of IR reception, Schmitz and Bleckmann, 1998) it could be
possible that the sensitivity threshold of a *Melanophila* IR organ
is about thousand fold lower (analogous to the mammalian
cochlear amplifier) than the hitherto published lowest threshold
of 60 µW/cm².

CLOSING REMARKS

Among biological IR sensory systems it is a unique feature
of *Melanophila* beetles that IR sensilla serve as photon
receptors although they are innervated by mechanosensitive
neurons. The cuticular apparatus absorbs incoming IR radiation
and transforms photon energy into a micromechanical event
measured by a dedicated mechanoreceptor. In principle this
constellation provides the possibility of active amplification of
faint mechanical input signals. As mentioned in the Introduction
active amplification has been shown in the context of hearing: for
the hair cells in the cochlear amplifier in vertebrates (Hudspeth,
1997; LeMasurier and Gillespie, 2005; Fettiplace and Hackney,
2006; Ashmore et al., 2010) but also for the chordotonal organs
in the ears of certain flies (Göpfert and Robert, 2001; Göpfert
et al., 2005; Nadrowski et al., 2008) and a tree cricket (Mhatre
and Robert, 2013). In hearing, however, the energy required for
amplification is expended by the sensory cells themselves whereas
in the proposed active IR receptors in *Melanophila* beetles the
energy originates from the flight motor. Thus, in principle, the
proposed mechanism to achieve a high sensitivity in a receptor
for electromagnetic radiation is new. We further suggest that
three different mechanisms are involved: (i) as proposed active
signal amplification, (ii) active sensing which means that activity

of the sensor system already starts in anticipation of a stimulus
(Nelson and MacIver, 2006), and (iii) stochastic resonance:
noise—in this case self-generated—is used for better signal
detection (Harmer et al., 2002; Moss et al., 2004; McDonnell and
Abbott, 2009).

The proposed beat mechanism, however, shows some marked
differences compared to the mechanisms mentioned. No energy
used for target analysis is emitted into the surrounding
(difference to conventional active sensing), muscular energy is
used for signal amplification (fundamental difference to the
cellular molecular motors of the sensory cells in ears) and
the crucial part of the “noise” is produced by self-generated
oscillations. For the purpose of ultrasensitive stimulus detection
the probability of action potential generation can be adjusted
by altering the overall noise amplitude (difference to stochastic
resonance that only works at an optimal noise intensity, which
can hardly be influenced by the sensor system).

If the proposed high sensitivity of the IR organ could be
demonstrated, the biological IR sensor would advance into
the sensitivity gap currently existing between relatively cheap
uncooled thermal IR sensors and expensive cooled quantum
detectors requiring much more effort during operation and also
more costly service (see **Table 1**). Thus, the demonstration of the
postulated amplification mechanism would also be of technical
interest for the development of new active IR sensors.

ACKNOWLEDGMENTS

We are indebted to Horst Bleckmann for useful discussion and
improvements of the manuscript.

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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 Schneider, Schmitz and Schmitz. 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

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

Berthold G. Hedwig *

Department of Zoology, University of Cambridge, Cambridge, UK

Intraspecific acoustic communication requires filtering processes and feature detectors in the auditory pathway of the receiver for the recognition of species-specific signals. Insects like acoustically communicating crickets allow describing and analysing the mechanisms underlying auditory processing at the behavioral and neural level. Female crickets approach male calling song, their phonotactic behavior is tuned to the characteristic features of the song, such as the carrier frequency and the temporal pattern of sound pulses. Data from behavioral experiments and from neural recordings at different stages of processing in the auditory pathway lead to a concept of serially arranged filtering mechanisms. These encompass a filter for the carrier frequency at the level of the hearing organ, and the pulse duration through phasic onset responses of afferents and reciprocal inhibition of thoracic interneurons. Further, processing by a delay line and coincidence detector circuit in the brain leads to feature detecting neurons that specifically respond to the species-specific pulse rate, and match the characteristics of the phonotactic response. This same circuit may also control the response to the species-specific chirp pattern. Based on these serial filters and the feature detecting mechanism, female phonotactic behavior is shaped and tuned to the characteristic properties of male calling song.

**Keywords:** feature detection, calling song, onset activity, reciprocal inhibition, delay line, coincidence detector, post-inhibitory rebound, modulation

OPEN ACCESS

**Edited by:**

Hadley Wilson Horch,
Bowdoin College, USA

**Reviewed by:**

Jens Herberholz,
University of Maryland, USA
Ralf Heinrich,
University of Göttingen, Germany

*Correspondence:

Berthold G. Hedwig
bh202@cam.ac.uk

**Specialty section:**

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

**Received:** 15 September 2015

**Accepted:** 01 February 2016

**Published:** 25 February 2016

**Citation:**

Hedwig BG (2016) Sequential Filtering Processes Shape Feature Detection in Crickets: A Framework for Song Pattern Recognition. *Front. Physiol.* 7:46.
doi: 10.3389/fphys.2016.00046

## INTRODUCTION

In many species of insects, intraspecific signaling systems have evolved to allow mate attraction over long distances, including systems based on sex pheromones in moths and butterflies (Jacobsen, 1972), light patterns in fireflies (Carlson and Copeland, 1985; Lewis and Cratsley, 2008) and acoustic signals in orthoptera and hemiptera (Busnel, 1963; Alexander, 1967; Hedwig, 2014). These specialized communication systems are shaped by evolution so that both the signal generation and recognition processes are selective to a species-specific pattern. As intraspecific communication is crucial for the animals' mating success it requires reliable performance at the sender and the receiver side. The species-specific signals emitted by a sender require matched detection and recognition mechanisms by the receiver. Signal generation and signal recognition processes in insects are implemented in rather simple nervous systems and therefore provide a chance to unravel the underlying neural mechanisms at a cellular level.
The acoustic behavior of crickets is an established model system to analyse the neurobiological basis of auditory processing. Females approach singing males by phonotaxis, using only acoustic cues for pattern recognition and subsequent orientation. Considerable research in this field is aimed to understand how the temporal pattern of male calling song is recognized by the female nervous system (Popov et al., 1974; Hoy, 1978; Huber, 1978). As outlined in different hypothesis (review by Kostarakos and Hedwig, 2015), for pattern recognition to occur single neurons or networks of neurons should selectively respond to the species-specific characteristics of a signaling pattern. These are known as “feature detector” neurons or networks (Bullock, 1961; Hoy, 1978). Revealing the cellular and network mechanisms that lead to the selectivity of these neurons provides the opportunity to understand how a sensory system has been shaped during evolution to specifically process behaviorally relevant stimuli (Konishi, 1991).

In crickets, phonotaxis toward a species-specific calling song requires that its salient features are reliably detected, processed, and transformed into an appropriate motor response by the nervous system. Here a framework is outlined that calling song pattern recognition is organized in a set of serial filters, with each filter selectively responding to a particular characteristic of the song. At each level of auditory processing, a different feature of the calling song is extracted from the overall original signal leading eventually to the very specific activity of feature-detecting neurons in the brain.

This outline for song pattern recognition is mainly based on data in the sister species of *G. bimaculatus* and *G. campestris*, which have similar sound patterns and auditory preferences (Thorson et al., 1982). It however should provide a framework for different species of crickets as well as for the processing of communication signals in other specialized sensory pathways. Note, that data regarding auditory thresholds and tuning may slightly vary in papers cited, as different experimental procedures and recording methods were used.

## THE MALE CALLING SONG AND THE FEMALE AUDITORY CHALLENGE

Only male crickets sing, which they achieve by the rhythmic opening and closing of their elevated front wings, with sound generated only on the closing movements. Males of different species produce different species-specific patterns of sound pulses in the contexts of mate attraction, courtship and rivalry behavior (Alexander, 1962; Otte, 1992). During calling song in *Gryllus bimaculatus* (*Figures 1A,B*), sound pulses are 15–20 ms long, separated by 15–20 ms silent intervals; and grouped into chirps of 3–5 pulses, which are repeated at a rate of 3–4 chirps/s (Doherty, 1985). Sound pulses rise to a maximum intensity of about 100 dB Sound Pressure Level (SPL) within a few milliseconds, and have a carrier frequency of around 4.8 kHz. Thus, the typical calling song of *G. bimaculatus* is characterized by four features: carrier frequency, pulse duration,

pulse repetition rate, and chirp structure, which is given by the number of pulses per chirp and the interchirp interval. In male-male interactions, variable short rivalry songs are generated with chirps comprising 6–12 pulses. When courting a female, males generate single sound pulses at the chirp rate of the calling song, but with carrier frequencies of 11–16 kHz (Libersat et al., 1994). As compared to the more episodic courtship and rivalry song, which are accompanied by other sensory signals, e.g., antennal contact, and emitted in close male-female and male-male encounters, the stereotypic calling song is a long distance communication signal which may be emitted continuously for many hours to attract females.

Sexually-receptive females walk or fly toward a singing male, using only the male's acoustic cues as guidance for their phonotactic orientation. The tuning of their phonotactic behavior matches the temporal pattern of the male calling song (Figure 1C). They prefer pulse patterns similar to calling song, and are not attracted by short pulses repeated at a high repetition rate or by long pulses repeated at a lower rate (Thorson et al., 1982; Hedwig, 2006). Female *G. bimaculatus* and *G. campestris* therefore show a band-pass tuning of their phonotactic behavior based on pulse duration and pulse interval. Sound pulses also need to be at the species' typical carrier frequency to be attractive.

<figure><img src="image_183.png" /><figcaption>FIGURE 1 | Sound pattern of a male *G. bimaculatus* calling song in the temporal (A) and frequency domain (B). Chirps with four sound pulses are repeated at a rate of 3 per second. The main carrier frequency of the sound is around 4.8 kHz, with corresponding higher harmonics. (C) Tuning of female phonotactic behavior toward different temporal patterns of sound pulses as revealed in trackball experiments (Hedwig and Poulet, 2005). Test patterns of chirps with different pulse durations and intervals are vertically arranged. Each pattern was presented for 30 s from the left and right hand side while the cricket's phonotactic steering toward the stimulus was recorded.</figcaption></figure>
# SERIAL FILTER PROCESSES UNDERLYING CALLING SONG RECOGNITION

Current data indicate that the properties of the peripheral and central auditory pathway are specifically adapted to process the male calling song. This process is organized in a set of serially arranged filter mechanisms (Figure 2) that finally leads to a highly selective response of feature detecting brain neurons and the tuned phonotactic behavior. First, at the level of the hearing organ a peripheral filter is selective for the song carrier frequency; second is a neural filter mechanism of phasic afferent and interneuronal activity which enhances the response to the onset of sound pulses; and third is a neural network in the brain with a delay line and coincidence detector feeding into feature detecting neurons which are tuned to a specific pulse repetition rate. The detection of the species-specific pulse rate may also control the phonotactic steering responses to the chirp pattern. All these filters together contribute to and shape the band-pass tuning of the cricket phonotactic orientation behavior.

## Processing of the Calling Song Carrier Frequency

In all auditory systems, frequency processing starts at the biophysical level. Due to the mechanical filtering properties of the peripheral transduction mechanism, frequency components are separated and forwarded to spatially distinct structures of the hearing organ in a frequency-specific way as revealed in the ears of moths, locusts, and cicada (Windmill et al., 2005, 2007; Sueur et al., 2006). Oscillations of these structures then drive the activity of afferent neurons in the hearing organs. In the field cricket *G. bimaculatus* the carrier frequencies of calling songs cover a range of 4.3–5.2 kHz (Kostarakos et al., 2009), and courtship songs are in the range of 11–16 kHz (Libersat et al., 1994). The biophysics of the peripheral auditory system allows for selective responses at the level of the auditory afferents to these low and high frequency components of the communication signals (Oldfield et al., 1986). The afferent activity is then carried forward to the central nervous system where it sets the limits for the subsequent frequency tuning of central interneurons, and finally the categorical phonotactic responses (Wyttenbach et al., 1996; Figures 2, 3).

