmful insects. Reisenman and Riffell review the neurobiology of host plant selection in the moth Manduca sexta in an ecological context. Reisenman et al. evaluate how results from the field of neuroethology can be used to elucidate how harmful insects, be it crop pests or insects transmitting diseases, trace the odors of their host plants or animal hosts. They summarize the current knowledge on the use of semiochemicals, and how results

from applied studies improve our knowledge on detection and processing of olfactory signals.

The tracking of host plants sometimes changes between different life stages, and Wu et al. show that one of the odorant binding proteins (OBPs), binding to attractant semiochemicals, is upregulated in mated females of a pest fruit fly. This upregulation possibly accounts for an increased attraction to their host plant. The role of OBPs in chemosensory detection and coding is also addressed by Sun et al. Although OBPs are often found to be necessary for odor discrimination, the authors show that the function of homologous OBPs in bugs can change between species to binding of non-volatiles in the gustatory system.

The visual system basically counts the number of photons from a certain direction during a defined time period, and potentially also measures their energy (color) and their polarity. The challenges here are to extract behaviourally relevant information under limiting environmental conditions. Two papers of this Research Topic deal with coding in the visual system under low light conditions. Two strategies to enhance vision in darkness are through temporal and/or spatial summation of the photons, which will increase sensitivity on the cost of temporal and spatial resolution, respectively. Nocturnal bees enhance sensitivity through optical specializations but not through temporal summation, which would probably hamper their flight control at night (Baird et al.). Garm et al. examine vision in a nocturnal box jellyfish, which enhances sensitivity by having both a low temporal and spatial resolution. This visual system seems to be optimized to code only one highly specific information, the direction of bioluminescent flashes indicating high prey densities.

Two papers in this Research Topic deal with different forms of heat perception. The molecular actors of heat perception have been very little investigated so far. Junca and Sandoz tested the association of a heat shock with aversive olfactory conditioning of the sting extension response, and show that the TRP channel HsTRPA may be involved in heat detection in honeybees. Infrared (IR) sensing has been considered as either a specialized type of vision or thermoreception. In Jewel beetles, Schneider et al. propose a theory, which would allow bimodal photomechanic sensilla housed in IR organs to increase sensitivity to weak IR signals during flight, by making use of muscular energy coupled out of the flight motor. This mechanism could explain the capacity of these beetles to detect wood fires over distances of more than 100 km, allowing to find the resources for larvae developing in fire-killed trees.

Two contributions deal with different aspects of processing and coding of mechanosensory information. Hedwig reviews how a series of filters at different levels of the auditory pathway is used to process and code the male song by the female cricket. Carrier frequency, pulse duration and the pulse pattern are serially processed and finally tune the female phonotactic behavior to the characteristic properties of the male calling song. The other paper examines touch sensing in leeches. Kretzberg et al. show that touch- and pressure-sensitive cells in the skin, converging on common interneurons, both use quantitative and temporal elements to encode the precise location of tactile stimuli in order to elicit minimal, but precise avoiding behavior.
Altogether the work presented in this Research Topic shows the advantage of studying coding of behaviourally active sensory stimuli in invertebrates of different phyla as it reveals common features in the processing of complex sensory signals and different modalities.

## AUTHOR CONTRIBUTIONS

All authors listed, have made substantial, direct and intellectual contribution to the work, and approved it for publication.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Garm, Hedwig and Anton. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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CrossMark

# Rising Background Odor Concentration Reduces Sensitivity of ON and OFF Olfactory Receptor Neurons for Changes in Concentration

Maria Hellwig and Harald Tichy *

Department of Neurobiology, Faculty of Life Sciences, University of Vienna, Vienna, Austria

The ON and OFF ORNs on cockroach antennae optimize the detection and transfer of information about concentration increments and decrements by providing excitatory responses for both. It follows that the antagonism of the responses facilitates instantaneous evaluations of the odor plume to help the insect make tracking decisions by signaling "higher concentration than background" and "lower concentration than background". Here we analyzed the effect of the background concentration level of the odor of lemon oil on the responses of the ON and OFF ORNs to jumps and drops of that odor, respectively. Raising the background level decreases both the ON-ORN's response to concentration jumps and the OFF-ORN's response to concentration drops. Impulse frequency of the ON ORN is high when the concentration jump is large, but for a given jump, frequency tends to be higher when the background level is low. Conversely, impulse frequency of the OFF cell is high at large concentration drops, but higher still when the background level is low. Analyses of this double dependence revealed that the activity of both types of ORNs is raised more by increasing the change in concentration than by decreasing the background concentration by the same amount. This effect is greater in the OFF ORN than in the ON ORN, indicating a bias for falling concentrations. Given equal change in concentration, concentration drops evoke stronger responses in the OFF ORN than concentrations jumps in the ON ORN. This suggests that the OFF responses are used as alert information for accurately tracking.

**Keywords:** odor concentration coding, ON and OFF responses, asymmetric sensitivities, effect of background concentration, gain of responses

## INTRODUCTION

An insect tracking a turbulent odor plume to its source perceives the odor signal as a sequence of pulses of high concentration interspersed with the surrounding medium containing gaps of low or zero concentration (Moore and Atema, 1991; Zimmer-Faust et al., 1995; Vickers, 2000). Key features pertaining to the location of an odor source are the timing of concentration jumps and concentration drops as well as the level of odor concentration between these changes, referred to here as background concentration. The effect of the background value on the responses of ORNs

## OPEN ACCESS

**Edited by:**
Sylvia Anton,
Institut National de la Recherche
Agronomique, France

**Reviewed by:**
Silke Sachse,
Max Planck Institute for Chemical
Ecology, Germany
Martin F. Brill,
Howard Hughes Medical Institute,
USA

*Correspondence:
Harald Tichy
harald.tichy@univie.ac.at

**Specialty section:**
This article was submitted to
Invertebrate Physiology,
a section of the journal
Frontiers in Physiology

**Received:** 26 November 2015

**Accepted:** 11 February 2016

**Published:** 01 March 2016

**Citation:**
Hellwig M and Tichy H (2016) Rising Background Odor Concentration Reduces Sensitivity of ON and OFF Olfactory Receptor Neurons for Changes in Concentration. *Front. Physiol.* 7:63.
doi: 10.3389/fphys.2016.00063
to superimposed concentration pulses was first investigated in the lobster (Borroni and Atema, 1988) and more recently described in the housefly (Kelling et al., 2002). Those experiments tested single synthetic compounds, and the analysis was limited to the concentration jump at the onset of the pulse. While with increasing background level the response of lobster ORNs to concentration jumps gradually diminished, the response of housefly ORNs was enhanced to small jumps and reduced to large jumps. The response enhancement was interpreted as the result of depolarization or diminution of the resting membrane potential due to the presence of the background odor. The response reduction was attributed to competition between stimulus and background molecules for membrane receptors (Borroni and Atema, 1988; Kelling et al., 2002).

Based on these studies, two statements can be made. The first is that ORNs are detectors for the relative rather than the absolute concentration. The second is that the higher the background level, the weaker the responses to rapidly increasing odor concentration. An animal tracking a turbulent odor plume will therefore perceive the same concentration jump progressively weaker the closer it approaches the source. However, weak responses signify less sensory evidence than strong responses. Such constraint has been described in the neural circuits implementing the binary decision about the direction of a motion stimulus, which determines saccadic eye movement in the rhesus monkey (Roitman and Shadlen, 2002; Bogacz et al., 2009). The speed of decision is associated with integrator neurons in pre-motor brain areas which gradually increase their discharge rate by accumulating the inputs of sensory neurons over time. With decreasing difference between the baseline activity of these integrator neurons and the response threshold of non-integrator neurons, decisions are prone to errors. Simply sampling of information sequentially facilitates accurate detection and decision-making under uncertain conditions (Heitz and Schall, 2012; Heitz, 2014). However, sampling as little as possible will save time and effort (Drugowitsch and Pouget, 2012). In view of the situation of the olfactory system, it would be a possible economical alternative to create a separate system of ORNs with a different coding mechanism. Such a strategy of sensory coding and information processing seems to be realized in the ON and OFF ORNs on the cockroach's antennae: they produce opposite responses to changes in odor concentration (Hinterwirth et al., 2004; Tichy et al., 2005; Burgstaller and Tichy, 2011, 2012). The discharge rate of the ON ORNs is increased by raising odor concentration and decreased by lowering it, and the discharge rate of the OFF ORNs is increased by lowering odor concentration and decreased by raising it. During moment-to-moment contact with the odor signal, the activity of the ON ORN increases when the odor concentration jumps to a higher value when contact is made with an odor pulse. Conversely, the activity of the OFF ORN increases when odor concentration drops to a lower value after encountering an odor gap. A recent study reveals a bias for concentration drops, suggesting a bias for detecting the loss of contact with the odor signal (Burgstaller and Tichy, 2011).

In this study, we quantified the simultaneous dependence of the ON and OFF ORNs on the background level and the

superimposed concentration jumps or drops of the same odor, respectively. We used the complex odor of lemon oil emanating from citrus fruits as odor stimulus and we determined the gain of response for the background concentration and for the jumps or drops in concentration. In particular we asked: (i) what is the difference between the two gain values in each type of ORN, and (ii) what is the difference between the two types of ORNs? A falling-concentration bias results in overestimation of concentration drops relative to jumps. In terms of accuracy, a drop would be perceived by the cockroach as being larger than it actually is. We examined whether the disparity between the ON and OFF responses to equal concentration jumps and drops depends on the amplitude of the change and the background level. A disparity for larger concentration changes at higher background levels would be an advantage for receiving information about large jumps at the lateral edges of the plume than small jumps within the odor plume. Asymmetry in the neural coding of concentration jumps and drops is at the root of understanding what characteristics of the odor signal are important for tracking a turbulent odor plume.

## MATERIALS AND METHODS

An adult male cockroach was anesthetized with CO₂ and fixed on a Perspex holder with strips of Parafilm wrapped around the holder. The antenna was fastened with adhesive tape and dental cement on a Perspex stage projecting from the holder. Action potentials were recorded extracellularly with electrolytically sharpened tungsten electrodes. One electrode was placed lengthwise into the tip of the antenna and the other was inserted into the base of the sensillum. The recorded signals were amplified (NPI, SEC-05X) and filtered (0.1–3 kHz), passed through a CED 1401plus (Cambridge Electronic Design, Cambridge, UK; 12 bit, 10 kHz) interface connected to a PC for on-line recording. Spikes were detected and classified off-line using commercial software (spike2, version 6). Impulse frequency (imp/s) is the per-second impulse count for fixed periods of 0.2 s.

The gain of response is defined as the ratio of output to input and given by the slopes of the regression planes that approximate the relation between impulse frequency, background concentration and concentration change ($F = y_0 + aC + b\Delta C$; where F is the impulse frequency and $y_0$ the height of the regression plane, a is the background concentration and b the concentration change). The $R^2$ coefficient of determination indicates how well the regression plane approximates the real data points.

The odor of lemon oil (Art. 5213.1; Carl Roth GmbH + Co.KG; Karlsruhe, D) was applied by an air stream merging at 2 m/s from a glass tube 7 mm in diameter. An air dilution olfactometer was used to control odor concentration (Burgstaller and Tichy, 2011). Compressed clean air was divided into two streams and their flow rates were controlled by passing them through mass flow meters. Each stream was led through a 25-l tank; the first tank contained the liquid odorant and the second tank was empty. After flowing out from the tank, each air stream was passed through an electrical proportional valve (Kolvenbach KG, KWS ¾) and
<figure><img src="image_7.png" /><figcaption>**FIGURE 1** | Simultaneously recorded discharge rates of a pair of ON and OFF ORNs from a single sensillum exposed to the odor of lemon oil at constant concentration of 20%. The ON ORN (green) displayed smaller impulse amplitudes than the OFF ORN (blue). *Inset* The waveform sensitive template mechanism (spike2) was used to identify and sort action potentials from ON and OFF ORNs.</figcaption></figure>

an air flow sensor (AMW 3000; Honeywell). The two streams were then combined. In order to hold the total flow rate of the combined air stream constant, the phase of the control voltages of the proportional vales was shifted by 180°. Instantaneous odor concentration was determined by the flow rate ratio of the odor-saturated air to clean air and indicated by the percentage of saturated air in the air steam playing on the antenna. The amplitude of the concentration change was described by the difference between the background level and concentration of the odor pulse. A positive value (+ΔC) indicates a concentration jump and a negative value (−Δ) a concentration drop. After adaptation for 30 s to a constant background level, there followed a series of concentration changes to various higher or lower concentrations, each of which was maintained for 1 s before the return to the background value. The steps were presented every 30 s. This paradigm enabled testing at least four series of concentration jumps or concentration drops from different background levels on each of 13 ON and OFF ORNs, respectively.

## RESULTS

The ON and OFF ORNs occur together in short, slightly curved hair-like sensilla on the distal margin of each antennal segment. Both ORNs were encountered simultaneously by penetrating the recording electrode gently into the sensillum base. The recordings usually contained the impulses from both ORNs, which could be easily separated by their amplitudes (Figure 1). The impulse trains were sorted into the responses of the ON and OFF ORNs by using a waveform-sensitive template matching mechanism (Figure 1, inset).

The experiment shown in Figure 2 illustrates some of the parameters determining the responses of the ON and OFF ORNs. The experiment involved four different background levels of the lemon oil odor (0, 40, 60, and 100%) as well as two 60% jumps and two 60% drops in the concentration of that odor. The ON ORN responded to the concentration jumps with a phasic increase in impulse frequency followed by a decline during the 1-s pulse period. The frequency of the ON ORN was higher at the low (Figure 2A) than at the high background level (Figure 2B). The

OFF ORN fell silent for the pulse period. The impulse frequency of the OFF ORN, in contrast, rose rapidly at an odor gap, followed by a decline during the 1-s gap period. Similarly to the ON ORN, the frequency of the OFF ORN was higher at the low (Figure 2D) than at the high level (Figure 2C). The ON ORN ceased discharging during the gap period.

To quantify the effect of the background on the ON-ORN's response to concentration jumps, four concentration series were tested at different levels in the 0–40% range. Frequency increased with the amplitude of the jump, but more rapidly the lower the background. As the equal-frequency line in Figure 3A illustrates, it takes a 60% concentration jump to elicit 10 imp/s at 40% level, but only a 13% jump at 0% level.

The effect of the background on the OFF-ORN's response to concentration drops was described by testing four concentration series at different levels between 40 and 100%. The data obtained resembled those from the ON ORN, inasmuch as frequency of the OFF ORN increased with the amplitude of the drops. The increase in frequency was more rapid the lower the background. This relationship is exemplified in Figure 3B. The equal-frequency line indicates that it takes a 64% concentration drop to elicit 30 imp/s at 100% level, but only a 26% drop at 40% level.

Multiple regressions ($F = y_0 + aC + b\Delta C$; where F is the impulse frequency and $y_0$ the height of the regression plane) were calculated to determine the simultaneous effect of the background concentration (a-slope) and the jump or drop in concentration (b-slope) on the frequency of the ON and OFF ORN, respectively. The slopes demonstrate the three properties that characterize both types of ORNs: (i) the sign of the a-slope is negative for the ON and OFF ORNs—that is, a decrease in the background raises the frequency of both ORNs to concentration changes; (ii) the sign of the b-slope is positive for the ON ORN and negative for the OFF ORN—that is, an increase in concentration jumps raises the frequency of the ON ORNs and an increase in concentration drops raises the frequency of the OFF ORNs, and (iii) the slopes are steeper for the OFF than for the ON ORN—that is, changes in both the background and in the size of the change have stronger effects on the frequency of the OFF than on that of the ON ORN with due consideration of the sign.

In all 13 examined ON ORNs and 13 OFF ORNs the coefficients of determination of the multiple regressions show a strong linear relationship between impulse frequency, the background level and the concentration change ($R^2 > 0.95$ in Figures 3C,D). The slopes of the regression planes emphasize the gain of responses for the background concentration (a-slope) and the concentration change (b-slope). In the ON ORN, the mean gain for jumps was 0.2 imp/s per Δ%, and the mean gain for the background was −0.1 imp/s per %. Frequency can be raised more by increasing the jump by yet another percent than by decreasing the background by 1%. Thus, an increase of 1 imp/s can be elicited either by a 5% increase in the concentration jump or by a 10% decrease in the background.