## The Peripheral Auditory System: Tuning of Tympanic Membrane Vibrations

The peripheral auditory system in crickets is characterized by a small frontal and a large posterior tympanic membrane, which are located on the tibia of each front leg. The hearing organ is positioned behind the posterior tympanum where a row of 40–60 auditory afferents is arranged in a structure known as the crista acustica. The organ is attached to the auditory trachea (Michel, 1974), which extends from the front tibia to the first thoracic segment where it ends with a lateral opening at the auditory spiracle (Nocke, 1972; Huber and Thorson, 1985). Sound enters the auditory system via the spiracles of the auditory trachea, and also via the posterior tympanic membrane in the tibia. For directional coding, the efficiency of the different sound pathways depends on the carrier frequency and the angle of incidence (Michelsen et al., 1994; Seagraves and Hedwig, 2014). The peripheral auditory pathway also provides the essential step of frequency filtering. Movements of the posterior tympanal membrane are necessary for hearing in crickets (Kleindienst et al., 1983) and mirror the frequency tuning of the auditory system. Laser vibrometry measurements of the mechanical oscillations of the posterior tympanic membrane in *G. bimaculatus* (Larsen, 1981) revealed the best response at 5.3 kHz (Figure 3A); the velocity response drops toward 2 kHz and decreases toward 14 kHz. Like in other species of crickets (Johnstone et al., 1970; Paton et al., 1977), these data indicate that the mechanical response of the peripheral auditory system matches the calling song carrier frequency (Figure 1B). Since these early measurements, the oscillation properties of tympanic membranes in field cricket have not been studied any further; using more recent laser technology refined tuning curves may be recorded or even active hearing mechanisms like those in tree crickets (Mhatre and Robert, 2013) may be revealed.

## Frequency Tuning of Auditory Afferents

The biophysical and neurophysiological basis for frequency tuning of the auditory afferents are not yet resolved in detail. They may depend on the opening state of the spiracles (Kostarakos et al., 2009), the properties of the tracheal tubes, and also on intrinsic properties of the sensory neurons. The 40–60 afferent neurons are linearly arranged over a distance of 300 µm in the tonotopically organized crista acustica, in which sensory

<figure><img src="image_184.png" /></figure>

**FIGURE 2** | Schematic diagram of serial filter processes in the cricket auditory pathway. At each stage, the response specificity is enhanced. Left to right: frequency tuning of the tympanic membrane response is based on biophysical filter mechanisms in the hearing organ; the detection of sound pulses is supported by the phasic-tonic response properties of auditory afferents and by reciprocal inhibition at the level of thoracic interneurons; the pulse rate is processed in the brain by a delay line coincidence detecting circuit, driving feature detector neurons that preferentially respond to the species-specific pulse rate. In addition, the feature detection circuit may modulate a reactive non-selective steering pathway at the time scale of the chirp rate (not shown). Finally, phonotactic walking behavior is tuned to sound pulses of the calling song carrier frequency presented at the species-specific pulse duration and pulse interval.
<figure><img src="image_185.png" /><figcaption>**FIGURE 3** | Frequency tuning at different processing stages in the auditory pathway of field crickets. (A) Vibration velocity amplitude of the posterior tympanic membrane in *G. bimaculatus* as measured with laser vibrometry at a constant sound amplitude of 94 dB SPL while acoustic spiracles were blocked. Redrawn from Larsen (1981). Inset shows the posterior tympanal membrane in the tibia. (B) Frequency tuning of the auditory organ. Threshold of the summed activity of the tympanal nerve in *G. campestris*. Redrawn on a linear frequency scale, after Nocke (1972). Inset shows the recording site of the auditory nerve in the femur. (C) Threshold for frequency tuning of AN1 spike activity in *G. bimaculatus*. Data pooled and redrawn from Rheinlaender et al. (1976); Schildberger et al. (1989), and Horseman and Huber (1994). Inset shows structure of AN1. (D) Frequency tuning of phonotactic behavior in *G. bimaculatus* based on auditory steering responses of tethered females walking on a trackball system. Calling song pattern presented at 75 dB SPL with varying carrier frequency. Data based on seven tests. Inset shows sound pattern and cricket on a trackball.</figcaption></figure>

neurons responding to low frequencies are located proximally and neurons responding to high frequencies are located distally (Oldfield et al., 1986). The sensory neurons are positioned right on the surface of the anterior branch of the auditory trachea while their dendrites project into a attachment cells of systematically varying size, which are linked to the lateral cuticle of the tibia (Michel, 1974). The auditory sensory neurons

may be activated in a frequency-specific way by sound-induced traveling waves in the auditory trachea, mechanically stimulating the dendrites and opening mechanically gated ion channels. Frequency-specific traveling waves within the auditory trachea of bushcrickets have recently been described (Montealegre-Z et al., 2012; Udayashankar et al., 2012); the waves preferably elicit oscillations of the auditory trachea in a tonotopically arranged gradient along the crista acustica and appear to establish the tuning of the auditory afferents.

At the afferent population level, the summed activity of the auditory nerve in *G. campestris*, shows the lowest threshold for hearing to be around 50 dB SPL for sound pulses of 4.0-4.5 kHz, i.e., in the range of male calling song (Figure 3B; Nocke, 1972). The hearing threshold sharply increases toward 2 kHz, but toward higher frequencies, a secondary broad-threshold minimum at 65 dB SPL occurs for sound of 7-9 kHz. The system becomes increasingly less sensitive toward 10-12 kHz, but sensitivity subsequently increases, with the threshold dropping to 60 dB SPL at 14 kHz. Overall, in the range of 4-6 kHz the threshold curve corresponds well with the velocity response of the tympanic membrane (Figure 3A). The summed activity of the auditory nerve comprises the response of many auditory afferents, each of which has a lowest threshold of around 45 dB SPL (Esch et al., 1980; Oldfield et al., 1986). The tuning of the individual auditory afferents shows a discontinuous distribution of best frequencies, with about 75% of afferents responding to the carrier frequency of male calling song (Zaretsky and Eibl, 1978; Esch et al., 1980; Imaizumi and Pollack, 1999), and with the remaining to high frequencies that represent the male courtship song with dominant frequencies of 11-16 kHz (Libersat et al., 1994) and ultrasound sonar calls of echolocating bats above 20 kHz. This discontinuous distribution of best frequencies may reflect the frequencies of the most behaviorally relevant sounds for females, as they must respond with positive phonotaxis to the signals of conspecific males and with negative phonotaxis to the calls of predatory bats (Wyttenbach et al., 1996; Imaizumi and Pollack, 1999).

The activity of the auditory afferents is carried toward the prothoracic ganglion where their axons terminate in the anterior ventral neuropil (Eibl and Huber, 1979; Wohlers and Huber, 1985). Axonal arborizations are tonotopically arranged with afferents tuned to calling song projecting more medially, and afferents tuned to sounds of higher frequencies projecting more laterally (Imaizumi and Pollack, 2005). In *Teleogryllus oceanicus*, and likely also in *G. bimaculatus*, the bifurcating axons of afferents tuned to calling song project more posteriorly. They may connect to descending interneurons like the DN1 neurons, which forward signals in the frequency range of the calling song to the posterior thoracic ganglia (Esch et al., 1980; Wohlers and Huber, 1982; Imaizumi and Pollack, 2005). Details of auditory processing in these ganglia are, however still not well analyzed.

## Tuning of Thoracic Interneurons

In the prothoracic ganglion, afferents make synaptic contact to bilateral pairs of local (ON1, ON2), descending (DN1), ascending (AN1, AN2), and T-shaped (TN1) auditory interneurons (Wohlers and Huber, 1982; Imaizumi and Pollack, 2005). The
<figure><img src="image_186.png" /><figcaption>**FIGURE 4** | (A) Arrangement of auditory afferents (yellow) and the mirror image ON1 neurons in the prothoracic ganglion, redrawn from Wohlers and Huber (1985). (B) Phasic-tonic response of the auditory afferents as revealed by a summed recording of the tympanal nerve in the distal femur. Stimulation with a 1000 ms pulse demonstrates the phasic synchronized onset response of the auditory afferents, which adapts to a tonic activity level. The phasic response is clearly revealed by averaging the rectified neural signal. (C) Acoustic stimulation with the species-specific pulse pattern shows the phasic onset and representation of the pulse pattern in the summed afferent activity. Modified from Nabatiyan et al. (2003).</figcaption></figure>

local omega shaped ON1 neurons (see Figure 4A) respond most strongly to the carrier frequency of the calling song, but also respond to the high frequency components of courtship songs or bat calls (Marsat and Pollack, 2004). The two pairs of bilateral ascending interneurons forward information from the thoracic auditory neuropil toward the brain. AN1 (Figure 3C) is tuned to the male calling song (Rheinlaender et al., 1976; Schildberger et al., 1989; Horseman and Huber, 1994). It has its lowest threshold

of about 43 dB SPL at around 5 kHz, and matches the best tuning of both the posterior tympanal membrane and the auditory nerve. The threshold of AN1 increases sharply to 80 dB SPL from 2 to 5 kHz and increases gradually to 80 dB SPL from 5 to 12 kHz (Figure 3C). AN1 is the only neuron that carries information about the calling song toward the brain. Schildberger and Hörner (1988) provided an experimental proof for the close link of AN1 activity and phonotaxis. Manipulation of the AN1 spike activity by intracellular current injection in phonotactic walking crickets changed the female's walking direction and performance. AN2 and TN1, the only other interneurons with an ascending axon, are tuned to high frequencies and do not reliably copy the calling song pattern (Wohlers and Huber, 1982). The tuning to high frequency sounds may relate to courtship song or the calls of bats (Libersat et al., 1994). Therefore, high frequency signals alone are not sufficient to reliably indicate a courting male or an echolocating bat.

## Frequency Tuning of the Phonotactic Behavior in *G. bimaculatus*

The frequency tuning of the auditory pathway closely corresponds to the frequency tuning of female phonotactic behavior (Figure 3D). If female *G. bimaculatus* crickets walking on a trackball (Hedwig and Poulet, 2005) are exposed to 75 dB SPL calling song patterns with systematic alteration of the carrier frequency, phonotactic steering toward the sound source is strongest at 4.5–5.0 kHz. From this maximum response, phonotactic behavior decreases sharply toward 3 kHz and becomes gradually weaker toward 12 kHz. There is a good match between the frequency tuning of phonotactic behavior and the tuning of AN1, which was also established by direct comparison of AN1 activity and lateral steering (Kostarakos et al., 2008). A similar match has also been indicated for the carrier frequency and the tuning of the AN1 interneuron in *Teleogryllus commodus* (Hill, 1974).

## Neural Representation of Sound Pulses

Patterns of sound pulses and silent intervals are a characteristic element of cricket songs. Sensory processing at the level of auditory afferents and first order interneurons may therefore be adapted to respond specifically to the temporal structure of the sounds and to represent it in patterns of neural activity.

### Phasic-Tonic Responses of Auditory Afferents

The primary auditory neurons are scolopidial mechanoreceptors (Michel, 1974), and show phasic-tonic response characteristics (Nocke, 1972; Oldfield et al., 1986; Nabatiyan et al., 2003). They project into the prothoracic ganglion and activate first order interneurons like ON1 (Figure 4A). When stimulated with a 1000 ms sound pulse at the carrier frequency of calling song (Figure 4B), summed recordings from the auditory nerve show a salient response of the afferents to the onset of the pulse. It is best revealed by averaging the rectified (i.e., the negative signal components have been made positive) nerve recording; this procedure preserves the tonic activity component, which otherwise is lost when the signal is directly processed. The phasic onset of the auditory nerve is in the range of twice the
<figure><img src="image_187.png" /><figcaption>**FIGURE 5** | (A) Diagram of the reciprocal inhibitory connection between ON1 neurons. (B) Intracellularly recorded activity of an ON1 neuron in response to a 1000 ms acoustic stimulus presented simultaneously to both ears. Averaging the instantaneous spike rate of ON1 reveals a pronounced phasic onset response to the pulse, followed by a reduced spike rate after the onset response (arrow) with a subsequent tonic activity level. (C) During acoustic stimulation with the species-specific pulse pattern, ON1 activity mirrors the temporal pattern of the signal in its spike activity and the instantaneous spike rate.</figcaption></figure>

amplitude of the subsequent tonic response. The onset response rapidly decays within 10–20 ms and then gradually to the lower level of the tonic response. Intracellular recordings from single auditory afferents showed a high spike-rate activity at the sound onset (Oldfield et al., 1986). During stimulation with a repetitive pulse pattern corresponding to the *G. bimaculatus* calling song (Figure 4C), the phasic component of the afferent activity reliably encodes the sound pulses, generating a peak response at the beginning of each pulse, even for pulse repetition rates higher than the pulse rate of the calling song (Nabatiyan et al., 2003).

Thus, the phasic-tonic response properties of the population of auditory afferents allow the temporal structure of the pulse pattern to be forwarded reliably to the central nervous system. Based on the spike response of single afferents in *T. oceanicus*, Marsat and Pollack (2004) calculated the information transfer rates as bits/s transmitted by the spike patterns at different amplitude modulation frequencies. The information transfer rate of the afferents broadly represented a spectrum of amplitude-modulated sounds up to 150 Hz. Therefore, the response range of the afferents is not specifically tuned to the species-specific pulse pattern of the calling song; the filtering for the temporal pattern rather must be achieved in the central nervous system.

## Sharpening Sound Onset Responses by Reciprocal Inhibition in Thoracic ON1 Neurons

The time course of the afferent response is mirrored in the response pattern of the local ON1 neurons (Figures 4A, 5A), a bilateral pair of first order interneurons (Casaday and Hoy, 1977; Popov et al., 1978; Wohlers and Huber, 1978, 1982). Each ON1 neuron receives synaptic input from the auditory afferents of the ear ipsilateral to its dendritic field while its axon projects to the contralateral side. As the auditory phasic onset response sums across all afferents tuned to the calling song (Ronacher and Römer, 1985; Pollack and Faulkes, 1998), the onset of sound pulses also leads to a pronounced phasic response in these interneurons.

The bilateral pair of ON1 neurons is coupled by reciprocal inhibition (Figure 5A; Selverston et al., 1985), which has been suggested to contribute to temporal filtering of the species-specific pulse pattern (Wiese and Eilts, 1985; Wiese and Eilts-Grimm, 1985) and may play an important role for their auditory response properties. When a 1000 ms acoustic stimulus at 75 dB SPL and 4.8 kHz is presented from the anterior, the ON1 neurons generate a transient onset response with a burst of spikes reaching instantaneous spike rates in excess of 300 AP/s. Thereafter, they rapidly stabilize to a tonic spike rate of about 150 AP/s (Figure 5B). At first sight, the time course of this response appears to be similar to the summed afferent response (Figure 4B). However, immediately following the phasic onset response of ON1, a pronounced drop in spike rate occurs, by about 50 AP/s (arrow Figure 5B), which transiently reduces the neuron's activity even below the subsequent level of tonic activity. This transient drop therefore enhances/sharps the phasic onset activity relative to the tonic response. The fast drop in ON1 spike activity is not typical for the decline of a phasic response and it is not expected by the time course of the afferent activity. This peculiar feature may rather indicate that the neural representation of the onset of sound pulses becomes more salient at the level of ON1 neurons due to their reciprocal inhibitory connection. Upon simultaneous acoustic stimulation of both ears each ON1 neuron will be driven by afferent activity and also by the inhibition from the contralateral ON1. Due to synaptic delay and conduction time between the neurons the inhibition reaches an ON1 just after its initial peak spiking response (Selverston et al., 1985; Wiese and Eilts, 1985; see also Römer et al., 1981 for similar processing in locusts). Without substantially changing its phasic onset response, the
reciprocal inhibition will have its greatest impact immediately after the onset activity and then during the tonic activity. The reciprocal inhibition, if strong enough, thereby can enhance the representation of pulse-like acoustic signals in ON1. This can be demonstrated directly: the onset response of an ON1 to sound becomes less pronounced when the contralateral hearing organ is removed and the contralateral inhibition abolished. Recording ON1, while presenting calling song like pulse patterns, reveals that the phasic onset response reliably mirrors each sound pulse with a burst of spikes (Figure 5C). Thus, following the phasic onset response of the auditory afferents, which drives the excitation of ON1, the reciprocal inhibition between the ON1 neurons can act as a mechanism that further sharpens the onset response to sound, and thereby provides an additional way to represent sequences of short sound pulses.

## Selective Attention to the Louder Signal

Another mechanism on a slower time scale that supports reliable coding of sound pulses is described as “selective attention” (Pollack, 1988). Continuous repetitive acoustic stimulation elicits spike activity in ON1, which causes a gradual increase in its cytosolic calcium concentration and subsequently triggers a hyperpolarizing potassium current (Sobel and Tank, 1994; Baden and Hedwig, 2007). This leads to a suppression of the ON1 neuron’s spike response to low amplitude sound pulses e.g., 60 dB SPL, when they are interspersed with a louder signal of 80 dB SPL (Pollack, 1988). Due to the build-up of hyperpolarization, the response to the low intensity sound signal gradually becomes subthreshold and the ON1 spike pattern is dominated by its response to the louder signal. In a non-competitive situation with only one signal source, the mechanisms will suppress any non-specific background noise and will enhance the neural representation of the sound pattern. In a situation of competing signalers the selective attention mechanism will ensure that when a female approaches a singing male the signal from this loudest/nearest male will dominate the spike pattern of its central auditory pathway. Behavioral experiments (Simmons, 1988; Harrison et al., 2013) indicate that females orient preferentially to the calls of louder males.

## The Detection of Pulse Periods by a Delay Line and Coincidence Detector Circuit in the Brain

As the simple opening and closing movements of the wings underlying sound production do not allow for a complex amplitude modulation, it is the temporal pattern of the signals that convey the male cricket's message, similar to the pulses in Morse code. Detecting the specific temporal sequence of sound pulses, i.e., the pulse period and the chirp pattern, is not achieved at the level of the auditory afferents, and so requires more complex processing in the central nervous system. Only the bilateral pair of AN1 interneurons forwards auditory signals in the range of calling song to the brain; another pair of neurons (AN2) responds to high frequency signals. AN1 is not tuned to the temporal pattern of the calling song (Wohlers and Huber, 1982; Schildberger, 1984b) and reliably responds to different temporal patterns of sound pulses, although, the spike rate of

the response decreases at high pulse repetition rates. Therefore, besides some pre-filtering that occurs at the thoracic level, the final processing and selective detection of the species-specific pulse rate must occur in the brain. Furthermore, crickets in which the connectives to the brain have been severed do not show any positive or negative phonotactic responses (Pollack and Hoy, 1981).

## Circuit Structure

Several different mechanisms have been proposed to underlie the processing of the species-specific pulse rates, such as internal templates, band-pass filtering, resonant networks, and delay line coincidence detection (Hoy, 1978; Schildberger, 1984b; Weber and Thorson, 1989; Bush and Schul, 2006; Kostarakos and Hedwig, 2015). Current data provide strong support for a circuit comprising a delay line and a coincidence detector (Figure 6A; Schöneich et al., 2015), a similar circuit design was originally proposed for directional auditory processing (Jeffress, 1948) and outlined as a concept of resonant networks by Reiss (1964). Corresponding to the delay line coincidence detection concept the response to sound pulses is split into two parallel pathways. The activity in one pathway is directly forwarded to the coincidence detector whereas the activity in the parallel pathway is delayed by the species-specific pulse period before reaching the detector. Consequently, a single sound pulse will only weakly activate the coincidence detector, but when the pulse-interval of the stimulus pattern corresponds to the internal delay, the direct input, and the delayed input from the previous pulse coincide and the response of the detector will be significantly enhanced. The delay in the cricket brain cannot be achieved by axonal delay-lines as proposed for binaural processing by Jeffress (1948) and which in owls allow only microsecond delays (Carr, 1993). As processing of communication signals in the cricket brain requires delays of about 40 ms the delay rather needs to be based on an inhibitory mechanism.

The axonal projections of AN1 (Figures 3C, 6B) terminate in the frontal protocerebrum and form a ring-like arborization. A set of four local auditory interneurons (LN2-LN5) closely match this arborization pattern and form a similarly-shaped ring-like auditory neuropil in the brain (Figure 6B; Kostarakos and Hedwig, 2012); the structure of LN5 (Schöneich et al., 2015) is similar to LN2. Like AN1, these local neurons are also tuned to the carrier frequency of calling song (Schöneich et al., 2015). The response properties of these neurons together constitute a delay line coincidence detection circuit as outlined in Figure 6C. This conclusion is supported by increasing latencies for auditory processing in the circuit and very specific synaptic responses of the neurons (Schöneich et al., 2015). Together these indicate one particular flow of activity in the circuit and allow only one most parsimonious interpretation for the function of the local circuitry (Figures 6D,E), which matches a previous hypothesis on pattern recognition (Weber and Thorson, 1989).

## Functional Properties of the Delay Line Coincidence Detector Circuit

A delay line coincidence detector requires two parallel pathways; a direct pathway and a delayed pathway. In the cricket brain,
<figure><img src="image_188.png" /><figcaption>**FIGURE 6** | Feature detection in the cricket brain. (A) Delay line coincidence detector circuitry proposed for feature detection of the species-specific pulse rate, after Weber and Thorson (1989). (B) Axonal arborizations of AN1 and local auditory brain neurons LN2–LN4 in the anterior protocerebrum, neurons are labeled as “B-L1” neurons in Kostarakos and Hedwig (2012). (C) Proposed feature detecting circuitry in the brain with the ascending neuron AN1, the delay line neurons LN2 and LN5, the coincidence detecting neuron LN3 and the feature detector neuron LN4. The coincidence detector neuron integrates activity directly forwarded from AN1 and delayed activity forwarded via the delay line with the non-spiking neuron LN5. (D) In response to a single sound pulse, AN1 generates a burst of spikes, which directly drives the activity of the coincidence detector neuron LN3. AN1 also activates the delay line via LN2, which tightly follows the activity of AN1, and subsequently inhibits the non-spiking interneuron LN5. At the end of a sound pulse, when released from inhibition, LN5 generates a post-inhibitory rebound, which reaches its maximum amplitude after about 40 ms and elicits a delayed gradual depolarization in the coincidence detector neuron LN3 (blue arrow). In response to the first sound pulse, the response of the feature detector neuron LN4 is dominated by the inhibition via LN2, and shows minor excitation via LN3. (E) In response to a second sound pulse presented at the species-specific pulse interval, the coincidence detector integrates the delayed response from LN5 (blue arrow) and the direct response from AN1. The second response of the coincidence detector LN3 is boosted, now its excitatory input to LN4 overcomes the inhibition, causing the feature detector neuron to spike (arrow). (A,C–E) from Schöneich et al. (2015); (B) modified after Kostarakos and Hedwig (2012).</figcaption></figure>

the direct pathway is based on the connection between AN1 and the coincidence detector neuron, LN3 (Figure 6C). The delayed pathway appears to be set up via two neurons, an inhibitory neuron, LN2, which closely follows the activity of AN1, and a non-spiking interneuron, LN5 (Schöneich et al., 2015), which is inhibited for the 20 ms duration of a single sound pulse. At the end of a sound pulse, and so release of inhibition

from LN2, neuron LN5 generates an excitatory post-inhibitory rebound response which reaches its maximum about 40 ms after the end of the sound pulse, corresponding to the duration of the pulse period (Figure 6D). This post-inhibitory rebound response can also be induced experimentally by applying a hyperpolarizing current pulse, and is produced upon the offset of the current pulse, i.e., when the hyperpolarization is removed. The post-inhibitory rebound has the same amplitude for stimulus intensities in the range of 50–80 dB SPL and thus provides a mechanism for intensity-independent auditory processing, which is a fundamental property of pattern recognition processes. The rebound response is also independent of stimulus duration, in the range of 10 to about 50 ms. For a delay line coincidence detector network, a coincidence detecting neuron should only respond when the direct and delayed pathways coincide, and one of the local brain neurons, LN3 exhibits response properties characteristic of a coincidence detector. Intracellular recordings of its synaptic activity indicate that the direct input to this neuron is provided by AN1, and the delayed input, based on the post-inhibitory rebound by the non-spiking interneuron, LN5. The response of LN3 to a single sound pulse is low (Figure 6D). However, if two pulses are presented at the species-specific pulse period of about 40 ms, its synaptic input and spike activity considerably increase, by a factor of 2.3 (Figure 6E). At lower pulse rates the direct and the delayed excitation to the coincidence detector are out of sync, and at higher pulse rates AN1 spike activity does not properly represent the sound pulses (see Schöneich et al., 2015 for details). Therefore, from the properties of this network, the neural circuitry responds best to the species-specific pulse rate (Figure 6E). The final element in this circuitry is the LN4 neuron. Its spike response to single sound pulses is subthreshold (Figure 6D) but it responds with 1–2 spikes if a second pulse arrives at the right interval (Figure 6E). This neuron integrates excitatory and inhibitory inputs, and its tuning toward different temporal patterns becomes more specific than the response of the coincidence detector neuron. This is due to the inhibition that suppresses spiking responses toward single sound pulses, and allows only sound pulses with the right interval to elicit spikes. Therefore, the LN4 is selectively only activated by the species-specific pulse pattern and acts like a feature detector for calling song. The neuron shows a band-pass tuning curve in its spike activity that very closely matches the tuning of female phonotactic behavior (Kostarakos and Hedwig, 2012). The evidence demonstrates that processing of the pulse rate occurs within the local network of ring-like brain neurons, which form a close association with the arborizations of AN1. This neural network may therefore represent the filter mechanism or feature detector circuit for the pulse pattern of calling song in crickets like G. bimaculatus. The auditory activity of other neurons in the brain with band-pass tuning curves similar to LN4 may be a consequence of this early processing mechanism (Schildberger, 1984b; Zorović and Hedwig, 2011).