In the OFF ORN, the mean gain for concentration drops was 0.4 imp/s per −Δ% and the mean gain for the background was −0.2 imp/s per %. Frequency can be raised more by increasing the concentration drop by still another percent than by changing
<figure><img src="image_8.png" /></figure>

**FIGURE 2** | Simultaneously recorded responses of an ON ORN and an OFF ORN during the final 1-s of a 30-s presentation of different constant background concentration of the lemon oil odor, followed by a 1-s presentation of higher or lower odor concentrations and a return to the initial background value. (A,B) Concentration jumps (+60Δ%) from 0 to 40% background level, respectively. (C,D) Concentration drops (−60Δ%) from 100 to 60% background level, respectively. *a* time course of odor concentration. *b* extracellular recorded action potentials; the OFF ORN displayed larger impulse amplitudes than the ON ORN. *c* action potentials represented as raster plots. *d* responses ORNs represented as time histograms (bin width, 0.2 s).

the background by 1%. Thus, an increase of 1 imp/s can be elicited either by a 2.5% increase in the concentration drop or by a 5% decrease in the background. The sensitivity of the OFF ORNs for concentration changes superimposed on the background level is twice as high as that of the ON ORNs.

DISCUSSION

ON and OFF ORNs responding antagonistically to increments and decrements of the same odor have been described so far only in the cockroach (Hinterwirth et al., 2004; Tichy et al., 2005; Burgstaller and Tichy, 2011, 2012). This may be due to technical reasons. First, the odor stimulus used by the cited authors was provided by means of an air dilution olfactometer. This set-up allowed continuous presentation of odor-loaded air and enabled conditioning the OFF ORN to high concentration levels before dropping to low or zero concentration values. Second, a natural odor was used for stimulation instead of single compounds. We do not know, however, which compounds contained in the odor of lemon oil are responsible for eliciting the antagonistic responses.

In the lobster (Borroni and Atema, 1988) and the housefly (Kelling et al., 2002), increasing the background level reduced the responsiveness of ORNs to concentration jumps (Kelling et al., 2002). A similar effect has been observed for the ON ORN of the cockroach (Burgstaller and Tichy, 2011). Furthermore, the

OFF ORNs fit well with this observation because the response to concentration drops decreases with increasing background. However, the responses of the ON and OFF ORNs are not mirror images. The responses of the latter span a larger frequency range than the former, which means that the OFF ORNs respond with higher frequencies to concentration drops than the ON ORN to equivalent jumps (Burgstaller and Tichy, 2011).

In this study we determined the gain of responses of the ON and OFF ORNs for background concentration and superimposed changes of the same odor. In the OFF ORNs, the gain values are twice as high as in the ON ORNs. Thus, falling concentration holds greater salience than rising concentration. Furthermore, with increasing background, the disparity between rising and falling values becomes grater. Notwithstanding this difference, the relationship between the gain values for background concentration and for concentration changes are similar in the ON and OFF ORNs: the value for changes in both types of ORNs is twice as high as the value for the background concentrations.

The stronger gain for changes vs. background reflects the significance of the dynamic aspect of the stimulus. Since the dominance of the gain for concentration change increases with the amplitude of the change and decreases with falling background level, the magnitude of response of an ORN cannot be predicted by simply adding background to change values. This conclusion was drawn from the regression functions in **Figures 3C,D**. By direct comparisons, an ON ORN will respond to an end-value of 80% attained by a 60% jump from a 20%
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

**FIGURE 3** | (A,B) Responses of a single ON ORN (A) and a single OFF ORN (B) plotted as a function of background concentration and jumps or drops in odor concentration, respectively. Dotted horizontal lines: equal frequency line at 10 imp/s for the ON ORN and 30 imp/s for the OFF ORN. (C,D) Mean responses of 13 ON ORN (C) and 13 OFF ORN (D) plotted as a function of background concentration and jumps or drops in odor concentration, respectively. Error bars represent SEM. Multiple regressions which utilize 3-dimensional planes (F = y₀ + aC + bΔC; where F is the impulse frequency, and y₀ the height of the regression plane) were calculated to determine the gain for background concentration (a slope) and the concentration change (b slope) on the response. Note that the sign of the concentration axis in (A,C) is oriented in different direction than in (B,D). *N* number of ORNs, *n* number of points used to calculate regression plane (A,B) or mean responses (C,D), R² coefficient of determination.

background with 18.8 imp/s, but to the same end-value of 80% attained by a 20% jump from an 80% background with 4.8 imp/. An OFF ORN will respond to an end-value of 20% attained by an 60% drop from a 80% background with 27.3 imp/, but to the same end-value of 20% attained by a 20% drop from a 40% background with 19.3 imp/s.

Another conclusion from **Figures 3C,D** is that the background concentration set limits to the dynamic responses of both types of ORN. With increasing background concentration, equal increments in jumps or equal decrements in drops do not cause equal increments in the rate of discharge of the ON and OFF ORNs. Instead, the increments in the discharge rate of both ORNs become progressively smaller. This compressed scaling has some advantages. An ON ORN whose sensitivity to concentration jumps is best at low backgrounds, and decreases

as the background from which the jump to be detected increases, provides strong sensory evidence when the cockroach encounters an odor plume. At low backgrounds it will be important to have available a wider dynamic frequency range in order to differentiate between small-amplitude jumps. The same small differences at high backgrounds could be trivial. Conversely, an OFF ORN whose sensitivity to concentration drops is best at low backgrounds, and decreases as the background from which the drop to be detected increases, provides strong sensory evidence at large concentration decreases, when the cockroach approaches the lateral edge of the plume or even leaves the plume. At low backgrounds it will be important to have available a wider dynamic frequency range in order to distinguish between small-amplitude drops. The same small differences at high backgrounds may be less important. Nonetheless, small-amplitude differences
<figure><img src="image_17.png" /><figcaption>**FIGURE 4** | Schematic representation of turbulent plume features proposed as potentially relevant to plume tracking, including odor pulses and odor gaps separated by the background odor concentration that gradually increases with decreasing distance to the source (lemon fruits). A cockroach walking toward the source will perceive the spatial distribution of odor concentration as temporal concentration change. By using simultaneous or successive sampling strategies, concentration differences determined by bilateral comparison of the responses of ON and OFF ORNs on both antennae will guide the cockroach to move forward or return to the previous position. Response bias for equal-amplitude concentration drops relative to jumps enhances detection of low concentration change with rising background level. Therefore, the OFF responses are used as alert information for accurately tracking motion.</figcaption></figure>

may bear a vital message too. Therefore, there must be some mechanism to secure the information conveyed by responses which become progressively weaker and prevent loss of contact with the odor signal. Such a mechanism seems to be realized by the bias of the antagonistically responding ON and OFF ORNs.

Classical concepts of odor plume tracking use spatial and temporal sampling to explain the mechanisms underlying initiation of a response and controlling the orientation of an organism to an odor source (Vickers, 2000; Willis, 2008). Irrespectively of whether bilateral or sequential comparison of odor concentrations is used for orientation, a cockroach following a background concentration gradient should balance between the responses of the ON and OFF ORNs. Clearly, strong responses of an ON ORN indicate the direction toward the odor source. Weak responses will also do so, provided that a change in the insect's course produces a stronger response in the OFF ORN. Strong responses of the OFF ORN indicate that concentration is falling. From a perceptual perspective, falling-concentration bias results in an overestimation of drops relative to jumps. In terms of accuracy, drops are not only perceived by the cockroach as being stronger than they actually are, they also specify the location of plume edges to be closer than they are. In this view, the cockroach uses the responses of the ON ORNs for distance information and the responses of the ON ORNs as alert or warning information. From the cockroach's perspective, tendencies in concentration changes rather than exact values of concentration change suffice for responding appropriately to odor pulses and odor gaps and provide timely arrival at the odor source (Figure 4).

The discharge of ORNs to concentration jumps superimposed on different background levels have recently been described in the fruit fly. In the experiments with OR59b ORNs (Kim et al., 2011), odor stimulation consisted of a step-like sequence of 3 different concentrations of acetone. Each concentration was presented for 2s and created the background for the next step. After an initial phasic increase to the concentration step, the ORNs displayed relatively constant rates of discharge over the 2-s stimulation period. The peak discharge to equal-amplitude acetone steps gradually decreased with increasing background level. Therefore, ORNs are unable to measure accurately the concentration change. In a study of ab3A ORNs it was shown that the peak discharge rates to 500 ms puffs of methyl butyrate, ethyl acetate and ethyl butyrate decreased with increasing background concentration (Martelli et al., 2013). However, when the discharge rates to different concentration puffs were normalized by the peak responses to the same odorant, the diminishing effect of the increasing background concentration disappeared. Moreover, the dynamics of the normalized responses did not depend on the dynamics of the brief concentration puff, even if the concentration of the odor puff was varied. This independence of the normalized dynamic responses on the dynamics of the odor puff was interpreted as being a prerequisite for ab3A ORNs to use the dynamics of their responses for mediating characteristics of the odor stimulus such as the presence of different compound in the mixture.

The inability of ORNs in insects and crustaceans to accurately measure the magnitude of concentration change is not a matter of variance of the discharge rates. It is rather the concession of their additional dependence on the background concentration. Since the ON and OFF ORNs adapt relatively slowly and only partially to the background level, the discharge rates signal relative concentration changes. ORNs adapting minimally or not at all may be capable of signaling the actual level of odor concentration. Tonic systems are well suited to convey information about unchanging concentrations, but would fail to signal concentration changes because their ORNs remain excited after the change has ceased. Such maintained discharge would distort temporal information.

## AUTHOR CONTRIBUTIONS

MH and HT conceived and designed experiments, MH performed experiments and analyzed data; MH and HT interpreted results and wrote the paper; MH prepared figures; HT edited and revised manuscript.

## ACKNOWLEDGMENTS

This work was supported by Austrian Science Fund Grant P 21777-B17.
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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Hellwig and Tichy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
# It takes two—coincidence coding within the dual olfactory pathway of the honeybee

Martin F. Brill*†‡, Anneke Meyer‡ and Wolfgang Rössler*

Behavioral Physiology and Sociobiology, Biozentrum, University of Würzburg, Würzburg, Germany

OPEN ACCESS

**Edited by:**
Sylvia Anton,
Institut National de la Recherche
Agronomique, France

**Reviewed by:**
Dominique Martinez,
LORIA, France
Hong Lei,
University of Arizona, USA

*Correspondence:
Martin F. Brill,
Cold Spring Harbor Laboratory, 1
Bungtown Road, Cold Spring Harbor,
New York, NY 11724, USA
mbrill@cshl.edu;
Wolfgang Rössler,
Behavioral Physiology and
Sociobiology, Biozentrum, University
of Würzburg, Am Hubland, 97074
Würzburg, Germany
roessler@biozentrum.uni-wuerzburg.de

†Present Address:
Martin F. Brill,
Cold Spring Harbor Laboratory, Cold
Spring Harbor, New York, USA

‡These authors have contributed
equally to this work.

**Specialty section:**
This article was submitted to
Invertebrate Physiology,
a section of the journal
Frontiers in Physiology

Received: 05 May 2015

Accepted: 10 July 2015

Published: 28 July 2015

**Citation:**
Brill MF, Meyer A and Rössler W (2015) It takes two—coincidence coding within the dual olfactory pathway of the honeybee. *Front. Physiol.* 6:208.
doi: 10.3389/fphys.2015.00208

To rapidly process biologically relevant stimuli, sensory systems have developed a broad variety of coding mechanisms like parallel processing and coincidence detection. Parallel processing (e.g., in the visual system), increases both computational capacity and processing speed by simultaneously coding different aspects of the same stimulus. Coincidence detection is an efficient way to integrate information from different sources. Coincidence has been shown to promote associative learning and memory or stimulus feature detection (e.g., in auditory delay lines). Within the dual olfactory pathway of the honeybee both of these mechanisms might be implemented by uniglomerular projection neurons (PNs) that transfer information from the primary olfactory centers, the antennal lobe (AL), to a multimodal integration center, the mushroom body (MB). PNs from anatomically distinct tracts respond to the same stimulus space, but have different physiological properties, characteristics that are prerequisites for parallel processing of different stimulus aspects. However, the PN pathways also display mirror-imaged like anatomical trajectories that resemble neuronal coincidence detectors as known from auditory delay lines. To investigate temporal processing of olfactory information, we recorded PN odor responses simultaneously from both tracts and measured coincident activity of PNs within and between tracts. Our results show that coincidence levels are different within each of the two tracts. Coincidence also occurs between tracts, but to a minor extent compared to coincidence within tracts. Taken together our findings support the relevance of spike timing in coding of olfactory information (temporal code).

**Keywords:** olfaction, insect, coincidence, multi-electrode-recording, antennal lobe, mushroom body

## Introduction

Animals process sensory input rapidly in order to behave adequately in their natural environment. In order to manage this challenging task, neural systems have developed a broad variety of mechanisms. Among these, parallel processing and coincidence detection appear to be almost universally useful throughout modalities and animal taxa. Parallel processing codes different aspects of the same stimulus along separate pathways. This way it increases both computational capacity and overall processing speed (Nassi and Callaway, 2009). In contrast, coincidence detection is an efficient way to integrate information from different sources and form association between these, to eventually promote learning (Hebb, 1949; Bliss and Lomo, 1973; Heisenberg, 2003) or stimulus feature detection (Jeffress, 1948; Hassenstein and Reichardt, 1951). In the honeybee olfactory system either one of these mechanisms could potentially be realized by
projection neurons (PNs) that transfer information from the primary olfactory neuropile, the antennal lobe (AL) to the multimodal integration center, the mushroom body (MB).

Parallel processing is most prominently known from the vertebrate visual system (Livingstone and Hubel, 1988), where color and shape of a stimulus are analyzed in parallel with a possible motion of the stimulus. Similar distribution of stimulus features on different pathways has been described in the auditory (Rauschecker and Scott, 2009) and the somatosensory systems (Gasser and Erlanger, 1929; Reed et al., 2005). In insects, parallel pathways were described both in vision (Ribi and Scheel, 1981; Fischbach and Dittrich, 1989; Strausfeld et al., 2006; Paulk et al., 2008, 2009) and audition (Helversen and Helversen, 1995). More recently, advances have been made to investigate the role of parallel processing in vertebrate olfaction. These works indicate a division of olfactory bulb output into parallel channels of olfactory information mediated by mitral and tufted cells (Fukunaga et al., 2012; Igarashi et al., 2012; Payton et al., 2012). The two output tract responses differ in their phase to the respiratory oscillation cycle and in detail, tufted cell phase is unperturbed in response to purely excitatory odorants, whereas mitral cell phase is advanced in a graded, stimulus-dependent manner (Fukunaga et al., 2012). However, the existence of a similar spike timing mechanism in insects remains uncertain (Galizia and Rössler, 2010; Sandoz, 2011).

In favor of potential roles of parallel processing and spike timing, recent anatomical work in the honeybee (Kirschner et al., 2006) and other Hymenoptera (Rössler and Zube, 2011) has shown a dual tract system that pervades from the sensory input stage at the antenna to higher level processing in the MB (Figure 1). Olfactory receptor neurons (ORNs) provide mainly redundant input (Carcaud et al., 2012; Galizia et al., 2012) to the two prominent subsystems of the AL: the ventral and dorsal hemilobe (Kirschner et al., 2006). The ventral hemilobe comprises about 88 spheroidal neuropiles called glomeruli, and gives rise to the lateral antennal lobe tract (l-ALT, new tract nomenclature after Ito et al., 2014) containing about 510 PNs. The dorsal hemilobe consists of about 77 glomeruli which send out about 410 PNs via the medial ALT (m-ALT) (Abel et al., 2001; Kirschner et al., 2006; Rybak, 2012) PNs from the two separate tracts respond to a similar stimulus space. For instance there is no apparent specialization for either floral odors or pheromones. Nevertheless, PNs of l-and m-ALT differ in physiological properties, like response latency, odor specificity and response dynamics (Müller et al., 2002; Krofczik et al., 2008; Nawrot, 2012; Brill et al., 2013; Carcaud et al., 2015), implying different functions. Both the anatomical layout and the physiological distinction make l-ALT and m-ALT candidates well suited for parallel processing of different stimulus aspects.

Having said that, the dual olfactory pathway also displays mirror-imaged like trajectories which could likewise implement coincidence detection. The m-ALT first innervates the MB and finalizes in the lateral horn (LH), known for innate odor responses (Gupta and Stopfer, 2012; Roussel et al., 2014; Strutz et al., 2014). The l-ALT runs exactly opposite, projects first to the LH and ends in the MB. This counter-rotating neuronal architecture thus produces a substantial temporal delay between

the two tracts depending on which downstream neuron is activated in the MB. For instance, is a medial KC activated the l-ALT PNs will need a comparably longer time to reach that cell in contrast to the m-ALT. In comparison, is a more distal lateral KC activated the l-ALT will have reached the neuron earlier than the m-ALT (see Figure 4 in Rössler and Brill, 2013). This counter-rotating layout resembles detectors of coincidence for stimulus features such as delay lines known from the vertebrate auditory system, where sound localization is achieved by coincident input from neurons of both ears (Jeffress, 1948; Joris et al., 1998). A structural similarity that naturally has inspired speculations about a similar function in odor processing (Galizia and Rössler, 2010; Rössler and Brill, 2013).