Interestingly, the overall auditory response within the circuit i.e., the number of spikes elicited per chirp, decreases at different levels of processing from AN1 to LN4 by about 90% (Kostarakos and Hedwig, 2012). This points toward sparse coding of the stimulus pattern (Olshausen and Field, 2004), which shifts the
representation of the stimulus features from a temporal code to a neuron-specific place code. Sparse coding appears to be an efficient way for simple nervous systems to ensure a robust representation of stimulus patterns.

## PROCESSING AT THE CHIRP LEVEL: INSIGHTS FROM PATTERN RECOGNITION AND AUDITORY STEERING

In addition to the pulse pattern of calling song, in many species of crickets (Alexander, 1962; Otte, 1992) sound pulses are grouped into chirps, which in *G. bimaculatus* are repeated at a rate of 3-4/s. In phonotactic experiments, females tolerate a range of chirp periods and respond even when chirps are presented only at a rate of 1/s (Doherty, 1985). The chirp pattern may require an additional filter mechanism on a longer time scale than the pulse repetition rate (Grobe et al., 2012). Some insights into possible mechanisms of processing at the chirp time scale can be derived from female phonotactic steering responses. When exposed to an attractive calling song signal presented from above, female crickets will have no directional cue and cannot orient toward the sound source. However, when a non-attractive sound pattern is additionally interleaved and presented from the side, a female will steer toward the non-attractive pattern. This indicates that steering is under control of the pattern recognition process and that pattern recognition and phonotactic steering are organized in a serial manner (Doherty, 1991); a pattern apparently has been recognized before the steering process is permitted.

More details can be revealed with a trackball system that measures the fast steering responses during phonotaxis. Female *G. bimaculatus* will not orient to non-attractive sounds like chirps with long sound pulses or oval-shaped amplitude-modulated sound signals presented at the natural chirp rate, whereas they readily respond toward the species-specific pulse pattern (Figures 7A,B). The females however, do steer to non-attractive chirps when these are interspersed into an ongoing calling song (Poulet and Hedwig, 2005), or in some animals even when presented just after single normal chirps, interspersed into a sequence of non-attractive chirps (Figure 7C). The readiness to orient toward non-attractive chirps gradually decays over several seconds after listening to a sequence of calling song (Poulet and Hedwig, 2005). This steering response to non-attractive patterns indicates that a modulation process on a longer time scale is initiated which modulates the auditory motor response when the species-specific pattern is processed.

Once female *G. bimaculatus* have been exposed to the calling song, they no longer evaluate the complete temporal pattern of chirps during phonotaxis, but instead steer to the first sound pulse of a chirp. They even rapidly orient toward individual sound pulses, when these are presented in a split-song paradigm, with alternating pulses on the left and the right hand side of the animals' length axis; the steering responses occur with a latency of only 55-60 ms (Hedwig and Poulet, 2004, 2005). As the recognition for the pulse rate requires at least two sound pulses (see above) the pattern recognition network can not directly provide the commands for phonotactic steering. The

sparse coding at the level of the feature detector neuron LN4 (Kostarakos and Hedwig, 2012; Schöneich et al., 2015) further makes it difficult to envisage how its spike pattern might drive rapid auditory steering responses. Steering may rather involve a form of low level reactive processing which however is controlled and modulated on a longer time scale by the pattern recognition process in the brain (Poulet and Hedwig, 2005). The time-scale of this modulatory process is sufficient to explain phonotactic processing at the chirp level and could be the basis for trade-off phenomena of different song parameters as observed before (Stout et al., 1983; Doherty, 1985). A modulatory effect on phonotactic steering will be useful under natural conditions and will allow females to pursue their phonotactic approach to a calling male, even when the signal is temporally degraded due to diffraction or obstacles.

A specific neural circuitry for temporal filtering on the time scale of the chirp rate may not be required; processing at the chirp level may rather emerge from the modulatory properties of the pulse processing network. The upper limit for tolerated chirp periods could be set by the time constant of the gradually decreasing modulation effect and its lower limit may be reached when the pulse rate filter becomes ineffective, as very short chirp intervals will lead to an adaptation of the network and prevent its recovery. Whether the modulatory effect occurs within the thoracic ganglia or within the brain is unknown. The more posterior projection pattern of auditory afferents tuned

<figure><img src="image_189.png" /></figure>
to the calling song may point toward a thoracic pathway that nonetheless will be under descending control from the pattern recognition process in the brain. Processing at the level of the thoracic ganglia could provide an advantage because the auditory signals for steering could be directly forwarded to the walking motor control system with a short latency, avoiding a long loop via the brain.

## DISCUSSION: FRAMEWORK AND OPEN QUESTIONS

“Deciphering the brain’s codes” (Konishi, 1991) is a central ongoing topic in neuroscience. In relation to sensory pattern recognition, ideas of a “single central integrator” (Barlow, 1961; Bullock, 1961), or “feature detectors” (Hoy, 1978) that represent complex sensory input at the highest level have been central, and have shaped our thinking and concepts (Martin, 1994). Experimental approaches aiming to identify such higher order feature detecting neurons and their response properties have fostered an understanding of the way that sensory systems operate when processing behaviorally relevant stimuli (Konishi, 1991). Even simple acoustic communication signals require a combination of sensory filters for a selective behavioral response. These sensory filters, such as for the amplitude, duration, or frequency of a signal, could be arranged in parallel, to finally feed into a feature detector similar to the combination-sensitive neurons in vertebrate auditory processing (Bullock, 1961; Rauschecker and Tian, 2006). Alternatively, pattern recognition may be broken down into a sequential process of autonomous stages (Barlow, 1961). The latter may be more specific, and adaptive in “simple” insect nervous systems, in which the capacity for neural processing is more restricted (Wehner, 1987). Auditory feature detection underlying cricket mate attraction points toward such a sequential solution. Otherwise, in the insect CNS and brain also multimodal neurons integrate information from different sensory pathways (Pearson et al., 1980; Schildberger, 1984a) a process which at a higher level of behavioral control may be essential for selecting and initiating adaptive motor responses (Wessnitzer and Webb, 2006).

In *G. bimaculatus* the problem of recognizing the conspecific calling song can be described as a sequence of filter processes that gradually sharpen the neuronal responses to be more selective, which eventually lead to a species-specific phonotactic motor response (Figure 2). In this sequence, only the final stage of signal processing may be regarded as a “feature detector,” whereas the lower levels provide “filtering processes.” An important functional difference between the filtering processes and the feature detector is that only the feature detector activity should be coupled to a behavioral decision that may be initiated once the detector is activated; none of the preceding filter processes should have such an impact. Several filtering steps contribute to calling song feature detection in the cricket brain, with a similar organization of sensory processing in other sensory systems.

## Processing of Sound Frequency

The conserved frequency tuning at different levels of the auditory pathway demonstrates that peripheral biomechanical

filtering provides the essential basis for the tuning of phonotactic behavior. The frequency selectivity of female phonotactic behavior is already determined at the level of the hearing organ, and the tuning of the hearing organ defines the tuning of the auditory afferents. The detailed basis of frequency tuning in the cricket hearing organ is however not yet revealed. Auditory filter mechanisms, which tune hearing organs to the frequencies of the communication signal, are found in many other species, which depend on acoustic signals for mate attraction (grasshoppers: Meyer and Elsner, 1996, 1997), predator avoidance (moths: Schiolten et al., 1981; Fullard, 1984), and host detection (parasitic flies: Robert et al., 1992; Oshinsky and Hoy, 2002). These systems represent examples of a peripheral “matched filter,” which limits the information received by the nervous system, but simplifies the way it can be processed (Wehner, 1987).

Comparing the tuning curve of AN1 with the tuning of the auditory nerve may suggest that some additional central neural processing may sharpen the response of AN1 or rather that AN1 is selectively activated by the low frequency afferents. The data nonetheless indicate that the best mechanical response of the auditory organ drives the tuning of the majority of auditory afferents and finally the tuning of the AN1 interneuron, which matches phonotaxis (Kostarakos et al., 2008) and is crucial for phonotaxis as it provides the auditory information to the brain (Schildberger and Hörner, 1988). The response of the AN1 neuron subsequently determines the frequency tuning of brain neurons in the delay line coincidence detector network (Schöneich et al., 2015) and the tuning of the behavioral response. Like in other insect auditory systems the frequency filter in crickets is already established at the most peripheral level and provides the first filter in the calling song recognition process.

## Onset Responses to Sound Pulses

Phasic responses of afferents and interneurons are a common feature of insect mechanoreceptive neurons (Field and Matheson, 1998). In auditory sensory neurons, they enhance the response to the onset of sound pulses (Nabatiyan et al., 2003) and are therefore suited to reliably code the timing of song patterns (Machens et al., 2003). The pool of afferent neurons with synchronously activated spike patterns (Ronacher and Römer, 1985) provides the nervous system with a robust temporal representation of regularly repeated communication signals. The prevalence of phasic responses in auditory neurons may indicate that evolution has shaped the call of male crickets into a series of regularly-repeated sound pulses in order to exploit the phasic response of the auditory afferents of females. This is in-keeping with the concept of sensory exploitation; as communication signals may evolve by the signaler exploiting pre-existing sensory biases in receivers (Ryan and Rand, 1993).

Reciprocal inhibition at the level of thoracic ON1 neurons enhances and sharpens the response to the onset of sound, and thereby is suited to especially represent short sound pulses in the activity pattern of the neurons. The dynamics of spike activity in ON1 at sound onset is in agreement with the reciprocal inhibition functioning as a temporal filter (Wiese and Eilts, 1985; Wiese and Eilts-Grimm, 1985). Based on the time constants of the transmission delay between the neurons, these authors had
suggested that the tuning of the cricket auditory pathway to the calling song pattern may be due to the reciprocal inhibition, which follows the intervals of the sound pattern. However, such a filter had not yet been clearly demonstrated experimentally, as cricket auditory systems have rarely been analyzed under symmetrical stimulus conditions, like during phonotaxis when the auditory signal arrives from the front. As the strength of the inhibitory coupling may vary in different animals, the significance of this bilateral processing mechanism remains to be substantiated; it certainly is not the pattern recognition mechanism for the calling song. However, the mechanism may contribute to the enhanced information transfer, i.e., the number of bits coded by the spike patterns, in ON1 neurons for species-specific pulse rates as described in *T. oceanicus* (Marsat and Pollack, 2004). The excitatory and inhibitory inputs to ON1 neurons depend on the directionality of the ears, therefore processing at the level of the ON1 neurons may form a type of spatially selective filter for the crickets' communication signal (Marsat and Pollack, 2004). This filter mechanism would be especially important whenever the insects face a frontal signal source, such as during the approach of a singing male. As a spatially selective filter, it should play a crucial part in hyper-acute auditory orientation that allows females to steer to signal sources which are just 1–2 degree off their length axis (Schöneich and Hedwig, 2010).

The combination of the phasic-tonic response properties of the auditory afferents and the onset-enhancing mechanisms of some first order interneurons allow for an efficient neural representation of the cricket's acoustic communication pulses. Together, they can be regarded an important filtering step for the processing of calling song pulses which occurs at the thoracic level. To what degree this processing at the level of the ON1 also influences auditory activity ascending to the brain will need further elucidation.