Principle neurons of the MB, the Kenyon cells (KCs) receive highly convergent PN input (Caron et al., 2013; Gruntman and Turner, 2013). Moreover, PNs sent diverging output onto several KCs (Yasuyama et al., 2002; Leiss et al., 2009; Groh et al., 2012). Combined, both connectivity patterns lead to a temporally and spatially sparse KC population code (locust, e.g., Perez-Orive et al., 2002; honeybee, e.g., Szyszka et al., 2005; moth, e.g., Ito et al., 2008; fly, e.g., Turner et al., 2008). This code includes that individual KCs are activated only by highly coincident input from many PNs (Gruntman and Turner, 2013). Accordingly, synchronous activation of PNs has repeatedly been shown to be an important strategy for detection and learning of odors (e.g., Christensen et al., 2003; Martin et al., 2013; Riffell et al., 2013). Moreover, about 25% of PNs show odor specific latencies, which are shorter for l-ALT PNs than for m-ALT PNs (Krofczik et al., 2008; Brill et al., 2013). This latency code, in combination with the counter-rotating inputs, may lead to odor

<figure><img src="image_18.png" /><figcaption>**FIGURE 1** | Schematic overview of the dual olfactory pathway and its connectivity with upstream neurons into the mushroom body of the honeybee brain. Drawing of a bee's head with the brain exposed and arrangement of the dual olfactory pathway in both brain hemispheres (purple: m-ALT, green I-ALT). Magnification of one hemisphere illustrating the innervation pattern of the m-ALT (purple) and I-ALT (green). Uniglomerular PNs of the dorsal AL hemilobe first innervate the MB lip and basal ring (Br) and finalize their innervation in the lateral horn (LH), giving rise to the m-ALT. The I-ALT emerges from ventral AL glomeruli runs to the LH and later innervates the olfactory MB input sites. An, Antennal nerve; AL, Antennal Lobe; vL, vertical Lobe; mCa and ICa, medial and lateral Calyx; Co, collar.</figcaption></figure>
specific activation of different KC ensembles due to step-by-step coincidence between l-ALT and m-ALT PNs (Rössler and Brill, 2013).

While our knowledge about PN-tracts supports both, parallel processing and coincident delay lines, the two mechanisms put different constraints on the system. A prerequisite for an olfactory delay line would be that the information carried by the different tracts is combined. Accordingly, activity of individual neurons responding at the same time to the same stimulus should be highly coincident between the different tracts (see Figure 4, Rössler and Brill, 2013). And might also incorporate coincidental activity within tracts, which has been shown in other insects (Stopfer et al., 1997). Instead, parallel processing does not require the combination of inputs from different tracts, but might rather integrate information carried by neurons within the same tract.

To investigate how olfactory information is combined along the two tracts, we recorded odor responses of simultaneously active PNs and measured coincident activity within and between different PN subpopulations. Our results illustrate that coincidence is differently pronounced within each of the two tracts. Coincidence between tracts is present but does not outplay coincidence within the individual tracts. Taken together the findings strengthen the idea of parallel processing and the relevance of spike timing in coding of olfactory information in the olfactory system of the honeybee.

# Material and Methods

## Animal Preparation

Foragers of the European honeybee Apis mellifera carnaica (Pollmann) were caught from a feeder filled with saccharose solution (Apiinvert, 50%) and harnessed and movement restrained according to standard routines. The brain was exposed by opening the head capsule. Glands, trachea and neurolemma were removed carefully. A reference electrode (chloride Ag-wire, 150 µm, AGT05100 WPI, Germany) was placed between the ocelli, a second electrode monitored proboscis movements by recording muscle activity. Tetrodes to record from the olfactory tracts were inserted outside the AL where l- and m-tract run in separate loops to the MB. Following electrode placement the brain was either covered with two component low viscosity silicone (Kwik-Sil, WPI, USA) or left untreated (Brill et al., 2014).

## Odor Stimulation

Odor and control stimuli were delivered by a custom build olfactometer under constant stream of humidified and charcoal filtered clean air. Stimulation airstream was removed by an exhaust. The mean delay between stimulus expulsion from the olfactometer and the arrival at the animals antenna was 99 ms as estimated from Electro Antennogram (EAG, c.p below). Each animal was stimulated with the full set of 12 different odors in randomized order. The set comprised key elements of general plant odors (limonene, hexanal, 1-pentanol, 1-octanol, 2-octanone), natural plant odors (clove oil, orange oil, citral), and pheromones (geranylic acid, isoamylacetate, 1-hexanol, 2-heptanone) (Table 1). All stimuli were diluted 1:100 in mineral oil, applied in pulses of 500 ms and response measured repeatedly

in 20 trials each. Mineral oil and pure air were applied as control stimuli.

For further details of stimulus application and data acquisition refer to Brill et al. (2013).

## Electrophysiology
### Multi-unit Recordings

Electrodes consisted of three micro-wires made of copper (polyurethane-coated, 15 µm diameter, Elektrisola, Germany) and glued together with melted dental wax (Brill et al., 2014). One of these electrode shanks was placed to record from the l-, a second from the m-ALT, both of which were connected to a switchable headstage (SH16, Tucker-Davis-Technologies, USA). A silver-wire reference electrode was placed between the ocelli. Signals were fed into a custom designed connection module (INT-03M, NPI, Germany) and transferred to a custom-made amplifier system consisting of 16 custom designed low noise differential amplifier modules (DPA-2FL, NPI, Germany). To control for a potential influence of muscle activity on multi-unit recordings, the activity of the proboscis muscle, M17 was monitored. Recordings from all channels were 5 k diffentially amplified to the reference electrode, band-pass filtered (300–8000 Hz), and shank-wise differentiated, that is: potentials recorded from each micro-wire within one shank were pairwise subtracted from each other to eliminate interfering signals (e.g., muscle activity, electrical hum). Subsequently data was stored for offline analysis. Sampling rate was 31,250 Hz at 16 bit resolution on each channel.

## Electro-Antennogram (EAG) Recordings

EAGs were measured from the antenna ipsilateral to multi-unit recordings in five bees tested with the complete odor panel. Low-resistance (<0.5 M Ohm) borosilicate electrodes (1B100F-3,WPI,USA) were pulled with a horizontal filament puller (DMZ Universal Puller, Germany) and filled with 0.5 M KCL-solution. A tungsten electrode below the scapus of the same antenna served as reference. Signals were amplified first by an intracellular headstage (Gain 10, Model 1600, A-M-Systems, USA) and subsequently by the same custom build amplifier as the multi-unit recordings (Gain 100) and band-pass filtered (0.1–100 Hz). Prior to analysis recordings were smoothed offline with an algorithm provided by Spike2 (time constant 32 µs). Smoothed EAGs were averaged over repeated trials. Response onset was defined as the relative maximum preceding the steepest negative slope of the potential drop which demarcated an odor response (Meyer and Galizia, 2012).

## Spike Sorting

Spikes were sorted using established routines implemented in commercial software (Spike2, v7.4, Cambridge Electronic Design, England). Each channel was preprocessed by smoothing with a FIR-filter (time constant 80 µs) and DC removal (time constant 3.2 ms). Signals recorded from all three channels were used for spike- sorting, unless one of the channels had to be excluded due to insufficient signal-to-noise (SNR) ratio, in which case the remaining two channels were used. We performed semi-automated template-matched spike sorting with an amplitude
<table><caption>TABLE 1 | Odor stimuli used in the experiments.</caption><thead><tr><th>Odor</th><th>Abbreviation</th><th>CAS number</th><th>Odor type</th><th>Biological significance</th></tr></thead><tbody><tr><td>Spontaneous</td><td>spont</td><td></td><td></td><td></td></tr><tr><td>Control air</td><td>ctr</td><td></td><td></td><td></td></tr><tr><td>Citral</td><td>Cit</td><td>5392-40-5</td><td>terpen</td><td>floral and pheromone</td></tr><tr><td>Geranyl acid</td><td>Ger</td><td>459-80-3</td><td>terpen</td><td>floral and pheromone</td></tr><tr><td>Isoamylacetate</td><td>IAA</td><td>123-92-2</td><td>ester</td><td>pheromone</td></tr><tr><td>(+) Limonene</td><td>Lim</td><td>5989-27-5</td><td>terpen</td><td>floral</td></tr><tr><td>Clove oil</td><td>Clv</td><td>8000-34-8</td><td>natural blends</td><td>floral</td></tr><tr><td>Orange oil</td><td>Orng</td><td>8008-57-9</td><td>natural blends</td><td>floral</td></tr><tr><td>Hexanal</td><td>6-al</td><td>66-25-1</td><td>aldehyde</td><td>floral</td></tr><tr><td>1-Pentanol</td><td>1-5-ol</td><td>71-41-0</td><td>alcohol</td><td>floral</td></tr><tr><td>1-Hexanol</td><td>1-6-ol</td><td>111-27-3</td><td>alcohol</td><td>pheromone</td></tr><tr><td>1-Octanol</td><td>1-8-ol</td><td>111-87-5</td><td>alcohol</td><td>pheromone</td></tr><tr><td>2-Heptanone</td><td>2-7-ne</td><td>110-43-0</td><td>ketone</td><td>pheromone</td></tr><tr><td>2-Octanone</td><td>2-8-ne</td><td>111-13-7</td><td>ketone</td><td>floral</td></tr></tbody></table>

Odor abbreviations, the chemical abstract service number (CAS), chemical group of odorants and their biological significance for the honeybee are given. The used trials per odor are 20, number of recorded bees is 12, and the number of single PNs from the l-ALT is 54 and m-ALT 65, respectively.

threshold set to the mean spontaneous activity ±3 standard deviations. Spontaneous activity was recorded over at least 1 min of activity prior to odor-test trials. Templates were formatted in semi-automated fashion in time windows from -0.4 to 0.8 ms around each spike's peak. Units were clustered and sorted by applying the Spike2 built-in dialogs based on PCA and additional feature extraction. For more detailed description refer to previous publications based on the same dataset (Brill et al., 2013).

## Data Analyses

After spike sorting individual units were judged with respect to responsiveness and reliability (see paragraph “identification of odor-response profiles in PNs” in Materials and Methods in Brill et al., 2013). We wanted to know if coincident activity is a mechanism that is potentially used by honey bee PNs to combine the odor information that is carried in their spike trains. To isolate odor related activity, we excluded trials without odor-responses as well as those that were corrupted by artifacts (e.g., hum from the mains, muscular activity etc.) We analyzed coincidence within each animal between simultaneously active PN units using cross correlation. From each of the 12 animals we extracted eight units on average. Altogether 102 units were included in the analyses. Based on electrode placement these units could be identified as either l-ALT (49 units, on average 4 per animal, minimum 2, maximum 6) or m-ALT PNs (53 units, on average 4 per animal, minimum 3, maximum 6) such that coincidence within and between tracts could be identified. Analysis routines were custom written in Matlab (2010a; The MathWorks, Inc.).

## Detection of Simultaneous Odor Responses Across Units

Our objective was to analyze simultaneous odor evoked activity in small ensembles of PNs. For this purpose we compared the activity between units within one animal and selected pairs that were responsive to the same stimulus. In brief, we

detected for each individual unit which odors were effective in evoking responses. Subsequently we matched each unit's response spectrum to those of the other units in the same recording. This way we ended up with pairs of l-ALT, m-ALT (within tract) and l-m-ALT (between tracts) units that were simultaneously active.

In order to achieve the response detection for each unit, we employed a fully automated routine of five successive steps: (1) To detect responses from averaged trials we re-sampled to bins of 1 ms and averaged trials of repeated presentation of the same stimulus. (2) We estimated the rate function of this averaged trial by convolution with a symmetric smoothing filter (Savitzky and Golay, 1964, polynomial order 0, 301 ms width, Welch-windowed). (3) Baseline firing rate was estimated over an interval of 600 ms before stimulus onset. (4) A response was defined as a deviation from baseline ±2 standard deviations with duration of at least 50 ms in a time window from 0 to 600 ms post stimulus. Deviations above threshold correspond to excitations-deviations below baseline correspond to inhibitions. (5) If a response was indicated in the average trial, we repeated the procedure on the level of the underlying single trial spike trains. (6) If a response occurred in at least half of all single trials, we accepted the odor as a potent stimulus for the given unit. Setting the threshold for responsiveness to 25 or 75% did not significantly change the quantitative results (Brill et al., 2013). Trials without a response as well as inhibited responses (<1%) were excluded from further analysis.

Control stimuli are expected to evoke no (air) or only weak (mineral oil) responses. In order to monitor baseline coincident activity we included all control trials into the analysis irrespective of whether or not a response was detected.

## Cross Correlation

We detected coinciding spikes between different units by estimating the cross-correlation function for simultaneously recorded spike trains carrying odor information.
After selecting those trials in which a given pair of neurons was active simultaneously, we estimated cross-correlation using the observed elapsed times from one spike in the first unit's train to all spikes in the second unit's train in time window $v$. In repeating this procedure for every spike, we obtained for each unit pair the set of all possible differences between spike times for all simultaneous trains in the cross-correlation window $-v$ to $+v$.

Next we estimated the density function of this cross-correlogram using a Gaussian kernel with a fixed bandwidth of 25 ms ($\sigma$ = 5 ms). This procedure is equivalent to classical cross-correlation but avoids a-priori determination of fixed bin sizes with equal weight. The density function reflects the probability of simultaneous occurring events at a given time. It is normalized to the total number of events within the underlying data.

At our chosen bandwidth of 25 ms, 68% of all integrated events fall within the central 10 ms of the kernel. A timing that resemble the integration time at a possible post synapse of a KC receiving input from both of the correlated units (PNs) as was shown by modeling approaches in honeybee, locust, and moth (Perez-Orive et al., 2004; Cassenaer and Laurent, 2007; Finelli et al., 2008; Martin et al., 2013).

To account for stimulus induced and random coincidence of spiking events, we subtracted a shuffle predictor from the density function of the raw cross-correlogram. The shuffle predictor was obtained by the same routine as explained above but from non-simultaneous trains of the same neuron pair. Bootstrap resampling from this non-simultaneous cross-correlation yielded a 95% confidence interval. Coincident activity was accepted as significant when the density function of the raw cross-correlogram exceeded the upper bound of the 95% confidence interval of the shuffle predictor.

To quantify coincident activity within a pair of units, we calculated the Coincidence Index (CI). CI is the summed Area from periods of significant coincident activity [$t(D_{95\%})$] under the density function of the shuffle corrected cross correlogram ($D_{cross} - D_{shuffle}$).

$$CI = ((D_{cross} > D_{95\%}) - D_{shuffle})$$

CI reflects the significant coincident activity exceeding the expected coincidence.

## Statistical Testing

We hypothesized that coincident activity is differently distributed between tracts. To test this assumption we needed a non-parametric procedure suitable for samples of unequal size but dependent data. In using a bootstrap hypothesis test all these requirements were met. We proceeded as follows: Each time we tested two out of the three possible datasets (coincidence strength of l-tract, m-tract and lm-tract, respectively) against each other. From each set, we drew 500 bootstrap resamples, the same size as the smaller of the two underlying dataset. A bootstrap resample is defined as a random sample drawn with replacement from the empirical distribution. We calculated the population median for each of these resamples, which left us with two equal sized

samples. Given that these two samples distribute around equal medians, subtracting one sample from the other should yield a distribution around zero. A hypothesis (H0) that can easily be tested by calculating the probability of the observed median and comparing it to a predefined level of significance (alpha). We set our alpha to 0.05 but corrected for multiple testing using the Bonferroni procedure, yielding a final alpha of 0.016, if all three possible combinations (l-tract:m-tract; l-tract:lm-tract; m-tract:lm-tracts) were tested.

## Correlation Matrix

We wanted to test if strong odor responses go hand in hand with high coincident activity. For this purpose, we correlated the tuning to an odor with coincident activity. We extracted odor tuning as follows: We measured response magnitude of each of our 102 units to every of the 12 odors used for stimulation. Response strength was given by the peak rate of the evoked firing rate change. Next, we ranked response strength within each unit. We thus obtained for every odor 102 position ranks between 1 and 12. We extracted the matching coincidence activity as follows: For each unit we summed its strength of coincident activity with all other simultaneously recorded units that responded to a given odor. Like for the odor tuning, we ranked coincidence strength to each of the 12 stimuli within every unit. This left us with another vector of 102 position ranks for each odor. The relationship between these two population vectors describing odor response strength and coincident activity was assessed by correlation. High correlation is associated with similar ranks in both tuning vectors, low correlation with very different ranks.

## Results

When different neurons fire action potentials in close succession their activity is detected as coincident by a shared postsynaptic target. Coincident activity can be used by the neural system to combine information carried by individual neurons. We wanted to know whether this mechanism may be utilized by the medial and lateral AL tracts of the honeybees' dual olfactory pathway.

For this purpose we analyzed extracellularly recorded spike trains from a whole of 102 units (49 l-ALT, 53 m-ALT, 12 animals). Units from both tract of each animal were recorded simultaneously and stimulated repeatedly (20 trials each) with 12 different odors (Brill et al., 2013). Each unit responded simultaneously with at least one other unit of the same recording to at least one odor, resulting in a total of 397 combinations. Simultaneous odor responses occurred within one tract (85 unit pairs in l-ALT, 96 unit pairs in m-ALT) and between the two tracts (216 unit pairs l-ALT:m-ALT). Whenever a unit pair responded simultaneously to a set of stimuli it also displayed coincidental activity to at least one of these stimuli (100% congruence in l:l, 99.5% in l:m, 96% in m:m), however, not necessarily to every single of the effective stimuli. On average a given unit pair displayed coincident activity for 84% of the odors that were effective in driving simultaneous responses.
In order to remove spurious coincidence, we corrected for stimulus modulation of firing rates, by subtracting a shuffle predictor from the original cross correlogram (see methods). Further, we only considered coincident activity that exceeded a 95% confidence interval.

## Coincidence Increases with Odor Stimulation

A prerequisite for every mechanism potentially encoding environmental information is that it should be more pronounced in the presence of a stimulus than in its absence. We compared recordings of spontaneous activity (Figure 2A) with odor stimulation trials (Figure 2B) to test whether this applies to coincidental activity of PNs within and between tracts. For this purpose we calculated a Coincidence Index (CI). CI reflects the significant coincident activity exceeding the expected coincidence.

Coincidence was present in both cases, but significantly higher under conditions of odor stimulation, than under spontaneous activity (Figure 2C; Wilcoxon signed rank test, *p* < 0.001). The amount of units expressing coincidental activity varied between recordings (animals). While coincidence was generally high in some ensembles (Figures 2A,B bottom row) it was rather low in others (Figures 2A,B middle row). Likewise, coincidence was not equally distributed between units of the same recording. While some units did not coincide with any other unit, others fired in

close succession with many of the simultaneously recorded units, giving the impression of a “coincidence hub”. As a general rule, units with high spontaneous coincidence showed even stronger odor related coincidence. Considering our careful correction for spurious coincidence, this odor related effect cannot be attributed to the pure increase in firing rate that naturally follows excitatory responses.

## m-ALT Units Show More Coincidence Activity than I-ALT Units

PNs from the l- and m-ALT differ both in morphology and their functional properties. In how far do they produce different degrees of coincidental activity?

As can be seen from visual inspection of ensemble plots alone, m-ALT units (Figure 2B right column) are more likely to produce coinciding spikes than l-ALT units (Figure 2B left column). Coincidence between tracts seems to appear more often than within the l-ALT but less pronounced as compared to activity within the m-ALT. This qualitative observation is confirmed by a quantitative bootstrap hypothesis test (Bonferroni correction, *p* < 0.002, Figure 2D): With a median CI of 44%, pairs of m-ALT units were significantly more prone to engage in synchronous firing than pairs of l-ALT units (CI = 32%) or mixed pairs of units from both tracts (CI = 36%). Strongest coincidence of unit-odor pairs occurred at relative times of 11 ms

A
spontaneous coincidence

<figure><img src="image_19.png" /></figure>

B
odor related coincidence

<figure><img src="image_20.png" /></figure>

C
spontaneous odor related

<figure><img src="image_21.png" /></figure>

D
CI [%]
CI [%]
I-ALT
Im-ALT
m-ALT

<figure><img src="image_22.png" /><figcaption>**FIGURE 2** | Coincidental activity of single PNs within and between tracts. (A) Significant coincident activity during recordings of spontaneous activity within I-ALT PNs (green, left row), m-ALT PNs (purple, right row) and between PNs of both tracts (middle row) from simultaneously multi-unit recordings in three honeybees as example. Lines indicate coincidence strength across PN pairs estimated by the coincidence index CI. (B) Significant coincident activity during odor stimulation trials. Colors and</figcaption></figure>

Indices as in (A). (C) Quantitative measurement of coincident activity across all 392 combinations of recorded PN pairs, indicates significant increase of coincidence during odor stimulation ("Wilcoxon signed rank test, *p* < 0.001). (D) Coincident activity is highest within the m-ALT, followed by significant coincidence across PNs from both tracts. This qualitative observation is confirmed by a quantitative bootstrap hypothesis test ("Bonferroni correction, *p* < 0.002).
in the l-ALT, 10 ms in m-ALT and 9 ms between unit pairs of both tracts. A delay that is within the integration time of a postsynaptic KC as estimated by modeling approaches (Perez-Orive et al., 2004; Cassenaer and Laurent, 2007; Finelli et al., 2008; Martin et al., 2013). The unintended variability of electrode placement in the range of about 100 µm at the output of the AL is of minor relevance since a presumed neuronal conduction velocity of about 20 cm/s (Oleskevich et al., 1997) would add a temporal variance of less than 1 ms.

## Unit-pairs with Similar Odor-tuning do not Synchronize Stronger than Controls

Neural codes typically involve the identity of individual neurons. Extracellular measurements sample randomly from groups of neurons with various identities, i.e., different odor specific

characteristics. To access the possibility of an odor-specific code of coincidence that depends on unit identity, we investigated coincident activity of unit pairs with similar tuning properties. To assess similarity we ranked odor responses within each unit according to strength. We compared these tuning profiles of ranked odor responses by correlation. Positive correlation was indicative for similar tuning. Non-significant correlation around zero was indicative for dissimilar tuning. Altogether 50 unit pairs (l:l 8 pairs, m:m 20 pairs, l:m 22 pairs) showed significantly positive correlated tuning (Figures 3A,B). Compared to unit pairs with non-correlated tuning (344 pairs, random examples Figures 3C,D) similarly tuned units did not differ significantly in coincidence strength (bootstrap hypothesis test, *p* < 0.05). However, there was a tendency for similar tuned units being rather less well synchronized than others.

<figure><img src="image_23.png" /><figcaption>**FIGURE 3** | Relationship between tuning and coincidence of exemplary unit pairs. Odor tuning of individual units and coincidence of unit pairs within and between tracts. Barplots show tuning in percent response rate of the individual unit. Gray shaded areas indicate the strength</figcaption></figure>

of coincidence in percent of overall coincidence of a given pair. Rows (A,B) give examples of units with similar tuning (significantly positive correlated). Rows (C,D) show examples of unit pairs with dissimilar tuning (no correlation). Green: I-ALT; purple: m-ALT.
Qualitative assessment of this relation shows that this trend was particularly visible for unit-pairs within the l-ALT. Odors that evoked low response rates could produce strong coincidence (Figures 3A,C: left column, row A, orange Oil or row C, clove oil). Pairs, in which both l-ALT units showed a strong tuning to one particular odor, usually did not synchronize to that same odor (Figures 3A,C: left column, row A, hexanal or row C, hexanol). On the contrary, for unit pairs within the m-ALT at least one unit, less often both, showed prominent tuning to an odor if the pair produced notable coincidence (cp.: Figures 3A,B: right column, row A, hexanal, row B, octanol). For mixed pairs between the l-ALT and the m-ALT an odor that evoked strong rate responses in both units likewise exhibited strong coincidence (cp.: Figures 3A,C: middle column, row A, octanol, row C, octanol). Octanol and hexanal appeared to be particularly potent in driving both response rates and coincidence of m-ALT units.

## Odor Identity is not Reflected in a Simple Code of Coincidence Strength

Identification of biologically relevant odors is a key function of the olfactory system in behaving animals. Recent approaches have repeatedly described temporal relationships between neurons to be involved in this task (Stopfer et al., 1997; Perez-Drive et al., 2002; Riffell et al., 2013). We observed particularly strong coincidence amongst m-ALT units evoked by octanol and hexanal. Accordingly, we were curious whether odor identity was

reflected by coincidental activity between units of the same or different tracts.

In a first step, we broke down the overall coincidence to the individual odor stimuli. For this purpose, we plotted the median *CI* distribution for each odor in the stimulus set, within and between tracts (Figure 4). Under the assumption that odor identity could be coded simply by the magnitude of coincidence, one would expect to see a systematic variation across animals in this distribution. Such a simple relationship however was not apparent. The median *CI* overlapped broadly between 0 and 80% for unit pairs within both tracts (Figures 4A,C). Between tracts, coincident activity was less dispersed but likewise overlapping (Figure 4B). None of the odors evoked a systematically high or low coincident activity. To the contrary, an odor that produced high *CI* scores in one recording could show low scores for another recording.

We conclude that a relationship between odor identity and coincident activity within an ensemble of units is not captured by a simple but inflexible code of coincidence strength.

## Odor Tuning Correlates with Coincidence Strength in m- but not in l-ALT Units

We suspected a more flexible and thus more useful way of odor coding might get apparent when properties of individual units were taken into account. Based on the observations we made on single unit pairs within the m-ALT and between l- and m-ALT, we hypothesized that highly coincident activity

<figure><img src="image_24.png" /><figcaption>**FIGURE 4** | Odor-specificity of coincident activity of PN pairs within and between tracts. (A) Odor-wise analysis of coincidental activity of l-ALT PN pairs. (B) Odor-wise analysis of coincidental activity of mixed l-ALT to m-ALT PN pairs. (C) Odor-wise analysis of coincidental activity of m-ALT PN pairs. Note: since the m-ALT was shown to respond to odors sparsely with high odor specificity (lifetime</figcaption></figure>

sparseness), in 2 out of 12 odors not more than one PN fired in coincidence with l-ALT PNs, whereas in 2 out of 12 odors no m-ALT PN fired in significant coincidence with neither l- or m-ALT PNs. Box-plots of all analyzed PN pairs in response to the given odors with box-line as median, Box: first and third quartile, whiskers: first and ninth centile. Circles indicate the bee-wise median.
would be more likely to appear for an odor that a given unit was better tuned to. To investigate the possibility of such a relationship we determined the response strength for every odor in each individual unit together with the strength of the corresponding coincidence. Next we correlated odor tuning with its corresponding coincident activity (Figure 5). The resulting correlation matrixes illustrate a marked difference between tracts: while the relationship appeared negligible within the l-ALT (Figure 5A), a strong pattern of significant correlation was apparent within the m-ALT (Figure 5B).

The emerging correlation matrix of the m-ALT met our expectations, since a positive correlation between odor tuning (e.g., to citral) and coincidence for the same odor (citral) was clearly visible. In practice: a unit that was strongly tuned to a given odor likewise showed strong coincidence, another unit with low tuning to the same odor would instead produce little coincidence. Surprisingly, positive correlation was not exclusively describing the relationship between tuning and coincidence to the same odor, but occurred similarly, even though generally less pronounced, to different odors belonging to similar chemical groups; e.g., citral and limonene (terpene); clove oil and orange oil (natural blends); pentanol and hexanol (alcohol). In contrast, negative correlations dominated the relationship between tuning and coincidence to odors from more distinct chemical classes; e.g., citral (terpene) and hexanol (alcohol). In fact, the characteristic pattern of positive and negative correlations might help the receiving mushroom-body circuits to discriminate and by this identify odors.

The marked lack of a similar relationship in the l-ALT is in congruence with both its previously documented rather stimulus unspecific responses (Brill et al., 2013) and its less pronounced coincident activity (c.f.: above). Our findings thus strengthen the notion of l-ALT and m-ALT being responsible for processing different stimulus properties and imply the utilization of different mechanisms for this purpose.

Discussion

In the present work, we set out to investigate in what fashion olfactory information is combined along the separate tracts of the honeybee dual olfactory pathway. Does coincident activity between the tracts foster a detection of stimulus features comparable to the delay line system of the vertebrate auditory system? That is: do l-ALT and m-ALT PN show prominent coincident activity? Or is coincidence a potential mechanism to integrate information within the same tract, facilitating parallel processing of stimulus properties comparable to the prominently known parallel visual pathways? That is: do neurons within the same tract show prominent coincident activity? To answer these questions, we recorded odor responses of simultaneously active PNs and measured coincident activity within and between the different subpopulations. Our results illustrate that coincidence is differently pronounced within each of the two tracts. Coincidence between tracts is present but does not outplay coincidence within the individual tracts. Taken together the findings presented in this work support the notion of

<figure><img src="image_25.png" /></figure>
coincidence as an important mechanism in olfactory processing and, at the same time, strengthen the idea of parallel processing and delay-line coding in the dual olfactory system of the honeybee.

Synchrony within the AL has been shown to correlate with odor identity and intensity (Christensen et al., 2000; Lei et al., 2004; Riffell et al., 2009a,b) and has been suggested to represent a common encoding dimension for food odors and pheromones (Martin and Hildebrand, 2010). In agreement with these works, we find that PNs produce significant amounts of coincident spikes. More importantly however, this activity is specifically related to the presence of an odor stimulus. This relationship does not seem to be realized by the magnitude of coincidence alone. Accordingly, we could not find indications for a systematic relationship between coincidence strength and odor identity *per se*. Coincidence is the product of coordinated activity between at least two neurons. As such it represents the smallest unit of a processing network. Information processing in neuronal networks is believed to underlie higher order computations rather than an easy mathematical relationship (Laurent, 2002; Friedrich, 2013). Within the framework of network, our results and those of related works (Riffell et al., 2009a; Martin et al., 2013) suggest coincident activity to be a highly flexible mechanism that crucially depends on factors like the individual neurons' odor tuning and is as such suited to integrate biologically relevant information in upstream neurons.

In the same line of thinking, many studies have stressed the importance of coincidence detection by mushroom body KCs in the context of odor learning (Riemensperger et al., 2005; Cassenaer and Laurent, 2007; Gervasi et al., 2010) and odor discrimination (Perez-Orive et al., 2004; Jortner et al., 2007; Riffell et al., 2009a,b; Martin et al., 2013). Based on studies like these, the MB has been assumed as a coincidence detector for synchronous activity provided by the AL (Heisenberg, 2003; Rybak and Menzel, 2010; Davis, 2011). So far however, it has been difficult to disentangle what contribution is made by which type of neuron. Closing this knowledge gap is an important step in order to understand the function of different AL-neuron subpopulations (Galizia and Rössler, 2010). Moreover, it will help to develop refined models describing how upstream neurons in the MB make use of the information provided by the AL.

Using simultaneous dual-tract recordings we show for the first time coincidental activity that can directly be attributed to different morphological subclasses of AL PNs, which give rise to the lateral and medial tract projecting from the AL to the MB. The amount to which coincident activity is provided differs significantly within and between tracts. A finding that inspires to speculate about underlying mechanisms.

How is the striking difference in coincidence within l-ALT and m-ALT to be explained? What makes m-ALT PNs more likely to unite in synchronous firing than l-ALT PNs? And what impact might these differences have on upstream neurons? On the one hand, quantitative analysis of odor-evoked spike trains have attested higher overall firing rates in l-ALT units (Brill et al., 2013). On the other hand, qualitative observations of spiking patterns from both tracts have repeatedly led to descriptions of irregular and burst-like, phasic activity in m-ALT

PNs contrasted by tonic activity in l-ALT PNs (Abel et al., 2001; Müller et al., 2002). When coding for comparable signals, bursts, in comparison with single spikes, have been shown to improve the SNR ratio (Sherman, 2001) and are suggested to improve information transfer between neurons (Lisman, 1997). Accordingly, the tendency of m-ALT PNs to display more burst-like activity might in fact outplay higher firing frequencies of l-ALT PNs when it comes to producing coincidence.

However, from the generally lower expression of synchronous firing in l-ALT PNs, it does not necessarily follow that coincidence within this tract is negligible. Even tough to a lesser degree than within the m-ALT, l-ALT units do produce significant amounts of coincident firing which upstream KCs could make use of. Input from PNs of the dual tracts might be processed differently: pyramidal neurons of the weakly electric fish have been shown to extract different aspects of stimulus information from coinciding burst-like and coinciding tonic spike trains (Oswald et al., 2004). If similar mechanisms exist in insect KCs has not yet been investigated.