## Detecting Pulse Rate—A Feature Detector of Calling Song

The recordings from brain neurons provide strong support for a circuit comprising a delay line and a coincidence detector (Schöneich et al., 2015), as outlined in a general concept of resonant network design (Reiss, 1964; Weber and Thorson, 1989; for a discussion of concepts for cricket pattern recognition such as internal templates, band-pass filtering, resonant networks see Kostarakos and Hedwig, 2015). Based on the initial filter processes, the delay line coincidence detection circuitry in the brain allows a feature detecting neuron (LN4) to selectively respond to the pulse rate of the calling song. It provides a robust description of the pulse-rate filter at a circuit and cellular level. The pulse rate tuning of the LN4 neuron matches the band-pass tuning of phonotactic behavior, as well as its frequency dependence (Kostarakos and Hedwig, 2015; Schöneich et al., 2015). The neuron therefore is a higher order neuron that can be classified as a feature detector for the cricket's calling song (Hoy, 1978).

The function of this circuitry depends on two essential processes: generation of a delay line via a post-inhibitory rebound and coincidence detection. A computational model

for temporal selectivity in the acoustically communicating fish *Pollimyrus adspersus* based on a post-inhibitory rebound mechanisms, shows that temporal selectivity of the network can be tuned by the delayed time course of the post-inhibitory and by the subsequent excitatory input that coincides with the intrinsic rebound excitation (Crawford, 1997; Large and Crawford, 2002). By systematic changing the timing of the post-inhibitory rebound, this model network allows to tune output neurons to different click rates of the fish communication signal. Post-inhibitory rebound also occurs in the mouse auditory pathway where neurons in the superior olivary nucleus generate a pronounced post-inhibitory rebound underlying their selectivity for periodic low frequency amplitude modulations of sound signals (Felix et al., 2011). Post-inhibitory rebound is furthermore widely involved in precisely timed auditory processing (Koch and Grothe, 2003; Kopp-Scheinpflug et al., 2011). Delay lines and coincidence detectors covering time scales of many milliseconds are also implicated in the processing of echolocating signals in bats where they lead to topographic maps for echo delays (Suga, 1990; Kössl et al., 2014). In general they may represent a fundamental neural mechanism for processing the temporal structure of sound signals.

The feature detecting circuits are present at both sides of the protocerebrum and are coupled via local bilateral projecting neurons (Kostarakos and Hedwig, 2012), two bilateral song recognizers had been proposed by Pollack (1986). If and how the bilateral circuits interact, is not yet resolved; in acoustically communicating grasshoppers the auditory information from both sides is added in support of pattern recognition (von Helversen and von Helversen, 1995).

The field cricket *G. bimaculatus* may only need an auditory feature detecting mechanism for the pulse pattern of the calling song as rivalry and courtship signals are embedded in more complex close up encounters of mates. As the timing of sound pulses during calling and rivalry song is quite similar, the discussed filter mechanisms likely are also activated during rivalry song; whereas the high pitch courtship signals may require a different line of processing. The modulatory component in the auditory pathway, which allows for transient steering to non-attractive signals, may provide the basis for temporal filtering at the chirp level. For females, it will be sufficient to employ one neural circuit for pulse rate recognition and use a modulatory effect based on the pattern recognition process to also control responses at the time scale of the chirps.

## Control of Phonotactic Behavior

Female phonotaxis gradually develops and appears with sexual maturation 6–7 days after the last molt in *G. bimaculatus* (Loher et al., 1993) and 10–13 days in *G. assimilis* (Pacheco et al., 2013); it is strongly reduced after mating and upon female contact to males (Cade, 1979; Loher et al., 1993); and in *T. oceanicus* it may even depend on social experience of the larvae (Bailey and Zuk, 2009). The quality and strength of phonotaxis varies among females. Only 25–50% perform phonotaxis reliable under experimental conditions (Weber et al., 1981) and the probability that a female shows phonotaxis changes over time
(Bailey, 2011). The physiological background for the maturation and variation in the strength of phonotaxis over periods of days is not yet resolved; however juvenile hormone may not play a role (Loher et al., 1992). Understanding the physiological background and moreover having neurochemical tools available to control phonotactic behavior would be a decisive advance for the neurophysiological analysis.

On a short time scale females steer to non-attractive patterns which are interspersed into calling song (Doherty, 1991; Poulet and Hedwig, 2005). This change in phonotactic behavior will be adaptive under natural conditions, when the acoustic signal quality transiently deteriorates (Forrest, 1994) and needs to be considered when interpreting behavioral data.

The location of the delay line coincidence detector circuit in the anterior protocerebrum raises a central question of how the pulse rate recognition circuit may finally initiate the phonotactic motor response. The central control mechanisms for phonotaxis is likely embedded in more general brain control architecture for insect behavior involving the central body complex (Strausfeld, 1999; Wessnitzer and Webb, 2006). Cricket auditory behavior is controlled by the circadian clock; medulla bilateral neurons project toward the neuropil dorsal to the central body and the stalk of the mushroom body (Yukizane et al., 2002). Neuropil areas in the vicinity of the central complex and the mushroom bodies are implicated in the control of singing (Huber, 1962; Otto, 1971; Hedwig, 2006; see Hoffmann et al., 2007 for grasshoppers). In flies and cockroaches the central body complex is involved in the control of walking (Strausfeld, 1999; Strauss, 2002; Bender et al., 2010). Like in some other insects in crickets it provides a compass like map for spatial orientation to polarized light (Sakura et al., 2008); yet so far we do not know to what degree it may contribute to auditory orientation during phonotaxis. The dendrites of local and descending auditory responsive brain neurons are found in the lateral accessory lobes, which generally are implicated in the control of insect motor activity and are regarded as a pre-motor region of the insect brain (Zorovič and Hedwig, 2011). Ipsilateral descending brain neurons controlling walking have also been identified in the dorsal protocerebrum (Böhm and Schildberger, 1992). However, recordings of descending brain neurons during robust phonotactic walking are still lacking; so far the reported auditory responses of such interneurons (Staudacher and Schildberger, 1998; Zorovič and Hedwig, 2013) are not sufficient to identify neural commands as required for fast and precise phonotactic steering.

The modulatory effect of pattern recognition on phonotactic steering may control auditory-motor integration at the level of thoracic networks involving posteriorly branching auditory afferents and DN1 interneurons which are tuned to the cricket calling song (Imaizumi and Pollack, 2005; Poulet and Hedwig, 2005). In such a scenario precise descending motor commands would not be required and direct reflex-like responses to auditory signals could be integrated into the walking motor pattern at the thoracic level once the modulation kicks in. How auditory steering is incorporated into the walking motor output adds another complexity to signal processing, which we just begin

to understand at the behavioral level using high speed video recordings (Baden and Hedwig, 2008; Witney and Hedwig, 2011; Petrou and Webb, 2012).

Animals with specialized behavior provide model systems to analyse adapted neural processing. Particularly, insects with their “simple” nervous systems allow a detailed study of neural mechanisms at the level of identified neurons, to unravel how the system is designed to process relevant stimuli. This review focussed on data in the crickets *G. campestris* and *G. bimaculatus*. From this a comprehensive picture starts to emerge outlining the functional properties and neural basis of auditory signal processing. Pattern recognition is based on a sequence of filter mechanisms in the auditory pathway, which selectively respond to a characteristic property of the calling song and gradually sharpen the response of a neural feature detector in the brain. Based on these findings a most interesting comparative approach could reveal the filter and feature detecting mechanisms in other species, which signal with different pulse patterns for mate attraction (Alexander, 1962). Will these species use functional similar filter mechanisms and a neural circuit in the brain as a feature detector network, and in which way will the properties of the component neurons and the networks be adapted to the species-specific signals? Computational approaches based on Gabor filters have addressed this question and predict different temporal filter properties (Hennig et al., 2014; Ronacher et al., 2015). However, physiological experiments are required to reveal the actual species-specific adaptations in the neuronal mechanisms for pattern recognition that have been adapted and shaped during evolution.

## ETHIC STATEMENT

Experiments in the Hedwig lab complied with the principles of Laboratory Animal Care.

## AUTHOR CONTRIBUTIONS

The author confirms being the sole contributor of this review and approved it for publication.

## FUNDING

Supported by the Biotechnology and Biological Sciences Research Council (BB/J01835X/1) and the Isaac Newton Trust (Trinity College, Cambridge).

## ACKNOWLEDGMENTS

I thank Kostas Kostarakos and Stefan Schöneich for their contribution to the analyses of the feature detector network; Tim Bayley and Pedro Jacob for their constructive comments on the MS, Julie Sarmiente-Ponce for the frequency tuning data of phonotaxis, Oliver Bock-Brown and Jack Stockdale for the data on the modulation of phonotactic steering, and Leonie Remm for images and sound recordings.
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**Conflict of Interest Statement:** The author declares 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 Hedwig. 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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# Encoding of Tactile Stimuli by Mechanoreceptors and Interneurons of the Medicinal Leech

Jutta Kretzberg¹,²*, Friederice Pirschel¹,³, Elham Fathiazar¹ and Gerrit Hilgen¹,⁴

¹Computational Neuroscience, Department of Neuroscience, University of Oldenburg, Oldenburg, Germany, ²Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany, ³Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA, ⁴Faculty of Medical Sciences, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK

For many animals processing of tactile information is a crucial task in behavioral contexts like exploration, foraging, and stimulus avoidance. The leech, having infrequent access to food, developed an energy efficient reaction to tactile stimuli, avoiding unnecessary muscle movements: The local bend behavior moves only a small part of the body wall away from an object touching the skin, while the rest of the animal remains stationary. Amazingly, the precision of this localized behavioral response is similar to the spatial discrimination threshold of the human fingertip, although the leech skin is innervated by an order of magnitude fewer mechanoreceptors and each midbody ganglion contains only 400 individually identified neurons in total. Prior studies suggested that this behavior is controlled by a three-layered feed-forward network, consisting of four mechanoreceptors (P cells), approximately 20 interneurons and 10 individually characterized motor neurons, all of which encode tactile stimulus location by overlapping, symmetrical tuning curves. Additionally, encoding of mechanical force was attributed to three types of mechanoreceptors reacting to distinct intensity ranges: T cells for touch, P cells for pressure, and N cells for strong, noxious skin stimulation. In this study, we provide evidences that tactile stimulus encoding in the leech is more complex than previously thought. Combined electrophysiological, anatomical, and voltage sensitive dye approaches indicate that P and T cells both play a major role in tactile information processing resulting in local bending. Our results indicate that tactile encoding neither relies on distinct force intensity ranges of different cell types, nor location encoding is restricted to spike count tuning. Instead, we propose that P and T cells form a mixed type population, which simultaneously employs temporal response features and spike counts for multiplexed encoding of touch location and force intensity. This hypothesis is supported by our finding that previously identified local bend interneurons receive input from both P and T cells. Some of these interneurons seem to integrate mechanoreceptor inputs, while others appear to use temporal response cues, presumably acting as coincidence detectors. Further voltage sensitive dye studies can test these hypotheses how a tiny nervous system performs highly precise stimulus processing.

**Keywords:** mechanoreception, somatosensory system, touch, pressure, skin stimulation, voltage sensitive dye, local bend, hirudo

## OPEN ACCESS

**Edited by:**
Sylvia Anton,
French National Institute for
Agricultural Research (INRA), France

**Reviewed by:**
Harald Tichy,
University Vienna, Austria
Daniel A. Wagenaar,
California Institute of Technology, USA

**\*Correspondence:**
Jutta Kretzberg
jutta.kretzberg@uni-oldenburg.de

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

**Received:** 26 June 2016
**Accepted:** 14 October 2016
**Published:** 28 October 2016

**Citation:**
Kretzberg J, Pirschel F, Fathiazar E and Hilgen G (2016) Encoding of Tactile Stimuli by Mechanoreceptors and Interneurons of the Medicinal Leech. Front. Physiol. 7:506.
doi: 10.3389/fphys.2016.00506
# INTRODUCTION

A simple neuronal system produces a basic behavior with a surprisingly high precision: The leech bends away locally from a light touch (Stuart, 1970; Kristan, 1982; Lockery and Sejnowski, 1992; Lewis and Kristan, 1998a; Zoccolan et al., 2002; Baca et al., 2005; Thomson and Kristan, 2006) with a spatial precision of approximately 1 mm (Baca et al., 2005); similar to that of the human fingertip (Johnson, 2001). The different leech mechanoreceptor types show similar spiking patterns to primate and human mechanoreceptor types (Lewis and Kristan, 1998b; Baca et al., 2005; Johansson and Flanagan, 2009; Smith and Lewin, 2009), and mechanoreceptor responses were shown to depend on common stimulus properties like touch location, mechanical force, duration, and speed (leech: Carlton and McVean, 1995; Zoccolan et al., 2002; Baca et al., 2005; Pirschel and Kretzberg, 2016; primate reviews: Johansson and Flanagan, 2009; Abraira and Ginty, 2013; Saal and Bensmaia, 2014). However, the number of mechanoreceptor cells in the leech skin is an order of magnitude lower than in the human fingertip, which is innervated by more than 200 mechanoreceptors per cm² (Vallbo and Johansson, 1984). Nevertheless, the complex innervation structure of the leech skin enables the highly accurate and reproducible local bend response to avoid being stimulated with minimal muscle movement. Hence, the small and simple neuronal system of the leech raises a fundamental computational question on sensory processing: How can such a precise behavior be performed with a nervous system consisting of so few cells?