Moreover, we should consider that KCs—just like vertebrate pyramidal neurons—might possess more than one type of coincidence detection (for reviews of coincidence detection in pyramidal neurons see Spruston, 2008). In our analysis of simultaneously recorded extracellular unit activity, we mimicked temporal summation by means of density estimation with a kernel about the length of possible postsynaptic integration. Our experimental approach did not allow to likewise consider coincidence detection as a result of spatial summation. Spatial summation crucially depends on large numbers of synaptic contacts. As a matter of fact, mature l-ALT PNs make more contacts with KCs than m-ALT PNs (Groh et al., 2012). Based on these morphological findings l-ALT PNs might thus be better suited to provide spatially coincident input, while m-ALT PNs give more temporally coinciding input, as indicated by our results.

In summary, the apparent differences of coincident activity as detected by our analysis illustrate that different mechanisms govern odor processing in each of the two tracts establishing the dual pathway. However, the final interpretation of these differences remains a matter of upstream KCs. In order to understand the interplay between PN output and KC response simultaneous recordings from all three types of neurons would be highly desirable.

Magnitude is not the only aspect in which coincident activity differs between l-ALT and m-ALT. We found a strong relationship of odor tuning vs. coincidence activity within the m-ALT, but not within the l-ALT. Based on these observations it is tempting to conclude that coincident activity of m-ALT PNs allows upstream KCs to specifically process odor identity; an assumption that is further supported by studies of stimulus specificity within the two tracts. Multi-unit recordings as well as calcium imaging from m-ALT PNs show significantly higher odor specificity than those of units from the l-ALT (Brill et al., 2013; Carcaud et al., 2015). A finding that could not be seen in a previous attempt using calcium imaging (Yamagata et al., 2009), most likely as a result of GABAergic mechanisms that impact PN activity in imaging approaches (Grünewald, 1999; Ganeshina
and Menzel, 2001; Froese et al., 2014). Interestingly, recent imaging results from m-ALT glomeruli do show coding related to chemical groups of odors (Carcaud et al., 2012). A finding, that complements nicely with our tuning-coincidence correlation, where we likewise found similar relationships between odors of the same chemical group. In agreement with the higher odor specificity, m-ALT PNs seem to keep track of the single odorants if challenged by odor mixtures (Krofczik et al., 2008)—a strategy termed elementary odor coding. Joint activity of odor specific m-ALT PNs could allow for a combinatorial code of mixture embedded odor identity by the receiving KCs.

The picture that emerges from our results for l-ALT PNs is very different, particularly regarding odor identity coding. The striking lack of correlation between odor tuning and coincidence implies that joint activity of l-ALT PNs conveys poor information about odor identity. This however appears little surprising considering that l-ALT PNs are rather broadly tuned and express little odor specificity (Brill et al., 2013; Carcaud et al., 2015). In contrast to the m-ALT, l-ALT PNs are characterized by shorter latencies (Krofczik et al., 2008; Brill et al., 2013) and start to respond to odors already at very low concentrations (Yamagata et al., 2009; Schmuker et al., 2011; Carcaud et al., 2012). If challenged by odor mixtures they tend to respond to the mixture as a whole, rather than the single odorant (Krofczik et al., 2008). It might well be that any of these characteristics could produce significant correlation with coincident activity for l-ALT PNs but not m-ALT neurons - an assumption that due to the lack of suitable experimental data has to remain speculative for the time being.

Taken together, our results imply that coincidence within the tracts of the dual olfactory pathway serves different functions. These functions probably rely on the characteristics of the PN subgroups that allow for dedicated processing of different stimulus aspects. These findings support the suggestion that the dual olfactory pathway is ideally suited to implement parallel processing.

Parallel processing keeps information from different sources separated. An olfactory delay line, in contrast, would combine information from different sources. As detailed above, the coincident activity we found within each tract gives strong support to the implementation of parallel processing by the dual olfactory pathway. However, we also found significant coincident activity across tracts. Even though joint activity between l-ALT and m-ALT PNs did not outplay activity within individual tracts, it produced significant amounts of coincidence which might just as likely be used by upstream KCs. Hence our finding complies likewise with the existence of olfactory delay lines. Could both of these mechanisms coexist? In fact, morphological evidence supports a possible implementation of both mechanisms in parallel (Figure 6). Mass-fill studies in different Hymenoptera have shown that PNs project to different sub-regions of the MB (Kirschner et al., 2006; Nishikawa et al., 2012). These separated inputs are received by various types of KCs. Some KCs make synaptic contacts only in one of the two PN input regions and likewise provide output to different regions (Strausfeld et al., 2000). That is, these types of KC maintain the possible separation of parallel pathways until its convergent input to extrinsic MB

neurons (Rybak and Menzel, 1993). Another population of KCs, the so called clawed KCs (KC II; Mobbs, 1982), span their postsynapses across the innervation fields of both l- and m-ALT PNs (Strausfeld, 2002). Patch clamp recordings in the fly could show that these clawed KCs, on average, require coinciding input from about 4-6 PNs in order to be driven above threshold (Gruntman and Turner, 2013). Patch clamp experiments in cockroaches likewise support coincident activation of KCs, as indicated by their high action potential threshold (Demmer and Kloppenburg, 2009). Similarly, indications emanate from studies showing that input of PNs conveying information about different odors in changing temporal relationships evoke activity in KCs specifically tuned to certain asynchronous inputs (Saha et al., 2013). An observation that recently was also found in the vertebrate's olfactory cortex (Haddad et al., 2013). This subtype of KCs is hence predestinated to function as a coincidence detector for information coming from both tracts (Rössler and Brill, 2013).

In this perspective, subclasses of olfactory PNs of the honeybee first of all establish parallel pathways. Subclasses of KCs again could realize an implementation of both maintained parallel processing and delay-line like coincidence detection. Although our experimental paradigm favors the idea of parallel processing, further experiments which take KC activity directly into account need to prove, if delay line coding in the olfactory system does exist. Along this line further experiments also should test which of the mentioned coding strategies, either parallel processing or coincidence coding, benefit the animal in detecting complex odors.

As proposed earlier (Rössler and Brill, 2013) the dual olfactory pathway reminds of a delay-line system. Taking the proposed

<figure><img src="image_26.png" /></figure>
neuronal conduction velocity of about 20 cm/s in honeybees into account (Oleskevich et al., 1997), we assumed that indeed different delays between the PN tracts activate different KCs within the MB calyx at different places. In favor of a delay-line coding the measured maximal coincidence of about 10 ms within and 9 ms across tracts could add up on already measured latency differences between tracts and between individual PNs (Krofczik et al., 2008; Brill et al., 2013). The measured maximal coincidence as well as response latency would thus enable the system to implement an even more fine-scaled temporal and spatial KC activation pattern, a prerequisite for sparse coding.

While parallel processing is most probable important for tasks like odor identification and learning, an olfactory delay line and temporal coding could help e.g., to navigate along a concentration gradient to a food source or a mate. These abilities are obviously not only vitally important for honeybees and other Hymenoptera but likewise for behaviorally less complex insects like flies or moths. In recent years several attempts have been

made to understand the possible functional relevance of the dual olfactory pathway of Hymenoptera (Abel et al., 2001; Müller et al., 2002; Krofczik et al., 2008; Yamagata et al., 2009; Brandstaetter and Kleineidam, 2011; Dacks and Nighorn, 2011; Rössler and Zube, 2011; Nishikawa et al., 2012; Brill et al., 2013; Carcaud et al., 2015). In the long run the knowledge gained from these studies might be transferred to insects with different tract layouts (Galizia and Rössler, 2010; Martin et al., 2011) and thus promote a more fundamental understanding of olfactory guided behavior.

## Acknowledgments

The authors would like to thank Martin P. Nawrot for valuable discussions. This work was supported by the Deutsche Forschungsgemeinschaft (DFG, SPP 1392, Ro1177/5-2) to WR. This publication was funded by the German Research Foundation (DFG) and the University of Würzburg in the funding program Open Access Publishing.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 Brill, Meyer and Rössler. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
# Intrinsic and Network Mechanisms Constrain Neural Synchrony in the Moth Antennal Lobe

Hong Lei¹, Yanxue Yu², Shuifang Zhu² and Aaditya V. Rangan³*

¹Department of Neuroscience, The University of Arizona, Tucson, AZ, USA, ²Institute of Plant Quarantine, Chinese Academy of Inspection and Quarantine, Beijing, China, ³Department of Mathematics, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA

Projection-neurons (PNs) within the antennal lobe (AL) of the hawkmoth respond vigorously to odor stimulation, with each vigorous response followed by a ~1 s period of suppression—dubbed the “afterhyperpolarization-phase,” or AHP-phase. Prior evidence indicates that this AHP-phase is important for the processing of odors, but the mechanisms underlying this phase and its function remain unknown. We investigate this issue. Beginning with several physiological experiments, we find that pharmacological manipulation of the AL yields surprising results. Specifically, (a) the application of picrotoxin (PTX) lengthens the AHP-phase and reduces PN activity, whereas (b) the application of Bicuculline-methiodide (BIC) reduces the AHP-phase and increases PN activity. These results are curious, as both PTX and BIC are inhibitory-receptor antagonists. To resolve this conundrum, we speculate that perhaps (a) PTX reduces PN activity through a disinhibitory circuit involving a heterogeneous population of local-neurons, and (b) BIC acts to hamper certain intrinsic currents within the PNs that contribute to the AHP-phase. To probe these hypotheses further we build a computational model of the AL and benchmark our model against our experimental observations. We find that, for parameters which satisfy these benchmarks, our model exhibits a particular kind of synchronous activity: namely, “multiple-firing-events” (MFEs). These MFEs are causally-linked sequences of spikes which emerge stochastically, and turn out to have important dynamical consequences for all the experimentally observed phenomena we used as benchmarks. Taking a step back, we extract a few predictions from our computational model pertaining to the real AL: Some predictions deal with the MFEs we expect to see in the real AL, whereas other predictions involve the runaway synchronization that we expect when BIC-application hampers the AHP-phase. By examining the literature we see support for the former, and we perform some additional experiments to confirm the latter. The confirmation of these predictions validates, at least partially, our initial speculation above. We conclude that the AL is poised in a state of high-gain; ready to respond vigorously to even faint stimuli. After each response the AHP-phase functions to prevent runaway synchronization and to “reset” the AL for another odor-specific response.

**Keywords:** antennal lobe, afterhyperpolarization (AHP), projection neuron, local neuron, disinhibition, computational model, synchrony, multiple-firing-event (MFE)

OPEN ACCESS

**Edited by:**
Sylvia Anton,
Institut National de la Recherche
Agronomique, France

**Reviewed by:**
Thomas Nowotny,
University of Sussex, UK
Matthieu Dacher,
Université Pierre et Marie Curie,
France

*Correspondence:
Aaditya V. Rangan
adirangan@gmail.com

**Specialty section:**
This article was submitted to
Invertebrate Physiology,
a section of the journal
Frontiers in Physiology

**Received:** 03 October 2015
**Accepted:** 18 February 2016
**Published:** 08 March 2016

**Citation:**
Lei H, Yu Y, Zhu S and Rangan AV
(2016) Intrinsic and Network
Mechanisms Constrain Neural
Synchrony in the Moth Antennal Lobe.
Front. Physiol. 7:80.
doi: 10.3389/fphys.2016.00080
# INTRODUCTION

It has long been understood that recurrent connectivity as well as intrinsic cellular properties both play a role in the dynamics of the insect Antennal Lobe (AL) (Hansson and Anton, 2000; Vosshall et al., 2000; Assisi et al., 2007; Galizia and Rössler, 2010). Nevertheless, it is still unclear how these features interact, and to what extent they influence the functional properties of the AL. In this paper we investigate this question within the hawkmoth (Manduca sexta) AL.

The *Manduca* AL itself houses many interneurons, including both Local Neurons (LNs) as well as Projection Neurons (PNs) which send information further downstream (Hornberg et al., 1989; Lei et al., 2010). These neurons are organized into functional and morphological modules—a.k.a. glomeruli—which are each stimulated by different classes of odorants. In this paper we largely concentrate on two such glomeruli—named the “cumulus” and “toroid” in male moth—which form the so-called Macroglomerular Complex (MGC) (Matsumoto and Hildebrand, 1981; Christensen and Hildebrand, 1987). This MGC serves as the first central stage of detection and processing of conspecific female sex-pheromones, and plays a crucial role in many of the *Manduca's* mating behaviors (Schneiderman et al., 1986; Hansson et al., 1991).

Our previous work, along with the work of others, has shown that PNs and LNs within the *Manduca's* MGC respond—with a vigorous depolarization—to brief puffs of odor containing the appropriate chemical components found in the animal's sex-pheromones (Warren and Kloppenburg, 2014; Kim et al., 2015; Lavialle-Defaix et al., 2015). Intriguingly, the response of the PNs also drops precipitously after each brief odor pulse—a phenomenon we refer to as the “After HyperPolarization” (AHP) phase of each response (Lei et al., 2002; Reisenman et al., 2005). Our previous work has shown that this AHP-phase is somehow implicated in odor-processing: pharmacological manipulation which interferes with the AHP-phase also prohibits *Manduca* from reliably detecting and responding to pheromone pulses (see e.g., Lei et al., 2009). Moreover, similar AHP-like phases have been widely reported as important for the sensory systems of many other animals (Wilson and Goldberg, 2006; Saito et al., 2008). Thus, rather than being a mere curiosity, the AHP-phase seems to be a rather general dynamical feature which plays a necessary functional role in sensory processing.

Our goal in this paper is to probe the dynamical mechanisms responsible for the AHP-phase and its associated currents within the *Manduca* AL. As mentioned above, we expect these mechanisms to include both intrinsic cellular properties (see e.g., Pedarzani et al., 2005), as well as recurrent connectivity (see e.g., the role played by GABA-B receptors discussed in Otmakhova and Lisman, 2004; Wilson and Laurent, 2005). Some intrinsic and recurrent mechanisms have also been studied in the modeling work done by Belmabrouk et al. (2011a,b). By clarifying how these mechanisms either compete or assist one another, we hope to reveal some of the computational principles at work in the olfactory system.

The main conclusions of this paper are that the dynamics of the hawkmoth antennal-lobe are consistent with: (a) strong

heterogeneous inter- and intra-glomerular synaptic connectivity, and (b) slow inhibitory intrinsic currents acting on the PNs. Feature (a) grants the AL a kind of automatic-gain-control—i.e., allowing the AL to respond very sensitively to faint odor puffs with the robust activation of multiple PNs—involving a kind of synchrony we refer to as “multiple-firing-events.” Feature (b) protects such a sensitive AL from “runaway synchronization,” allowing the AL to respond effectively to sequences of odor-stimuli separated by a few hundred milliseconds.

The Results section of our paper is organized as follows. First, in Section R1 we describe some of our experimental results. These experiments involve the application of various pharmacological agents to the AL, and motivate our computational model, which we discuss in Section R2. We use our computational model to try and understand the kinds of dynamics which underlie the phenomena we observe in experiment. This computational model then informs several predictions (Section R3), some of which we test in Section R4. Finally, we close with a discussion; touching on possible consequences of our investigation, as well as related work.

# MATERIALS AND METHODS

In this section we give an overview of our experimental and computational methods. This section is reinforced by material in the online Supplementary Information.

## Insect Preparation

*Manduca sexta* (L.) (Lepidoptera: Sphingidae) were reared in the laboratory on artificial diet under a long-day photoperiod, and adult male moths, 4 days post-emergence, were prepared for experiments as described previously (Hansson et al., 1991). For electrophysiological recordings, the moth was restrained in a plastic tube with its head fully exposed. The labial palps, proboscis and cibarial musculature were then removed to allow access to the brain. To eliminate movement, the head was isolated and pinned to a wax-coated glass Petri dish with the ALs facing upward. Tracheae and a small part of the sheath overlying one AL were then removed with fine forceps. The preparation was continuously superfused with physiological saline solution containing 150 mM NaCl, 3 mM CaCl2, 3 mM KCl, 10 mM TES buffer (pH 6.9), and 25 mM sucrose.

## Electrophysiological Recording

To allow long-term recording from single neurons, which is needed for the pharmacological experiments in this study, we used a juxtacellular recording technique modified from Pinault (1996) and tested in Lei et al. (2009). In short, electrodes resembling those used for patch recording were pulled from thin-wall borosilicate glass capillaries using Sutter P-2000 laser puller and filled with physiological saline, resulting in <10 mΩ electrode resistance. An Axoprobe-1A amplifier connected to a 10x DC amplifier (Model FC-23B, WPI, Sarasota, FL) was used to amplify the signal up to 1000x. Calibration pulses from the Axoprobe-1A amplifier were added to the output channels. A Leica micromanipulator was used to advance the electrode into the MGC region of an AL until a contact similar to that used for
perforated-patch recording was achieved. A key technique in this configuration is to bring the electrode tip close to a neurite, nearly touching but not impaling it. We found that the amplitude of the recording was affected by the closeness of the electrode tip with the neurite. During the course of an entire experiment the relative position between the juxtacellular-electrode and neurite may drift, causing visible changes in the amplitude of the recorded spikes, but not their frequency or timing.

## Sensory Stimulation and Characterization of Neurons

Olfactory stimuli were delivered to the preparation by injecting odor-laden air puffs onto a constant air flow that was controlled at 1 liter per minute. The flow was directed at the middle of the antenna ipsilateral to the AL from which recordings were made. Trains of 5 air puffs (50 ms) with 2 s inter-pulse intervals were generated by means of a solenoid-activated valve controlled by an electronic stimulator (WPI, Sarasota, FL). Shorter or longer intervals were used in particular experiments to test the effect of intervals on response consistency (Figure 2). These air puffs were directed through a glass syringe containing a piece of filter paper, bearing various amounts of a single pheromone component (0.1–100 ng in decadal steps). Not every concentration was used in all experiments. The stimulus compounds used were: (i) E10, Z12-hexadecadiennal (EZ, the primary component of the conspecific female's sex pheromone); (ii) E11, E12, Z14-hexadecatriennal (EEZ, a second essential component of the sex pheromone). MGC-PNs were characterized using 3 physiological criteria: (1) randomly bursting spontaneous firing pattern; (2) response specificity to pheromone components; and (3) multiphasic pattern of responses. In *M. sexta*, uniglomerular MGC-PNs have been shown repeatedly to give predictable responses to the pheromone components according to the MGC glomerulus in which their dendrites arborize (Christensen and Hildebrand, 1987; Heinbockel et al., 1999, 2004; Lei et al., 2002): Cumulus PNs are excited by antennal stimulation with EEZ but inhibited (or not affected) by stimulation with EZ, whereas the Toroid PNs are excited by stimulation with EZ but inhibited (or not affected) by stimulation with EEZ. These types of PNs typically exhibit a biphasic response pattern in juxtacellular recordings, i.e., a depolarization phase followed by a period of afterhyperpolarization (Lei et al., 2009). Finally, the spontaneous activity of MGC-PNs typically is more randomly bursting, while that of LNs is more tonic (Lei et al., 2011).

## Pharmacological Manipulation

Picrotoxin, bicuculline methiodide, L-2-4-diaminobutyric acid and nipecotic acid (Sigma-Aldrich, >95%) were diluted in physiological saline solution to 200 µM and then bath-applied to moth preparations as described previously (Lei et al., 2009). In short, pharmacological agents were applied to moth preparations through a syringe drip system. The time when the drugs took effect was determined by observing the change of spontaneous activity of the recorded neuron. Spontaneous activity and odor-evoked responses were first recorded under the normal physiological saline solution and then repeated under the drug treatment, and finally the normal saline wash. Note that the final

saline wash was typically applied many minutes after the initial recordings, during which the juxtacellular electrode may drift slightly, reducing the amplitude of the recorded spikes (see e.g., Figure 4A).

## Data Acquisition and Analysis

Spike traces were digitized at 25 kHz sampling rate using Datapack 2k2 software (Run Technologies, Mission Viejo, CA), and the time stamp of each spike was extracted off-line with the event-extraction function within the software package. The spike train data (columns of time stamps) were imported into a custom-written Matlab (The Mathworks Inc, Natick, MA) script, which calculates interspike-interval derived parameters such as mean instantaneous firing-rate and duration of the afterhyperpolarization. To determine the width of the response window, the spike train data were exported into Neuroexplorer (Nex Technologies, Littleton, MA) for plotting the peri-stimulus time histograms (PSTH), which allowed approximate estimation of response duration. Then the average of instantaneous spiking frequency (i.e., inverse of inter-spike interval) within the response window was calculated. We chose a 500 ms period starting from 120 ms after the onset of solenoid opening as response window. We also examined different window size such as 400, 600, and 750 ms and found no significant changes on our quantification of responses. This robustness may be due to the fact that the measurement is derived from averaging individual interspike intervals (ISI). In order to measure the duration of the afterhyperpolarization, we compared the ISI in a sequential manner after the stimulus onset. If an interval is at least 5 times longer than its previous interval, this later interval is considered as the afterhyperpolarization. All statistical comparisons were performed using the Statistics Toolbox of Matlab. To statistically compare the pharmacological effects in a balanced data set (i.e., across the same group of neurons at different stages, such as control vs. drug vs. wash), we selected the non-parametric Friedman's test (Figures 4, 8). Where there were only two groups in comparison (Figures 6, 9), we selected the non-parametric Mann-Whitney *U*-test. In both tests, the cut-off for type-I error were set at the 5% level (i.e., $\alpha = 5\%$). Following the Friedman's test, the Tukey-Kramer multi-comparison method was applied to determine the pairwise significance level.

## Computational Model

We constructed a spiking network model of the AL with a modest number of architectural features—allowing it to simulate certain kinds of AL phenomena—while at the same time having few enough parameters to allow for serious benchmarking and subsequent investigation. While we sketch out our model in this section, the full details of our model are contained within the Supplementary Information.

The network model discussed in this paper contains PNs, as well as two subclasses of local neurons: LN1s and LN2s. These neurons (totaling several dozen altogether) are organized into clusters that represent distinct glomeruli. The neurons are interconnected, both within each glomerulus and across glomeruli. This connectivity is illustrated in Figure 1.
<figure><img src="image_27.png" /><figcaption>**FIGURE 1** | Illustration of computational network architecture. On the left we illustrate the anatomy of a single glomerulus, comprising 10 PNs, 8 LN1s, 12 LN2s, each modeled using single-compartment integrate-and-fire equations. These neuronal populations are connected together sparsely via local intra-glomerular connections. Representative connections are illustrated in this diagram with colored lines, the thickness of which roughly corresponds to the coupling strength. In the middle we illustrate our full network, comprising K glomeruli (e.g., K = 2–6). The neuronal populations within each of these glomeruli are connected together sparsely via long-range inter-glomerular connections; these long-range connections have the same synaptic strength as the intra-glomerular connections, but are randomly connected using different sparsity coefficients. On the right we list a few tables for the synaptic coupling strengths (given in terms of approximate EPSP and IPSP amplitudes), and sparsity coefficients. A full description of the network, as well as the parameters involved, is given in the Supplementary Information.</figcaption></figure>

Within our network model, each neuron is modeled by a single-compartment integrate-and-fire equation, driven by a combination of intrinsic, feedforward and synaptic currents:

$$\tau_V \frac{d}{dt} V(t) = -(V - V^L) + I^{SK} + I_{fast}^{input} + I_{fast}^{syn,LN1} + I_{slow}^{syn,LN1} + I_{fast}^{syn,LN2} + I_{slow}^{syn,LN2} + I_{fast}^{syn,PN},$$

The intrinsic currents determine how each neuron responds to stimuli, and are different for the different neuron types (e.g., PN are equipped with SK-channels). The feedforward-input currents are independent (uncorrelated) between neurons, and are given by a feedforward Poisson input with time-varying rate. This feedforward Poisson input rate—which again depends on neuron type—comprises both a background (low rate) plus the time-varying stimulus-induced input (which can be high rate).

The synaptic currents involve recurrent nicotinic-type excitation (2 ms timescale), as well as GABA-A-type inhibition (2 ms timescale), as well as a slower synaptic inhibition (e.g., GABA-B-type with a ~750 ms timescale). The coupling strengths depend on the pre- and post-synaptic neuron types (e.g., the LN1 population inhibits the LN2s differently than the PN). In our model we assume that local neurons (LN1s and LN2s) are inhibitory, whereas PN are excitatory. We do not explicitly model any excitatory local neurons (see Olsen et al., 2007, as well as Shang et al., 2007), although the effective inter- and intra-glomerular excitation associated with such neurons might be similar to the excitatory effects of our PN (see Huang et al., 2010).

The recurrent connectivity matrix for our network is chosen to be an Erdos-Renyi random graph (i.e., each edge chosen independently with some given coupling probability) with

coupling probabilities that are functions of the pre- and post-synaptic neuron type and are slightly different for inter-glomerular connections vs. intra-glomerular connections.

As we will discuss below, we use our model to conduct numerical simulations: we subject our model to various stimuli while attempting to mimic a variety of experimental conditions. One important detail within this methodology is how we translate PTX and BIC application from the real world to our model. For our purposes, we will simulate PTX application as though PTX reduces the efficacy of GABA-A type receptors. When our model is operating under the influence of PTX (i.e., “PTX-on” condition), the postsynaptic currents associated with GABA-A synapses will be reduced by 75%. We similarly reduce by 75% the postsynaptic GABA-A currents under BIC application. In addition, we drastically reduce the SK-currents under this “BIC-on” condition (as motivated by the discussion in Section R1). Going forward, we will compare and contrast the behavior of our model in the PTX-on and BIC-on conditions with the “control” or CTRL-condition (i.e., CTRL = fully functional GABA-A and SK currents).

We emphasize two important features of our network are:

(1) We ensure that the inhibitory synaptic connections made by our LNs are “heterogeneous”; i.e., the distribution of post-synaptic connection strengths varies widely across the LN population. As mentioned above, we enforce this heterogeneity by dividing our LNs into two “subclasses” labeled LN1 and LN2. While both subclasses of LNs are connected sparsely and randomly to the other neurons in our model, the distribution of connection strengths is different for the LN1 and LN2 subclasses. This heterogeneity implies that some LNs will mostly inhibit PN, without inhibiting too many other LNs, whereas some other LNs will do
the opposite. This heterogeneity is crucial for allowing our model to facilitate PTX-on disinhibition of the PNs (see Section R1 for discussion).

(2) We ensure that the PNs are equipped with intrinsic SK-currents. These inhibitory currents are driven by each PN's own firing, serving to prevent that PN from firing multiple times in a row. Once elevated, this current persists for quite some time, decaying after ~400 ms. The presence of such a persistent intrinsic inhibitory current is crucial for allowing our model to facilitate the BIC-on shortening of the AHP-phase (see Section R1 for discussion).

## Benchmarking the Model

The model described above has several parameters which influence its dynamics. These parameters include both the strength and sparsity of the synaptic connections, as well as the strength of the intrinsic SK-currents and feedforward currents. Many of these parameters are constrained somewhat by physiology (e.g., the synaptic coupling strengths must be compatible with the observed sizes of EPSPs and IPSPs). Nevertheless, even as these parameters are varied within physiological bounds, the network can still produce a wide variety of dynamical regimes, ranging from the physiologically realistic to the unrealistic.

In order to further constrain these network parameters we “benchmark” our model. That is, we choose a variety of experimentally observed phenomena associated with the real AL (i.e., benchmarks) and demand that our network satisfy these benchmarks. Given any particular set of parameters—thought of as a point in parameter space—our network will operate within a particular dynamical regime and, generally speaking,

few-to-none of these benchmarks will be satisfied. Our goal is to find a region in parameter space that corresponds to dynamical regimes that satisfy *all* of our benchmarks; we hope that these dynamical regimes will be “realistic” to some extent.

Our benchmarks are listed below:

(1) Firing-rates, EPSPs and IPSPs: In the real AL, PN and LN firing-rates are between 5 and 15 Hz in background (i.e., when unstimulated), and usually between 40 and 80 Hz when stimulated. EPSPs and IPSPs are usually smaller than 1 mV. We require that corresponding values for our network lie within these acceptable ranges.

(2) Pulse-response attenuation: In the real AL, the PNs will respond less vigorously to an odor puff if that puff was immediately preceded by a previous puff. This phenomenon gives rise to attenuation of the PN response to a rapid sequence of odor pulses. We require that our network exhibit a similar attenuation when stimulated with simulated odor pulses. Compare **Figure 2** and **Figure 3**.

(3) PTX response when unstimulated: This benchmark is intended to capture the phenomena discussed in Section R1. As mentioned above, the pharmacological application of PTX to the real AL is translated in our network to the reduction of GABA-A presynaptic currents by ~75%. We require that, when compared against CTRL, the PTX-on condition exhibit both (i) a reduction in spontaneous PN firing-rates, as well as (ii) an increase in the typical spontaneous PN ISIs. Compare **Figure 4** and **Figure 5**.

(4) PTX response when stimulated: This benchmark is intended to capture the effects of PTX on the PN response to a train of odor pulses. As per experiment, we require that (i) the mean PN response per pulse for the PTX-on condition should

<figure><img src="image_28.png" /><figcaption>**FIGURE 2** | Varying the inter-pulse-interval (IPI) affects the attenuation of PN responses. (A) On top we show raster-plots of a PN response to five pulses in sequence (IPI 500 ms). The peristimulus-time-histogram is shown in the middle, and the normalized response per pulse is shown on the bottom. Note that there is a marked attenuation of PN response following the first pulse. (B) IPI = 1 s, and the attenuation is less marked. (C) IPI = 10 s, which is significantly greater than the AHP-phase; there is no attenuation of PN response.</figcaption></figure>
<figure><img src="image_29.png" /><figcaption>**FIGURE 3** | Our model qualitatively reproduces the pulse-response attenuation seen in Figure 2. On top we show traces of PN-activity for a model glomerulus responding to a sequence of five pulses. On the bottom we show the normalized spike-count per-pulse. On the left we use an IPI of 512 ms. On the right we use an IPI of 2048 ms, which is significantly longer than the AHP-phase.</figcaption></figure>

be comparable to that of the CTRL-condition, whereas (ii) the standard-deviation in PN response per pulse should be significantly higher when PTX is on. Compare **Figure 10** and **Figure 11**.

(5) BIC response when stimulated: This benchmark is intended to capture the phenomena discussed in Section R1. As mentioned above, the pharmacological application of BIC to the real AL is translated in our network to the combination of (i) a reduction of GABA-A presynaptic currents by ~75%, as well as (ii) a reduction in the strength of the intrinsic SK-currents that follow hyperpolarization of the PNs. We require that, when BIC is on, our model PNs exhibit prolonged responses to odor stimuli; responses that show a much diminished AHP-phase. Consequently, the BIC-on state should reduce PN pulse-response attenuation and prevent PNs from faithfully tracking rapid sequences of odor pulses. Compare (Lei et al., 2009) with Supplementary Figure S7.

(6) BIC response when unstimulated: This benchmark is intended to capture the effects of BIC on the spontaneous state. We require that, when BIC is on, the PNs exhibit slow modulation in their background dynamics, switching between long epochs of periodic firing and long epochs of relative silence (typical epoch length should be several

seconds to tens of seconds). Compare **Figure 12** and **Figure 13**.

To actually perform our benchmarking we repeatedly scanned sections of parameter-space by varying one or two parameters at a time, covarying the most influential parameters whenever possible. For each scan we chose the “best” set of parameters (i.e., those which came closest to satisfying our benchmarks) and scanned again; varying different parameters the next time. This repeated parameter-scanning was done by hand (and not automated) so that (i) we could gain some intuition for the vastly different kinds of dynamic-regimes our network was capable of producing, and (ii) we could be sure that our results were not too sensitive to any single parameter. We continued searching until we found a large open region in parameter-space, each point of which gave rise to a rather similar dynamical regime that exhibits all of our benchmarks. **Figure 1** lists sample values for many of these coupling and connectivity parameters for one point within such a region.

After benchmarking our network, we investigated the mechanisms at work within the resulting dynamical regime. We found that the dynamical regime that supported the above phenomena was one of “high-gain,” with strong recurrent connectivity that gives rise to multiple-firing-events (MFEs). This regime is discussed at length below in Section R2.
<figure><img src="image_30.png" /></figure>

**FIGURE 4** | Picrotoxin reduces PN's spontaneous activities. (A) Juxtacellular recording traces (12 s long) show a marked reduction in the number spikes due to the application of picrotoxin. (B) The reduction of number of spikes due to picrotoxin is statistically significant (Friedman test, *p* < 0.01, *n* = 8) but the effect is not fully reversible. Different letters on top of the box plots indicate statistical significance. (C) Cumulative sum of interspike intervals (ISI) shows that the picrotoxin treatment prolongs the maximal ISI to about 4 s (red line) while the maximal ISI under saline control is only about 1.5 s (black line). Moreover, nearly all (about 98%) ISIs under saline control are less than 0.5 s but about 87% of ISIs under picrotoxin are within this range. Saline wash produces a pattern that is between the drug treatment and control (blue line).

# RESULTS

## Section R1: Initial Experiments

In this section we present some of our experiments which hint at the nature of the after-hyperpolarization (AHP) phase in projection neuron (PN) response. These experiments will strongly suggest that the AHP-phase comprises both inhibitory synaptic currents as well as hyperpolarizing intrinsic currents.