The leech nervous system is a rigorously segmented, highly repetitive ventral nerve cord with one ganglion per segment. Each ganglion contains about 400 neurons of approximately 200 types (Kristan et al., 2005). The leech local bend behavior was suggested to be controlled by a three-layered feed-forward network consisting of mechanoreceptors, interneurons, and motor neurons (Kristan, 1982; Lockery and Kristan, 1990b; Lewis and Kristan, 1998a; Kristan et al., 2005), which are found in each segment of the animal.

The input layer of the local bend network consists of mechanoreceptors. The three types of leech mechanoreceptors were classically associated with tactile stimuli of distinct intensities, resulting in the names of these neurons: T cells for light touch, P cells for stronger pressure, and N cells for noxious, very strong squeeze (Nicholls and Baylor, 1968). In computational terms, these distinct functions refer to a labeled line code. Moreover, spike patterns in response to tactile skin stimulation differ characteristically between receptor types. T cells produce transient, fast adapting responses to stimulus on- and offset, while P cells respond with sustained, regular spiking throughout the stimulation and N cells produce only few spikes separated by long interspike intervals (Nicholls and Baylor, 1968; Carlton and McVean, 1995; Lewis and Kristan, 1998b; Pirschel and Kretzberg, 2016). In each segment the total population of mechanoreceptors consists of only 14 cells (6 T, 4 P, 4 N) innervating the skin at different depths with their processes (Blackshaw, 1981; Blackshaw et al., 1982) and sending information about tactile stimuli toward their cell bodies in the segmental ganglion. Skin regions innervated by

several cells lead to widely overlapping receptive fields between mechanoreceptors. For example, tactile stimulation applied to the ventral midline cause spike responses in two P, two T, and two N cells (see **Figure 1A** bottom for a sketch of overlapping receptive fields at ventral midline). In all mechanoreceptors the inhomogeneous distribution of dendritic branches and nerve endings in the skin (Blackshaw, 1981; Blackshaw et al., 1982) cause spatially structured receptive fields. Stimulation close to the most densely innervated receptive field center triggers highest spike counts and shortest spike latencies (Nicholls and Baylor, 1968; Thomson and Kristan, 2006; Pirschel and Kretzberg, 2016).

Since P cell responses were found to influence muscle movements more strongly than T cells, previous studies assumed that P cells elicit the local bend reflex (Kristan, 1982; Lewis and Kristan, 1998b; Zoccolan et al., 2002). Therefore, a prior study aiming at the identification of interneurons involved in the local bend network was limited to neurons responding to presynaptic P cell spikes (Lockery and Kristan, 1990b). Based on a huge number of double recordings this study identified one unpaired and eight paired interneurons to be involved in the local bend network. Most of these interneurons responded with postsynaptic potentials to spikes from all four P cells suggesting very extended receptive fields. Substantial lateral synaptic connections between interneurons were not found in this study except for electrical connections between the pair of interneurons of the same type (Lockery and Kristan, 1990b). In contrast, identified motor neurons were found to be laterally connected. In addition to inhibiting longitudinal muscles, inhibitory motor neurons also suppress excitatory motor neurons, which cause contraction of the antagonistic muscles (Granzow and Kristan, 1986; Lockery and Kristan, 1990a; Kristan et al., 2005; Baca et al., 2008). This antagonistic inhibition leads to a stronger bending movement to one side.

Based on these results, Lewis and Kristan (1998a) developed a computational model of the local bend network. The model consists of three layers of cells with evenly spaced cosine shaped tuning curves, implementing a population vector paradigm as optimal decoding scheme from one layer to the next. Using the spike rates of the four P cells as input, this model predicted the behaviorally observed direction of local bending. While the cell numbers of the input and output layers were fixed to 4 P cells and 10 motor neurons, the number of the much less-known interneurons was varied, revealing that the number of 17 previously identified local bend interneurons (Lockery and Kristan, 1990b) was compatible with the modeled network structure.

Despite the elegant plainness of this model, recent results require to revise the hypothesized structure and computations of the local bend network. Stimulus-estimation studies revealed that latency differences between two P cell responses carry more information about tactile stimulus position on the skin than spike counts (Thomson and Kristan, 2006; Pirschel and Kretzberg, 2016). Hence, temporal response features might play an important role in the network. Moreover, latency differences between T cell pairs allowed an even more exact estimation of stimulus location (Pirschel and Kretzberg, 2016).
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**FIGURE 1** | The body-wall preparation with receptive fields of mechanoreceptors and standard ganglion map. (A, top) Photograph of the body-wall preparation with the ganglion (sketch in B), which is pulled slightly posterior for better access through a hole in the skin. The gray bar indicates the main area used for tactile stimulation. The center of the preparation between the two dark stripes on the skin, called the ventral midline, was defined as 0°. The skin was touched at the third annulus of segment 10, identified by the sensilla positions. Touch locations to the left were denoted as negative numbers of degrees (left end of the preparation: -180°) and to the right as positive numbers (right end: +180°). The black stripes are located approximately at -90° and +90°. (A, bottom) Receptive fields of all mechanoreceptors responding to tactile stimulation at the ventral midline are shown in a sketch of the body wall preparation: Left and right $T_V$ cells (dashed blue ovals), left and right $P_V$ cells (red), and left and right N cells (green). (B) Sketch of the leech ganglion with cell body positions of bilateral mechanoreceptor pairs of $P_V$ (red), $T_V$ (blue), N (green), corresponding to the receptive fields shown in (A), and of interneurons 157, 159 (magenta), 162 (yellow). Electrodes (symbolized by black pointed angles) were used for intracellular recordings of up to 3 neurons (combinations of $T_V$, $P_V$, N, 157, 159, and 162; the electrode positions shown here refer to the data shown in **Figures 5A,B**), while the skin was stimulated mechanically (see section Methods).

Therefore, P cell spikes might not be the only relevant input to the network, but the role of T cells—and maybe also N cells—in the network should be reconsidered. Another aspect not covered by the classical local bend model is the fact that the behavioral response to a tactile stimulus depends on a combination of stimulus properties like location, mechanical force intensity and duration (Baca et al., 2005), as well as velocity (Carlton and McVean, 1995). On the mechanoreceptor level, the finding that several response features, including spike count and latency, depend on more than one stimulus property leads to the fundamental question how complex stimuli are encoded by the nervous system. For example, stimulus location and mechanical force intensity influence the neuronal responses of the mechanoreceptor types in an ambiguous way. A mechanoreceptor response with a high spike count and a short response latency could be elicited either by a relatively weak stimulus close to the cell's receptive field center, or by a stronger stimulus at a less preferred position (Pirschel and Kretzberg, 2016). How does the leech distinguish between these stimuli based on such ambiguous responses?

Our hypothesis is that a population of interneurons solves this task by means of multiplexing, simultaneous encoding of different stimulus properties with different response features. The population of T and P cells provides multiplexed information about combinations of e.g., stimulus location and intensity by encoding them simultaneously

with temporal and spike count features (Pirschel and Kretzberg, 2016). The first aim of this study is to investigate if interneurons respond specifically to one mechanoreceptor type—indicating a labeled line code, as it was classically assumed for intensity encoding—or if they integrate inputs from multiple receptor types. The second question is, which mechanoreceptor response features determine interneuron responses. Are all of them integrators as it was assumed in the spike rate-based computational network model (Lewis and Kristan, 1998a), or are some of them specialized for temporal processing?

In the first part of this study, responses of all three types of mechanoreceptors (T, P, and N cells) to tactile skin stimulation are revisited. Our recent results (Pirschel and Kretzberg, 2016) are extended by adding N cell responses and the analysis of two-dimensional tuning to combinations of different stimulus locations and intensities. In the second part, intracellular electrophysiology, anatomical studies and voltage sensitive dye recordings are performed as complementary experimental approaches to study interneurons on the next network layer. In particular, we aim to identify interneurons responding to input from P and/or T cells and to find out which mechanoreceptor response features determine their postsynaptic responses. In this way, we try to identify general computational principles of sensory information processing, which are not limited to the leech, but could be implemented also by other sensory systems.
# METHODS

## Animals and Preparations

All experiments were performed on adult, hermaphrodite medicinal leeches (Hirudo verbana), weighing 1–2 g. According to German regulations, no ethics approval is needed for the work on these invertebrates. Leeches were obtained from Biebertaler Leech Breeding Farm (Biebertal, Germany) and were kept in tanks with Ocean Sea Salt 1:1000 diluted with purified water. Animals were kept at room temperature and anesthetized with ice-cold saline (Muller and Scott, 1981) before and during dissection. Experiments were performed at room temperature.

For the body-wall preparation (Figure 1A; detailed description is given in Pirschel and Kretzberg, 2016), segments 9–11 were dissected and innervations of segment 10 remained unscathed, while the ganglion was accessible through a hole in the skin (Figure 1A). The middle annulus of the 10th segment, which was identified by the location of the sensilla (Blackshaw et al., 1982), was used for the skin stimulation.

Voltage sensitive dye (VSD) experiments (section Voltage Sensitive Dye Experiments and Analysis) and cell fills (section Dye Injection and Cell Labeling) were performed on isolated ganglia dissected from segment 10.

## Tactile Stimulation and Intracellular Electrophysiology

In the skin preparation, the skin was stimulated by the Dual-Mode Lever Arm System (Aurora Scientific, Ontario, Canada, Model 300B; poker tip size: 1 mm²; see Baca et al., 2005; Thomson and Kristan, 2006; Pirschel and Kretzberg, 2016). The stimulus was applied as an instantaneous step function of 200 ms length. At stimulus onset, the poker moved down at very high speed, reached the desired pressure within 2 ms, fluctuated slightly for less than 10 ms and stayed at a constant position, until moving up at very high speed again. Poker speed and duration of skin indentation were the same in all experiments. Touch locations were set relative to the ventral midline (set as 0°) of the preparation: Locations to the left are denoted as negative and to the right as positive numbers of degrees (Figure 1A). The stimulus was varied in mechanical force intensity (5–200 mN) and location (−20° to +20°, relative to the ventral midline, in 5° steps) (see Lewis and Kristan, 1998b; Baca et al., 2005; Pirschel and Kretzberg, 2016). Other parameters like shape or indentation depth were not varied. All combinations of stimulus properties force intensity and location were presented 8–15 times in pseudo-randomized order.

While stimulating the skin mechanically, intracellular recordings from one to three cells at the same time were performed with sharp glass micropipettes (resistances between 20 and 40 MΩ) filled with 3 M potassium acetate (for detailed description of the experimental rig, see Pirschel and Kretzberg, 2016). Varied combinations of the three types of mechanosensory cells (T<sub>v</sub> and P<sub>v</sub>, N) and three types of interneurons (157, 159, and 162) were obtained. Numbers of preparations used for analyses are given in the figure legends. Mechanosensory cell types were easily identifiable based on their electrical properties (Nicholls and Baylor, 1968). In tactily stimulated preparations,

the receptive field of recorded mechanoreceptors was confirmed prior to experiments, yielding the standard map shown in Figure 1B. In most ganglia, cell bodies of T<sub>v</sub> and P<sub>v</sub> (the mechanoreceptors with ventral receptive fields) were located most laterally. But since in particular T cells sometimes switch their positions, we specify in this manuscript subtypes of T and P cells only for experiments with attached skin. The interneurons (INs) were identified according to the results and descriptions by Lockery and Kristan (1990b).

To physiologically identify synaptic connections, intracellular double recordings of INs and a simultaneous recording of a mechanosensory cell were obtained, while stimulating the mechanosensory cell by constant current pulses of 1.5 nA, lasting 50 ms.