To preface, recall that the “control-condition” (i.e., saline wash, rather than any active pharmacological agent) produces spontaneous PN activity in the range of 6–12 Hz (see, e.g., **Figure 4A**). When stimulated by a pheromone pulse, the PN activity increases vigorously, and is usually followed by an AHP-phase, expressed as a nearly silent period in the raster plots and PSTH following each pulse (see e.g., **Figure 6A**). This AHP-phase not only truncates the excitatory response evoked by each odor pulse but also lasts for about a second or so. As a result, the AHP-phase caused by any given odor pulse can interfere with—and reduce—the magnitude of excitatory response to any subsequent

odor pulse occurring shortly after the first. To quantify this attenuation, we stimulate the MGC with a rapid sequence of five successive odor pulses (see methods) characterized by an “inter-pulse-interval” (IPI) ranging from IPI = 0.5–10 s. As expected, the PN response shows a marked attenuation when the IPI is less than or equal to the observed duration of the AHP (see **Figure 2**). On the other hand, when the IPI = 2 s or longer, the AHP from each pulse dies away before the next pulse arrives, and so the AHP does not significantly affect the PN response across pulses (i.e., there is little to no attenuation when $IPI \ge 2$ s).

Our first set of experiments perturbs the scenario above through the pharmacological application of picrotoxin (PTX) to the MGC. PTX has been shown to be an effective GABA-A receptor antagonist in both vertebrate and invertebrate preparations (Newland and Cull-Candy, 1992; Anthony et al., 1993; Laurent et al., 1999; Lee et al., 2003; Choudhary et al., 2012; Warren and Kloppenburg, 2014). Consequently, we expect PTX application to increase the PN response. However, to the contrary:
<figure><img src="image_31.png" /></figure>

**FIGURE 5** | Our model qualitatively reproduces the PTX-induced phenomena seen in Figure 4. On top we show traces of PN-activity for a model glomerulus in background, both in the control state (black) and PTX-on state (red). Below these traces we plot the time-averaged PN firing-rate (averaged over 1 s bins). On the bottom we show the average PN firing rates (left) and cumulative-distribution-function for the ISI-intervals (right).

## PTX Decreases PN's Spontaneous Activity

Under our experimental conditions, perfusing the moth AL with PTX (200 µM) significantly reduced the level of spontaneous activities on PNs (**Figures 4A,B; Friedman test, p < 0.01, n = 8**). Despite a reduction of the number of spikes (from 70 to 120 with median of 79 in a 10-s window to 10–50 with median of 20, **Figure 4B**), the bursting pattern was not altered (**Figure 4A**, middle panel). Apparently, the reduction of number of spikes was primarily caused by the increase of ISI, especially those intervals between the bursts. This inference was further confirmed by plotting the cumulative probability sum of ISI across saline control (812 ISIs pooled from eight neurons), PTX treatment

(261 ISIs) and saline wash (309 ISI; **Figure 4C**). Without PTX (i.e., saline control), the maximal ISI was 1.72 s (**Figure 4C**, black line), but this number went up to 3.95 s with PTX, an increase of 129% (**Figure 4C**, red line). Moreover, the distribution of ISIs associated with the saline control group was shifted (toward shorter ISI times) relative to the distribution of ISIs associated with the PTX treatment. For example, 95% of the control-ISIs were shorter than 0.2 s, whereas this range only comprised about 85% of the ISIs under PTX-treatment. Saline wash did not reverse the ISI distribution to the control pattern completely, but rather to a pattern between the saline control and drug treatment (**Figure 4C**, blue line).
<figure><img src="image_32.png" /><figcaption>**FIGURE 6** | Picrotoxin increases the duration of afterhyperpolarization (AHP). (A) Raster plot and peristimulus histogram show a typical excitatory response of a Toroid PN to five pulses of EZ stimulation. The stimulus onset is at time zero and lasts for 50 ms. Note the AHP (dotted arrows) following the bursts of excitatory responses. (B) Under the picrotoxin treatment, the duration of AHP is significantly increased, along with a reduced spontaneous activity. (C) Group data showing that the picrotoxin-induced increase of AHP duration is statistically significant either under 1 or 10 ng stimulation (Mann Whitney U-test, *p* < 0.05 or 0.01, *n* = 8; asterisks indicate the significance level).</figcaption></figure>

In addition to measuring the effects of PTX on spontaneous activity, we also measured the effects of PTX on the AHP-phase. We observed that:

## PTX Increases the Duration of PN's AHP Phase

As mentioned above, the MGC PNs' excitatory response to pheromones is usually followed by an AHP-phase, expressed as a gap in the raster plots and PSTH (Figure 6A, dotted arrows). The length of the AHP period was significantly increased by PTX application (Figure 6B, dotted arrows). Because the AHP is positively correlated with odor concentrations (Figure 7A), we also compared the PTX effect on low (1 ng) and high (10 ng) dose evoked responses. In both cases, PTX significantly increased

<figure><img src="image_33.png" /><figcaption>**FIGURE 7** | The length of the afterhyperpolarization (AHP) period is positively related to the odor concentrations under saline control (A), but the linear relationship is lost after picrotoxin treatment (B) (*N* = 8).</figcaption></figure>

the length of AHP (Mann Whitney U-test, *p* < 0.05 or 0.01, *n* = 8; **Figure 6C**). Interestingly, PTX application disrupted the linear correlation between odor concentrations and the duration of AHP (**Figure 7B**).

Thus, despite the fact that PTX is a GABA-A receptor antagonist, perfusion of PTX actually enhances the inhibitory modulation of the PNs. Moreover, because PTX increases the duration of the AHP-phase, these effects likely stem from an increase in the inhibitory currents responsible for the AHP-phase, and not to secondary-effects of PTX which might block nicotinic-excitation (as seen, e.g., in honeybee, see Barbara et al., 2005). Based on these considerations, we will explore the hypothesis that the PNs may be involved in a disinhibitory network operating within the MGC or even spanning the AL (for motivation, see Christensen et al., 1998a or Buckley and Nowotny, 2011).

As a very simple example of such disinhibition, one may consider a 3-neuron circuit consisting of a single local neuron (LN1) inhibiting a second local neuron (LN2) which inhibits
<figure><img src="image_34.png" /></figure>

**FIGURE 8** | Sketch of an inhibitory and disinhibitory circuit, as well as a more realistic disinhibitory network: (A) We illustrate a simple inhibitory circuit, involving a single LN which produces presynaptic inhibitory current I that inhibits a single PN. Under PTX we expect the efficacy of inhibitory synapses to decrease; thus I should get smaller, implying that the PN activity will increase. (B) We illustrate a simple disinhibitory circuit, involving an LN1 which produces a presynaptic inhibitory current I1 that inhibits another LN2. This LN2, in turn, produces a presynaptic inhibitory current I2 that inhibits a single PN. If the activity level of the first LN1 is high, we may expect LN2 to be only weakly active, and for the PN to be active. Under PTX we expect that the efficacy of inhibitory synapses will decrease: as a result I1 will decrease, and so the activity level of LN2 will increase. This increased activity level—combined with the now decreased efficacy of inhibitory synapses—will alter I2. If the activity level increase of LN2 is sufficiently large, it is possible for I2 to actually increase overall (even though PTX has been applied). As a consequence, it is possible for the PN activity level to actually decrease overall—even though PTX has been applied. (C) A more realistic disinhibitory network would involve not just three neurons, but rather a collection of LNs and PNs, with the former heterogeneously coupled. In the heterogeneous network shown we have colored the various LNs according to the role they might play with regards to disinhibition: those LNs that predominantly inhibit PNs are classified as “LN2s” and colored blue, while the LNs that predominantly inhibit the LN2s are classified as “LN1s” and colored green. In reality the roles are not so clear cut; some LNs will both inhibit PNs as well as inhibit other LNs, and it may not always be possible to clearly classify each and every LN into a specific role.

a projection neuron (PN). The layout for this simple circuit is illustrated in **Figure 8**. We'll also assume—for exposition—that this simple circuit is operating in a mean-driven regime (see, e.g., Destexhe and Sejnowski, 2009; Buckley and Nowotny, 2011). In such a regime, each neuron receives independent feedforward input currents that—alone—would be sufficient to cause them to fire at high rates. As we'll discuss next, this mean-driven regime can be understood by analyzing its firing-rates.

The “control” situation for this simple circuit involves LN1 being very active with high firing-rate $m_1$. In this condition, since LN1 is very active, the inhibitory presynaptic currents to LN2—denoted by “$I_1$” will be proportional to the LN1 activity. That is to say, $I_1$ will be roughly $S \times m_1$ for some “synaptic strength” S. The large presynaptic current $I_1$ will ensure that LN2 is only weakly active, with a firing-rate $m_2$ which will be a function [i.e., $f(\cdot)$] of the total input current to LN2. In this case we expect $m_2 = f(E - I_1) = f(E - Sm_1)$, where f depends on both $I_1$, as well as some background excitatory current E; $m_2$ should be lower as $I_1$ increases. The presynaptic inhibition to the PN—denoted by $I_2$—will be proportional to $f(E - I_1)$, roughly determined by something like $I_2 = S \times f(E - I_1) = Sf(E - Sm_1)$. Because $m_1$ and S are high, $I_1$ will be high, so $m_2 = f(E - I_1)$ will be low, so $I_2$ will be low, and the PN activity will be high.

When PTX is applied to this simple circuit, the situation will change. The LN1 will remain active, but no longer inhibit LN2 as much. If the application of PTX blocks, say, three-quarters of the GABA-A receptors, we might imagine the synaptic-strength S reduced to $1/4S$. With this reduction the presynaptic inhibition to LN2 is only $I_1 = 1/4Sm_1$, and so the new (higher) firing rate of LN2 will be $m_2 = f(E - 1/4Sm_1)$. This new

firing-rate $m_2$ might be much higher than before (i.e., the firing-rate may be a nonlinear function of the presynaptic currents), implying that the presynaptic inhibitory current to the PN will change to $I_2 = 1/4Sm_2 = 1/4Sf(E - 1/4Sm_1)$. If f has the appropriate structure, it is certainly possible that $I_2$ might actually be higher under PTX than under the control condition. In such a situation, we would observe the PN activity drop under PTX.

To be clear, we are not suggesting that each PN in the MGC is the target of an idealized disinhibitory circuit such as in **Figure 8B**, nor that the MGC operates in a mean-driven regime where such a firing-rate analysis is valid. Rather, we are suggesting that perhaps the collection of LNs in the MGC may be interconnected in such a way as to give rise to a similar disinhibitory phenomena— even without any single simple mean-driven disinhibitory circuit existing in isolation (see e.g., **Figure 8C**). Put another way: we suggest that an appropriately heterogeneous LN population (i.e., a population of LNs that have varying degrees of connectivity and coupling strength, both to each other and to the PNs) might—as a gestalt—give rise to the PTX-induced phenomena we observed above.

If, indeed, the MGC PNs are targets of such emergent disinhibition, we would expect many of the results we see under PTX to also manifest under other pharmacological agents which increase the overall level of inhibition in the MGC. One way to test this intuition is to use GABA transporter blockers— specifically L-2-4-diaminobutyric acid (L-DABA) and nipecotic acid. These blockers should increase the GABA concentration in the tissue (Mbungu et al., 1995; Oland et al., 2010). As confirmed below, this increase in GABA concentration has similar effects to PTX:
<figure><img src="image_35.png" /><figcaption>**FIGURE 9** | Blocking GABA transporters increases the duration of afterhyperpolarization (AHP). Two GABA transporter blockers, L-DABA (A) and Nipecotic acid (B), were used to treat the experimental preparations, and both blockers increase significantly the duration of AHP. The saline wash, however, does not fully reverse the drug effect. Different letters on the bar graphs indicate statistical significance (Friedman test, *p* < 0.01, *n* = 5).</figcaption></figure>

## GABA Transporter Blockers Enhance AHP

We perfused the AL with GABA transporter blockers, L-2-4-diaminobutyric acid (L-DABA) and nipecotic acid. As expected, both blockers increased the AHP duration significantly (Figure 9) (Friedman test, *p* < 0.01, *n* = 5). The saline wash, however, did not have significant effects. This could be due to insufficient amount of washing time limited by recording sessions.

Based on the PTX, L-DABA and nipecotic acid experiments above, we concluded that (i) the PNs in the MGC participate in some kind of disinhibitory circuit, and (ii) that disinhibitory circuit gives rise to an inhibitory presynaptic current within the PNs that contributes to the AHP-phase.

While sensible given the experiments we've discussed so far, this conclusion is not obviously consistent with some of our previous experiments involving bicuculline methiodide (BIC; see Lei et al., 2009). To elaborate, BIC is similar to PTX, in that

both agents are putative GABA-A receptor antagonists within the *Manduca* AL (Christensen et al., 1998b). Because there may be differences in the affinity of each agent for GABA-A, we don't expect BIC to act in exactly the same way as PTX. Nevertheless, at first blush we expected the effects of BIC to be qualitatively similar to PTX: i.e., to also lengthen the AHP within PN MGCs. To the contrary, however:

## BIC Eliminates the PN's AHP-Phase

BIC application substantially reduces the length of the AHP-phase well below ~200 ms, and sometimes eliminates the AHP-phase altogether. Consequently, under BIC the PN response exhibits a much prolonged excitatory phase, persisting several hundred milliseconds after the pheromone stimulus is removed. In addition, due to the lack of an AHP-phase, the PN response exhibits little to no attenuation from one odor pulse to the next—even when those odor pulses are within 1 s of one another (e.g., an IPI of 512 ms). Thus, this BIC-induced lack of attenuation prevents PNs from faithfully tracking the dynamics of a pulsatile odor stimulus (Lei et al., 2009).

These results are surprising; the BIC induced phenomena within the MGC seem diametrically opposite to the PTX induced phenomena. Thus, even though they are both GABA-A receptor antagonists (Waldrop et al., 1987)<fnref n="1" />, PTX and BIC cannot be doing the same thing to the MGC.

One potential explanation for this paradox is that BIC is actually more than just a GABA-A receptor antagonist. Specifically, BIC could also block certain channels within the PNs – channels that give rise to intrinsic currents which, in the absence of BIC, ordinarily contribute to the AHP-phase (for precedent see Villalobos et al., 2004; Pedarzani et al., 2005; Belmabrouk et al., 2011a). While this leap of logic may seem farfetched at first, we believe that there is a natural candidate for such channels: namely, calcium-dependent small-conductance potassium channels (SK-channels). Indeed, in a functional study of cloned SK-channels using *Xenopus* oocytes, BIC was found to block two types of SK-channels (Khawaled et al., 1999).

Although there is no direct molecular evidence that proves that *Manduca* PNs possess SK-channels, there are several pieces of evidence that point toward this possibility:

1. In the fruit fly, *Drosophila melanogaster*, one type of SK channel was reported. Moreover, this channel is likely important for sensory processing, since the photoreceptors of the mutant flies lacking the gene of this channel produce oscillatory currents that hinder their responses under dim conditions (Abou Tayoun et al., 2011).

2. In mammals, SK-channels are believed to mediate the AHP-currents following action potentials, both inhibiting cell firing and limiting the firing frequency of repeatable action potentials (Bond et al., 1999; Adelman et al., 2012).

<footnote n="1">¹Even though we used different methods to introduce PTX and BIC into the AL—bath perfusion vs. multibarrel pressure injection—we do not believe that this difference in procedure could be wholly responsible for the extreme discrepancies we observed in the PN dynamics.</footnote>
(3) An SK channel homolog, KCNL-2, has also been characterized in the nervous system of *C. elegans* and is believed to regulate egg laying behavior (Chotoo et al., 2013).

Is it possible that *Manduca* PNs are indeed equipped with SK-channels, and that these channels are both partially responsible for the AHP and blocked by BIC? On the surface, this scenario might be consistent with the experiments described above. Recall that PTX and BIC gave rise to, respectively, a lengthening and shortening of the AHP-phase. Perhaps, as previously discussed, PTX reduces the effectiveness of GABA-A receptors, thus lengthening the AHP-phase of the PNs through a disinhibitory network of LNs. Now BIC should also reduce the effectiveness of GABA-A receptors somewhat, but could also block putative SK-channels within the PNs. While the former alone would reduce the PN activity, just like PTX, the latter could remove a substantial component of the AHP-currents, increasing PN activity. Perhaps a combination of these two effects could somehow result in both the PTX-induced phenomena we see above, as well as the BIC-induced phenomena observed in Lei et al. (2009).

Going forward, we will explore this possibility: We will use computational modeling to investigate the scenario sketched out in the previous paragraph. More specifically, we will create

a spiking neuronal network that has (a) strong heterogeneous recurrent connectivity across the LN population, and (b) SK-channels within the PNs. We will determine whether or not it is even possible to benchmark such a network against the PTX- and BIC-induced phenomena described above. In doing so, we'll expose mechanisms that may be at work within the MGC or, more generally, across many glomeruli within the AL.