## Dye Injection and Cell Labeling

To study cell morphologies and putative points of contact, interneurons, and mechanosensory cells were filled in isolated ganglia by means of sharp glass electrodes (20–40 MΩ) with 10 mM Alexa-dyes (Alexa Fluor 488/546/633, Invitrogen, Karlsruhe, Germany) and/or 2% Neurobiotin (Vector Labs, Peterborough, UK) solved and backfilled with 200 mM KCl. Cells were iontophoretically injected either with positive (Neurobiotin) or negative (Alexa) currents (2–4 nA, 500 ms, 1 Hz, 30–60 min). Neurobiotin-filled samples were allowed to settle for 1 h after injection before further processing. All samples were fixated in 4% PFA (Sigma, Munich, Germany) for 1 h and rinsed 6 × 10 min in 0.1 M PBS. Neurobiotin-filled samples were afterwards incubated in 1:1000 Streptavidin DyLight 488 (Vector Labs)/PBS/0.5% Triton-X overnight at 4°C. Samples were rinsed afterwards (6 × 10 min) in PBS and embedded with VectaShield (Vector Labs) on a microscope slide for high resolution microscopy. Fluorescent image acquisition and analysis were performed as previously described (Meyer et al., 2014). Briefly, filled cells were scanned with a Leica TCS SP2 (Leica, Nussloch, Germany) Confocal Microscope with an HCX PL APO 40.0 × 1.25 OIL UV objective to obtain confocal stacks with a voxel dimension of 0.366 × 0.366 × 0.200 µm. The scanned sequential images were trimmed for the desired z-depth and a maximal projection of the images was calculated with ImageJ (NIH, Bethesda, MD). Channel overlay and gentle adjustment of contrast and brightness were done with Photoshop CS3 (Adobe, San Jose, CA). An animation of the confocal stack underlying Figure 6B is provided in the Supplemental Material.

## Voltage Sensitive Dye Experiments and Analysis

Voltage sensitive dye (VSD) recording was performed in isolated leech midbody ganglia simultaneously to a double intracellular recording from a P and a T cell. Both mechanosensory cells were stimulated with intracellular current injection, while the activities of all visible cells on the ventral side of the ganglion were monitored through a microscope [Zeiss Examiner.D1, objective plan-apochromat 20 x/1.0 DIC (UV)] with a CCD camera (Photometrics QuantEM:512SC), using bath-applied VF2.1.CL dye ($\lambda_{max}$ = 522 nm, $\lambda_{em}$ = 535 nm, see Miller et al., 2012). Imaging was performed with a temporal sampling frequency of
94 Hz and a spatial resolution of 64 × 128 pixels. Prior to the recording, a snap shot was taken with the full spatial resolution of the camera, 512 × 512 pixels (Figure 2A), based on which regions of interest (ROIs) representing individual cell bodies were selected manually (Fathiazar et al., 2016; see Figure 7B for an example). In this manuscript, data from one representative VSD recording is presented. Similar results were obtained in seven additional preparations.

Electrical stimulation, consisting of 10 ms long pulses of 2 nA (T cell) or 3 nA (P cell), was designed to mimic these cells' spiking patterns in response to tactile stimulation of 70 mN in the ventral midline of the skin in a semi-intact preparation (Pirschel and Kretzberg, 2016). Four different stimulus conditions were compared (see Figure 7): In the PT-stimulated condition, both sensory cells were electrically stimulated in a pattern that

reproduces natural responses to tactile stimulation. In the P-stimulated and the T-stimulated condition only one of the cells was stimulated, while the other cell remained unstimulated. In control condition, both cells were not stimulated. In our experiments, responses to 7 repetitions of each condition (trials) were recorded.

For data analysis, 55 ROIs corresponding to visible cell bodies were drawn over the first frame of the VSD recording presented in this manuscript. VSD signals of the cells were extracted by averaging and normalizing the brightness of the pixels in the corresponding ROIs. Movement and bleaching artifacts were corrected as described in Fathiazar and Kretzberg (2015). For each cell, baseline (black line in Figure 2B) was calculated as an average of the seven trials of control condition. Baseline was subtracted from all VSD signals obtained for all four stimulus conditions. To reduce the noise level, the difference signal was filtered with a moving average filter of three frames window size.

Statistical analysis to identify stimulus-activated cells was performed as described in Fathiazar et al. (2016). In short, the histogram of the filtered VSD difference signals in control conditions was calculated for each cell. Applying a statistical significance level of $\alpha = 0.05$ on this histogram, we defined the thresholds of activity differing significantly from baseline (black vertical lines in Figure 2C), indicating very strong de- or hyperpolarization of the cell's membrane potential. These thresholds (quantiles 2.5 and 97.5% of control response distribution) were applied to the filtered VSD difference signals obtained for the three conditions of mechanoreceptor stimulation (Figure 2D: P-stimulated condition) to discriminate which individual cells were activated at each time frame (activation map of the P cell in Figure 2B). The activation maps I(i,j) in Figures 7C–F show the pooled activity for all seven trials of each condition, where $I(i,j) \in \{0, ..., 7\}$ and $i \in \{1, ..., 55\}$ indicates the cell number (chosen by the sequence of cells' activations after stimulus onset, not corresponding to the cell numbers in the standard ganglion map shown e.g., in Lockery and Kristan, 1990b) and $j \in \{1, ..., 110\}$ is the frame (referring to times $0.16 < t < 1.36$ in s). $I(i,j)$ has the value of 0 (shown in dark blue) if the cell i was not activated in any of the seven trials. If cell i was found to be activated in all the trials in frame j, $I(i,j)$ has the value of seven (shown in yellow). A cell i was classified as a "stimulus-activated" cell for a specific stimulus condition (PT-, P-, or T-stimulated), if at least six of the seven trials revealed significantly increased or decreased activity compared to baseline in at least one time frame in the period $0.53 < t < 0.87$ s (from stimulus onset to offset plus five frames).

<figure><img src="image_193.png" /><figcaption>**FIGURE 2** | VSD data analysis method. (A) Snapshot of a ganglion with full resolution. The red circle indicates the cell body of the P cell, which was stimulated by intracellular current injection into the soma during the experiment. (B) Time course of the electrical P cell stimulation, applied through an intracellular electrode (top red trace) and corresponding single VSD response of the P cell (red spikes) in comparison to a single P cell response to control condition (not stimulated, gray) and baseline (averaged P cell responses to 7 traces of control condition, black). The activity map (colored line below) indicates for each recording frame in how many of 7 response traces the activity differed significantly from control condition. (C) Histogram of filtered differences between P cell responses to control condition (7 traces with 110 frames each) and baseline. Black vertical lines indicate thresholds of the $p < 0.05$ criterion for activity differing significantly from baseline. (D) Histogram of filtered differences between P cell responses to stimulated condition (7 traces with 110 frames each) and baseline. Black vertical lines indicate the significance thresholds determined in (C), showing that the activity differs significantly from baseline more often than in control condition. The activity map in (B) depicts in which frames the significant deviations from baseline occurred, indicating consistent activation during current stimulation.</figcaption></figure>

# RESULTS

## Encoding of Tactile Information by Mechanoreceptors

The three types of leech mechanoreceptors were classically associated with tactile stimuli of different intensities, as reflected in their notation: T cells for light touch, P cells for stronger pressure, and N cells for noxious, very hard mechanical
stimulation (Nicholls and Baylor, 1968). However, simultaneous recordings of different mechanoreceptor types responding to skin stimulation revealed a different picture: Both T and P cells responded reliably to a large range of stimulus intensities, from very light touch (5 mN) to strong pressure (200 mN), and even N cell responses started at a moderate touch intensity of 50 mN (Figure 3). These strongly overlapping sensitivity ranges clearly contradicted the classical idea of a labeled line code with different cell types, signaling the presence of stimuli in distinct force intensity ranges. Instead, this finding suggested that the tiny population of leech mechanoreceptors (6 T cells, 4 P cells, 4 N cells in each ganglion) uses a different strategy for encoding the intensity of tactile stimuli. As shown in Figure 3A, response patterns to tactile stimulation at the ventral midline differed considerably between cell types, in accordance with many previous publications (Nicholls and Baylor, 1968; Carlton and McVean, 1995; Lewis and Kristan, 1998b; Pirschel and Kretzberg, 2016). T<sub>v</sub> cells typically produced transient, rapidly adapting responses, both at stimulus onset and offset, while P<sub>v</sub> cells usually responded with sustained sequences of regularly occurring spikes within the entire duration of tactile stimulation. N cells were not very active when the skin was stimulated with relatively weak pressure, leading to responses consisting of only one or two spikes. Despite these differences in spike timing patterns, all three types of mechanoreceptors shared similar dependencies of standard response features on stimulus intensity. All cells responded to increasing pressure intensity with increasing spikes counts and decreasing response latencies, both of which saturated for high intensities (100–200 mN) in T and P cell responses. In a preceding study (Pirschel and Kretzberg, 2016), we showed for the intensity range of 5–100 mN that summed spike counts of mechanoreceptor pairs yielded the best estimation performance for stimulus intensity. In particular the sustained P<sub>v</sub> cell responses allowed a reliable estimation.

When stimulus location was varied, P<sub>v</sub> and T<sub>v</sub> cells showed the same effects as were reported in previous studies (Thomson and Kristan, 2006; Pirschel and Kretzberg, 2016). Spike rates decreased and latencies increased with increasing distance from the center of the cell's receptive field (Figures 4A,C,D). A similar tendency was also visible for N cell responses (Figure 4B), although the low spike counts (between 0 and 2 spikes in 200 ms), induced by the range of stimulus intensities applied in this study, made results more difficult to interpret. In Pirschel and Kretzberg (2016) it was shown that for a tactile stimulation with 50 mN, the latency differences between pairs of mechanoreceptors, in particular the fast responses of T<sub>v</sub> cells, led to the best location-estimation performance. Here, we extended the analysis of P<sub>v</sub> and T<sub>v</sub> cell responses by varying combinations of stimulus location and force intensity, while keeping velocity and all other stimulus parameters constant across experiments. Stimuli of all intensities yielded similar dependencies of spike counts and latencies on stimulus location, with higher mechanical force triggering more and earlier spikes, resulting in virtually parallel curves for both response features (Figures 4C,D). For T<sub>v</sub> cells similar response characteristics of spike counts and latencies were found even for strong pressure stimuli of 100 mN (Figure 4D),

giving further evidence against a labeled line coding of stimulus intensities.

Since T<sub>v</sub> cells also responded to the offset of a constant tactile stimulation (Figures 3A, 4A, 5A-C), stimulus force intensity and location dependencies of these off-responses were also analyzed (Figure 4E). Only strong pressure stimuli (100 mN) close to the receptive field center triggered large numbers of off spikes in T<sub>v</sub> cells. These off response spike counts decreased steeply with distance (Figure 4E left, Supplementary Figure 1C). For light and moderate tactile stimulation, off-response spike counts were lower then spike counts at stimulus onset. These off-response spike counts depended mainly on stimulus intensity, while

<figure><img src="image_194.png" /><figcaption>**FIGURE 3** | Influences of stimulus intensity on mechanoreceptor responses. (A) Example responses of an intracellular triple recording of a left T<sub>v</sub> cell (blue), a left P<sub>v</sub> cell (red), and a right N cell (green) responding to a tactile stimulus of 10 mN (left) and 100 mN (right) applied for 200 ms at 0° (ventral midline). (B) Spike count and (C) response latency (mean and STD) for T<sub>v</sub> cells (blue), P<sub>v</sub> cells (red) and N cells (green) responding to tactile stimuli with intensities of 5–200 mN applied at 0°. Stronger pressure intensities (> 100 mN) were tested with fewer cells (see legend).</figcaption></figure>
<figure><img src="image_195.png" /></figure>

**FIGURE 4** | **Influences of stimulus location on mechanoreceptor responses.** (A) Example responses of an intracellular triple recording of a left *T*<sub>v</sub> cell (blue), a left *P*<sub>v</sub> cell (red), and a right N cell (green) responding to a tactile stimulus of 50 mN for 200 ms at locations -20° (left) and +20° (right). Ventral midline is defined as 0°, stimulus locations to the right as positive and to the left as negative numbers of degrees. (B) Example for the dependency of N cell spike count and response latency on stimulus location. Spike count and response latency (mean and STD) are shown for one representative double recording of two N cells with 15 repeated stimulus presentations and a stimulus intensity of 100 mN. (C) Dependency of spike count and response latency (mean and STD) of *P*<sub>v</sub> cells (N = 10, each 8–10 stimulus presentations; pooled responses of left and right cells) on stimulus location. Responses at different locations [displayed as distance from receptive field center in (°)] are
(Continued)