Before we embark on such a project, we comment on two somewhat more subtle phenomena we have observed; the first relating to PTX application, the second to BIC:

## PTX Disrupts PN's Response Consistency across Repeated Isolated Stimuli

Recall that, in response to isolated pulses of pheromonal stimuli, the MGC PNs typically generate bursts of action potentials tracking each stimulus pulse (Figures 10A,B). Because the inter-pulse-interval (IPI = 2 s) in this case was sufficiently greater than the typical AHP-length (compare, e.g., Figure 6C with Figures 10A,B), the response from one pulse did not “interact” with the following pulse; consequently, there was little to no attenuation of the PN response across pulses. The same holds under PTX application, which did not significantly change the PNs’ response magnitude, measured as the mean instantaneous firing rate during the response window across odor pulses

<figure><img src="image_36.png" /><figcaption>**FIGURE 10** | *Picrotoxin* increases response variations. (A,B) Juxta-cellular recording traces show the responses of a Toroid PN to four pulses of EZ stimulation under saline control (black trace) and picrotoxin treatment (red trace). (C,D) Picrotoxin does not significantly change the mean instantaneous firing rate during the response window either to 1 or 10 ng stimulation; however, the treatment significantly increases the response variation measured by the standard deviation of firing rate across odor pulses (Mann Whitney U-test, *p* < 0.01, *n* = 8; asterisks indicate the significance level).</figcaption></figure>
(Figure 10C, Mann Whitney U-test, *p* > 0.05, *n* = 8). However, under PTX, the excitatory responses from pulse to pulse were not as consistent as those under the saline control, shown by a significant increase of the standard deviation of the mean instantaneous firing rate across all 5 odor-evoked responses (Figure 10D, Mann Whitney U-test, *p* < 0.01, *n* = 8). These drug effects were similarly observed when using low (1 ng) or high (10 ng) concentration of odors (Figures 10C,D). See Figure 11 for comparison with our model.

## BIC Introduces Structure into the PN Spontaneous Activity

When unstimulated, the MGC PNs usually produce sporadic spontaneous activity with no obvious structure. Under BIC application, the spontaneous PN activity can change into a long-lasting structured pattern, which alternates between epochs of fast-periodic-spiking and epochs of near total quiescence. The epochs of fast-spiking are characterized by ISI-intervals of

~50 ms, whereas the quiescent epochs have firing-rates near 0 Hz. The epochs can each last for several tens of seconds, and alternation between the spiking and silent epochs continues for as long as BIC is supplied. The transition between any given spiking epoch and the subsequent silent epoch can be very abrupt—often much less than 100 ms—and sometimes even instantaneous. While we had originally observed this phenomenon in our previous work (Lei et al., 2009), we confirmed it once again with a new set of experiments. In these recent experiments we again observed BIC-induced spontaneous activity patterns, this time with even more variation than what we had originally seen in 2009. Although the spiking activity was generally increased by BIC application, only one MGC PN exhibited extreme rhythmicity when alternating between quiescent and spiking epochs (asterisk in Figure 12A). The other MGC PNs also exhibited long-lasting epochs of spiking as well as quiescent epochs, but were less rhythmic (Rows 1–3 of raster plots in Figure 12A).

<figure><img src="image_37.png" /><figcaption>**FIGURE 11** | Our model qualitatively reproduces the PTX-induced phenomena illustrated in Figure 10. (A,B) PN-activity for a model glomerulus subject to a train of stimulus pulses separated by an IPI of 2 s. (C) The PTX-on state induces small changes in the mean PN-response—i.e., instantaneous firing-rate—averaged across pulses. (D) The PTX-on state induces larger changes in the standard-deviation (across pulses) of PN-response.</figcaption></figure>
<figure><img src="image_38.png" /><figcaption>**FIGURE 12** | Bicuculline methiodide (BIC) alters the spiking patterns of the spontaneous activity of PNs both in the MGC and in the ordinary glomeruli.

(A) Raster plots show that BIC treatment generally increases the spontaneous activity level of MGC PNs, opposite to the picrotoxin treatment, and produces the run-away pattern (asterisk) in some PNs. (B) Such run-away patterns are also present outside the MGC when BIC is used, verifying the predictions from the network model.</figcaption></figure>

These two phenomena both expose rather specific dynamic features of the MGC and neither is an obvious epiphenomenon of the mechanisms we have proposed so far. To elaborate, even if PTX does disrupt a disinhibitory network involving the PNs, why would such a disruption necessarily reduce the consistency of PN response across isolated pulses? Furthermore, even if BIC does block SK-channels—which have dynamics in the 100–500 ms range—why would blocking these channels give rise to structured spontaneous activity on a 10 s time-scale?

Thus, to further constrain and validate our computational modeling, we will use the above two phenomena as additional benchmarks. That is to say, we will determine if our computational model, possessing both (a) heterogeneous connectivity across the LNs and (b) SK-channels within the PNs, can reproduce all the phenomena discussed so far.

## Section R2: Computational Modeling

In this section we briefly describe our computational model, and use it to probe the potential consequences of disinhibition and SK-channels within the MGC.

Note that ours is certainly not the first model to investigate these mechanisms within the *Manduca* AL. For example, a mean-field model by Buckley and Nowotny (2011) analyzes the role of disinhibition within an idealized inhibitory network without

fast synapses. As another example, a spiking network-model by Belmabrouk et al. (2011a) includes SK-type channels in order to replicate some of the pharmacological results seen in Lei et al. (2009)—specifically the elimination of the AHP-phase and diminished pulse-tracking properties observed under BIC-application.

We view both these works as encouraging, and take them as additional support for the disinhibition and SK-channel hypotheses. That being said, our model—which combines disinhibition and SK-channels—is the only model we are aware of that attempts to capture the broad range of PTX- and BIC-induced phenomena we observed in Section R1. Moreover, as we will eventually discuss later, our modeling study illuminates the importance of multiple-firing-events, which depend critically on fast synapses and cannot be well understood via a mean-field framework.

Our model is a spiking network model of a few interconnected glomeruli within the *Manduca* AL. This network is built out of several dozen spiking single-compartment integrate-and-fire neurons, using the voltages and conductances of the individual neurons as microscopic variables. Each glomerulus in our model corresponds to a relatively tightly knit cluster of a few dozen neurons, including inhibitory LNs and excitatory PNs. In terms of connectivity, we have abstracted the complex topology of
the real AL as follows: we assume that different populations of neurons are interconnected sparsely and randomly, both within each glomerulus as well as across glomeruli. We remark that we do not explicitly model excitatory LNs, instead assuming that their effects are similar to the effects of the excitatory PNs (to which they may be strongly connected—see e.g., Huang et al., 2010).

With regards to the network's dynamics, we equip our neurons with fast synaptic currents, corresponding to nicotinic-type excitation and GABA-A-type inhibition, as well as slower inhibitory synaptic currents with a decay time ~750 ms. Both our LNs and PNs exhibit fast sodium-like spikes (modeled via the integrate-and-fire equations), but our PNs are also equipped with a slow intrinsic inhibitory current mimicking the putative SK-currents discussed above (decay time-scale ~400 ms). Each neuron in our network is also driven by independent feedforward Poisson input comprising (i) a background drive and (ii) a stimulus-specific drive targeting specific glomeruli at specific times.

These are the main ingredients of our model. Note that, as described in the Methods Section and in the Supplementary Material, our model has (a) heterogeneous recurrent inhibition provided by the LNs, as well as (b) slow intrinsic SK-currents within the PNs; while the latter is considered in Belmabrouk et al. (2011b), the former is not. The parameters for our model include excitatory and inhibitory couplings strengths (both within and across glomeruli), the strength of the SK-currents within the PNs, and the strengths of the feedforward input currents. As mentioned in the methods section and discussed in more detail in the Supplementary Material, we proceeded to tune this model by varying the parameters. Our goal when tuning was to search for parameters for which our model was “biologically plausible.” That is, for which our model satisfied all the benchmarks associated with our observations of the AL. We found that, indeed:

**Our model allows for “biologically plausible” behavior**

There exists a region in parameter space for which our model can simultaneously exhibit the following phenomena discussed in Section R1: (1) Firing-rates, EPSPs and IPSPs similar to those observed in the real AL; (2) Pulse-response attenuation for IPIs < 1 s, (3) PTX-induced reduction in PN spontaneous firing rates, (4) PTX-induced loss of consistency across isolated stimulus pulses, (5) BIC-induced reduction in PN pulse-response attenuation and pulse-tracking, and (6) BIC-induced slow patterns when unstimulated. These phenomena are illustrated in **Figures 3, 5, 11, 13**, as well as in Supplementary Figure S7.

Even this modicum of success points toward the plausibility of our previous hypothesis. Namely, that the phenomena we observed might be due to (a) heterogeneous recurrent connectivity involving the LNs and (b) intrinsic SK-currents within the PNs. More importantly, however, our computational model gives a hint as to how these architectural mechanisms give rise to the phenomena at hand, and how

those phenomena might coexist within a single dynamical regime.

After analyzing our model, we discovered that all the biologically plausible regimes we found shared a few things in common:

(1) **Strong recurrent inhibition:** Unsurprisingly, all our biologically plausible regimes had large inhibitory coupling strengths. This is to be expected, as we engineered our model to possess a heterogeneous collection of LNs capable of disinhibition. In order for our network to exhibit the desired PTX-induced reduction in PN firing rates, the effects of such disinhibition should be significant. Strong recurrent inhibition seemed to be a necessary prerequisite for this.

(2) **Strong intrinsic SK-currents:** Also unsurprisingly, all our biologically plausible regimes had large amplitude SK-currents within the PNs, corroborating previous modeling work by Belmabrouk et al. (2011a). This again is expected, as we required PNs in our model network to exhibit a BIC-induced reduction in the AHP-phase. Recall that, in our network, BIC-on corresponds to a reduction in both GABA-A-currents and SK-currents. Given that the former alone would lengthen the AHP-phase (through disinhibition), it is crucial that the AHP-phase also comprise a strong intrinsic inhibitory component—in our case this was an SK-current. In order for the BIC-on state to shorten the AHP-phase, this SK-current must be strong enough that its removal (under BIC) “outweighs” the additional presynaptic inhibition received by the PNs due to the disinhibitory network.

(3) **High gain:** Our model functioned well when the feedforward, intrinsic and synaptic currents combined to ensure that, most of the time, at least some neurons in the network had membrane potentials that were not too far from the firing-threshold. This requirement is tantamount to the statement that—barring obvious exceptions such as PNs in the AHP-phase—the currents driving each neuron were neither overwhelmingly excitatory nor overwhelmingly inhibitory. This “high gain” regime allowed neurons to be responsive to fluctuations in their input currents; this responsivity played an important role in all the phenomena we sought.

(4) **Strong recurrent excitation:** Finally, all our biologically plausible regimes had large excitatory coupling strengths. In this case “large coupling strengths” specifically means that the typical EPSP was of the same order as—or not too much smaller than—the typical IPSP coming from the heterogeneous network of LNs. This requirement can be thought of as a special case of the “high-gain” requirement from the previous paragraph, restricted to presynaptic currents. These large coupling strengths were instrumental in producing the somewhat more subtle dynamic phenomena discussed toward the end of Section R1.

These last two requirements—a high-gain state with strong recurrent excitation—were crucial for our model, and gave rise to a very important dynamic feature:
<figure><img src="image_39.png" /><figcaption>FIGURE 13 | Our model qualitatively reproduces the BIC-induced phenomena illustrated in Figure 12. (A) On top we show the spontaneous PN-activity for both glomeruli (two shades of gray) in a *K* = 2 glomeruli model. The time-averaged spike-count (2 s bins) is shown on the bottom. (B) On top we show the spontaneous PN-activity for both glomeruli (two shades of red) in the same *K* = 2 glomeruli model under the BIC-on state. Note that not only are there long periods of elevated activity observed within each of the glomeruli, but also that these periods of activity are anti-correlated with one another.</figcaption></figure>

## Our Model Exhibits Emergent "Multiple-Firing-Events" —or MFEs

These MFEs are a special kind of causally-linked synchronization across subsets of PNs. This brief synchronization occurs because the PNs are in a high-gain state; there are often a few PNs which are not too far from the firing-threshold. Because of the strong recurrent excitation, one or two typical EPSPs can close this gap, causing one spike to lead to the next. That is to say, it will not be uncommon for any given PN spike to drive at least one other postsynaptic PN over the spiking-threshold, causing a second PN spike almost immediately (i.e., within 1–2 ms). This second spike may cause a third, and so forth, resulting in a chain reaction including several PNs over a comparatively short period of time (say, <5 ms).

That these chain-reactions can occur depends on the high-gain and strong recurrent excitation; whether or not a chain-reaction *will* occur at any given time is due primarily to luck—which PNs have high subthreshold voltages and which do not.

While it is certainly possible for MFEs to occur spontaneously (i.e., when the model is unstimulated), most MFEs occur during the initial response to stimulation. This initial response period corresponds to the “highest-gain” of the PNs, before they will be suppressed by the inhibitory currents from the forthcoming AHP-phase.

An example of an MFE within an idealized 3-neuron network is shown in **Figure 14A**. This network comprises 3 PNs which are stimulated with feedforward Poisson input similar to our full network; the synaptic time constants are also similar to our full network (i.e., 2 ms), but the synaptic coupling strengths are about a factor of 10 higher so that MFEs clearly manifest. On the top left we show a short 5 ms sample trajectory from this network with the subthreshold voltage of each PN color-coded in accordance with the network to the right. Because each neuron is modeled using the integrate-and-fire equations, each subthreshold-voltage will fluctuate (based on its combination of input currents) until it reaches the firing-threshold (VT), at which point the neuron will
<figure><img src="image_40.png" /><figcaption>**FIGURE 14** | Illustration of multiple-firing-events (MFEs): (A–C) Here we use a collection of small networks to illustrate the kinds of chain-reactions we see in our simulations. See text for details.</figcaption></figure>

fire and reset to VR (firing denoted by vertical line), no longer participating in this short stretch of dynamics. Note that the first PN to fire adds an excitatory input current to the second PN and encourages it to fire as well; the combined effects of these first two PNs adds a substantial excitatory presynaptic current to the third PN, causing it to fire less than a millisecond later. This entire cascade takes place over <3 ms, and is similar to the MFEs in our larger network.

Of course, the chain-reactions we've been describing don't just include PNs; the first few PNs to initiate a chain-reaction will also cause firing amongst the LNs, many of which also benefit from the high-gain state. How such a chain-reaction unfolds can be very complicated and situation-dependent; recall that the LN population is heterogeneous. LN1s inhibit LN2s; LN2s inhibit PNs. The short-time-scale dynamics within each MFE can be rather complicated, with PNs, LN1s and LN2s competing over the fate of the cascade.

This complexity is illustrated in **Figures 14B,C**. In **Figure 14B** we expand the 3-neuron network of **Figure 14A** to include two LN2s (see addition blue neurons on the right, as well as bluish trajectories on the left), which affect how the cascade unfolds. This time, the first PN again adds excitatory presynaptic current to the other two PNs, but also to the two LN2s; these LN2s manage to fire before the other PNs would fire, giving rise to inhibitory presynaptic currents which actually prevent these other PNs from firing altogether. Another example is shown in **Figure 14C**, where the simple network is further expanded to include two LN1s (see additional green

neurons on the right, as well as greenish trajectories on the left). This time the first PN to fire causes one of the LN1s to fire second, which actually inhibits and delays the spikes coming from the LN2s. As a result, the LN2s are not capable of completely curtailing the cascade, and one of the remaining PNs manages to fire (compare **Figure 14B** with **Figure 14C**).

Thus, the specifics of any given MFE are rather variable: it is possible for a chain-reaction of PN spikes to trigger LN2 spikes which halt the cascade or to trigger LN1 spikes which help the cascade continue via disinhibition. When each MFE concludes—usually due to a barrage of inhibition—the PNs involved experience an abundance of persistent inhibitory currents: both presynaptic and intrinsic. If the system is stimulated, a sufficiently strong feedforward input can override these inhibitory currents and cause further firing. On the other hand, when the network is unstimulated, these inhibitory currents are usually sufficient to prevent further firing.

## MFEs Strongly Affect the Dynamics of Our Model

As one can see from the description above, MFEs represent a particular kind of synchrony; they are most decidedly *causal* in nature, stemming from strong and fast competition between synaptic excitation and inhibition. In this regard, MFEs can be viewed as a more complicated version of the “sandpile” cascades discussed in Bak et al. (1987) and later considered in the context of neuroscience by Mirollo and Strogatz (1990), Gerstner and van Hemmen (1993), DeVille and Zheng (2014).