<caption>**FIGURE 4** | **Continued**</caption>

shown for three stimulus intensities of 10 mN (yellow), 20 mN (orange), and 50 mN (red). (D) Dependency of spike count and latency (Mean and STD) of *T*<sub>v</sub> cells (N = 10, each 8–10 repeated stimulus presentations; pooled responses of left and right cells) on stimulus location. Responses at different locations [displayed as distance from receptive field center in (°)] are shown for four stimulus intensities of 10 mN (dashed-cyan), 20 mN (dashed blue), 50 mN (solid-cyan), and 100 mN (solid-blue) (N = 8 cells). (E) Dependency of off-spike count and off-spike latency (Mean and STD) of *T*<sub>v</sub> cells on stimulus location (same recordings and figure conventions as in D). Linear fits for the stimulus response curves shown in (D,E) are provided in the Supplementary Material.

stimulus location had virtually no effect, resulting in the parallel flat curves shown in the left panel of **Figure 4E**. Consequently, linear regression revealed shallower decreases and smaller y-intercepts of spike counts at stimulus offset (Supplementary Figure 1C) than at stimulus onset (Supplementary Figure 1A, see also Table 1 in Supplementary Material for comparison). In contrast, the latency of off-responses triggered by moderate and high mechanical force depended almost exclusively on stimulus location (**Figure 4E** right panel, Supplementary Figure 1D). The virtually identical off latency response curves obtained for intensities between 20 and 100 mN rose at least as steeply with increasing distance from the center of the receptive field as for the latencies observed at stimulus onset (Supplementary Figures 1B,D, Supplementary Table 1) and showed similarly low variability (**Figures 4D,E** right panels). Only very soft touch stimuli of 10 mN, which often failed to trigger off-responses at all, caused highly variable off response latencies, which were not approximated well by linear regression (Supplementary Figure 1D). In conclusion, these results suggest that T cell responses occurring at the offset of skin stimulation could play an additional role for tactile encoding.

Interneurons Involved in Tactile
Information Processing

After studying the encoding of tactile stimulus properties at the
mechanoreceptor level, the main questions arising from these
results are: Which mechanoreceptors provide input to which of
the cells at the next network level? And which mechanoreceptor
response features shape the responses of which interneurons
involved in processing tactile information?

To tackle these questions, we performed a combination
of three experimental approaches: Simultaneous intracellular
double recordings from a mechanoreceptor and an interneuron,
anatomical examination revealing potential contact points,
and voltage sensitive dye recordings providing access
to mechanoreceptor-induced responses of many cells
simultaneously.

In the first step, responses of three different interneurons
were characterized by the classical electrophysiological approach:
Intracellular double and triple recordings of mechanoreceptor(s)
and an interneuron (Figure 5). The three interneurons 157, 159,
and 162 (see Figure 1B for cell body positions in the ganglion)
were previously identified as members of the local bend network
according to the criteria that they responded to presynaptic P
<figure><img src="image_196.png" /></figure>

<figure><img src="image_197.png" /></figure>

<figure><img src="image_198.png" /></figure>

<figure><img src="image_199.png" /></figure>

<figure><img src="image_200.png" /></figure>

<figure><img src="image_201.png" /></figure>

**FIGURE 5** | Simultaneous, intracellular recordings of three different interneurons and ipsilateral mechanoreceptors. (A–C) Example responses of mechanoreceptors and three types of interneurons to tactile skin stimulation for 200 ms at 0° (ventral midline). (A) Triple recording of left $T_v$ cell (blue), right $P_v$ cell (red), and left cell 157 (magenta) responding to a tactile stimulus of 70 mN. (B) Double recording of left $T_v$ cell (blue) and left cell 159 (magenta) responding to a tactile stimulus of 50 mN. (C) Triple recording of left $T_v$ cell (blue), a left N cell (green), and left cell 162 (yellow) responding to a tactile stimulus of 70 mN. (D–F) Responses of the same interneurons of types (D) cell 157, (E) cell 159, (F) cell 162 to constant current injection of 1.5 nA for 50 ms (upper trace) applied intracellularly to the ipsilateral $T_v$ cell, which responded reproducibly with regular spike patterns (see spike probability in 5 ms bins) in the same double or triple recording as shown in the corresponding upper panel (A–C). Cell responses were averaged over 20–50 repetitions.

cell stimulation and influenced the activity of motor neuron 3 (Lockery and Kristan, 1990b). Here, our recordings showed that these three interneuron types also receive synaptic input from an ipsilateral $T_v$ cell. Intracellular injection of a constant current step reproducibly triggered rhythmic spike patterns in $T_v$ cells, which elicited clear excitatory postsynaptic potentials (EPSPs) in all three types of interneurons (Figures 5D–F).

Additionally, intracellular recordings of interneurons 157, 159, and 162 during tactile stimulation provided direct evidence that these cell types are involved in the processing of tactile information (Figures 5A–C). The recordings revealed their distinctly different response characteristics: Cell 157 displayed a sustained graded response, resembling integrated EPSPs lasting for the entire duration of stimulation (Figure 5A), while the other two cell types responded more transiently. Cell 159 produced large EPSPs both at tactile stimulus on- and offset (Figure 5B). Cell 162 responded mainly with a very large EPSP at stimulus onset, sometimes triggering a single postsynaptic spike (Figure 5C).

The second step of the network analyses provided anatomical evidence for network connections (Figure 6). Simultaneous dye injections into T cells and interneurons revealed cell morphology and prospective points of contacts. Potential locations of

contacts with a T cell (cyan) were found for all three types of interneurons 157 (magenta, arrowheads in Figure 6B), 159 (magenta, arrowheads in Figure 6C), and 162 (yellow, arrows in Figure 6B). Interestingly, the triple staining of T, 157, and 162 (see also stack animation in Supplementary Material) additionally identified putative contacts of interneurons 157 and 162 suggesting potential lateral network connections at the interneuron level (circles in Figure 6B). However, since the study by Lockery and Kristan (1990b) did not find synaptic responses in double recordings of this cell pair, additional electrophysiological tests are needed.

Neurobiotin injection into a T cell (Figure 6D) led to staining of five additional cell bodies suggesting electrical coupling. For one of them, the location of the cell body matched the location of cell 159 (labeled c in Figure 6D), fitted very well with the electrophysiological finding of this cell type's responses following the time course of T cell responses (Figures 5B,E). Judging from the cell body location one of the other cells could be cell 212 (labeled d in Figure 6D), which was also identified as local bend interneuron by Lockery and Kristan (1990b). Two more cells were stained in the posterior-lateral package of the ganglion. By location these cells could be numbers 61 and 62 (b and a in Figure 6D) in the standard ganglion map (Figure 1B). At a larger
<figure><img src="image_202.png" /><figcaption>**FIGURE 6** | Morphological connections of T cells and interneurons. (A) Dye injections (Alexa Fluor 488/546/633) were performed to reveal the morphology and putative cell-cell contact zones of a T cell (cyan), interneuron 157 (magenta), and 162 (yellow). Z-depth 70 µm. (B) Magnification of the area indicated in (A) (white box) shows putative contacts between T cell and interneuron 157 (arrowheads), T cell and interneuron 162 (arrows), and interneuron 157 and 162 (circles). Z-depth 10 µm. See Supplementary Material for an animation of the confocal image stack underlying this figure. (C) Visualized morphology of an Alexa-dye injected T cell (cyan) and a Neurobiotin injected interneuron 159 (magenta). Arrowheads indicate putative contacts. Z-depth 30 µm. (D) Neurobiotin (cyan) was injected into a T cell (indicated by asterisk) to reveal electrically coupled cells. Putative cell types: (a) and (b) interneurons 62 and 61, (c) 159, (d) 212, and (e) unknown. Z-depth 110 µm. Confocal microscope transmission image overlayed with 40% transparency for cell location identification. In all panels letters A and P indicate anterior to posterior direction of the ganglion.</figcaption></figure>

distance from the T cell an additional cell body (e in **Figure 6D**) was also clearly stained, but remained to be identified.

The third step of our analyses aimed to identify interneurons involved in tactile processing using voltage sensitive dye (VSD) recordings. In these experiments, intracellular double recordings of a T and a P cell were performed, while the activity of the ventral side of the ganglion was imaged. After VSD bath application, graded de- and hyperpolarization of all neurons could be estimated based on the emitted light of the corresponding pixels in the camera image (Miller et al., 2012). Individual spikes could only be identified in VSD traces of some cell types with large and slow spikes, otherwise the temporal resolution of the camera (94 Hz at a spatial resolution of 64

× 128 pixels) and the signal to noise ratio were too low. In the recording shown in **Figure 7**, 55 ROIs representing individual cell bodies were selected for analysis (**Figure 7B**). Four different conditions of electrical stimulation were used during VSD recordings: (1) Control condition without stimulation (**Figure 7C**), used to determine baseline spontaneous network activity, (2) PT-stimulated condition (**Figures 7D,G**) with short current pulses injected into the cell bodies of the T cell and the P cell, which elicited spike trains reproducing typical mechanoreceptor responses to a touch stimulation (Pirschel and Kretzberg, 2016), (3) P-stimulated condition (**Figures 7E,H**) with the same spike train elicited in the P cell as in the PT-stimulated condition, while the T cell remained unstimulated, and the corresponding (4) T-stimulated condition (**Figures 7F,I**) with only T cell stimulation.

For the statistical analysis to identify interneurons activated by mechanoreceptor responses the control condition was used to calculate the normal range of spontaneous activity for each cell. The upper and the lower thresholds of this range were defined as percentiles 2.5 and 97.5% of the empirically determined distribution of VSD values (leading to a significance level of α = 0.05, see Section Methods, **Figure 2C**, and Fathiazar et al., 2016) These thresholds were applied to the same cell's responses during the three different stimulated conditions to find if and when the cell was more de- or hyperpolarized than during control condition (see **Figure 2**). **Figures 7D–F** show for each cell at each time frame how many of the seven trials deviated significantly from baseline, with a color code ranging from dark blue (no deviations from baseline) to yellow (deviation from baseline in this time frame in all seven stimulus presentations). In **Figure 7** cells were numbered according to the timing of their first activation (significant deviation from baseline in at least six of seven stimulus presentations) after stimulus onset in the PT-stimulated condition (**Figure 7D**). Hence, cell numbers in **Figure 7** differ from the cell identity numbers used in the standard ganglion map, e.g. in **Figure 1B** and in Lockery and Kristan (1990b). In **Figure 7** cell number 1 is the stimulated T cell, cell number 2 the stimulated P cell. Cells showing consistent deviations from the baseline during stimulation in at least six out of seven presentations were classified as stimulus-activated (see Section voltage sensitive dye experiments and analysis). These cells are indicated by colored borders in **Figures 7G–I**.

Comparison of different stimulation conditions revealed that electrical stimulation of T and P cell together (**Figure 7D**) as well as activation of P cell alone (**Figure 7E**) activated 22 of the 55 analyzed cells, while T cell stimulation alone elicited significant activation only in 10 cells. However, populations of activated cells were not identical for PT-stimulated and for P-stimulated conditions. One cell (number 24, magenta in **Figure 7G**) reached activation threshold exclusively when it received input from both the P and the T cell. Since it was located in the posterior package and was not activated by P cell input alone, this cell did not correspond to any of the known local bend interneurons (Lockery and Kristan, 1990b). In addition to the T cell (number 1) itself one cell (number 18, blue in **Figures 7G,I**), putatively interneuron 161, needed T cell but no P cell stimulation for activation. Interestingly, three cells [numbers
<figure><img src="image_203.png" /><figcaption>**FIGURE 7** | Identification of interneurons involved in the processing of tactile stimuli based on VSD recordings. (A) High resolution image of VSD labeled cells in the leech ganglion. For typical positions of the cell bodies located on the ventral surface of the ganglion, refer to **Figure 1B**. Intracellular electrodes (symbolized by white pointed angles) were used for electrical stimulation of a T cell (Electrode #1) and a P cell (Electrode #2). (B) One frame of the VSD recorded ganglion with superimposed blue cell borders showing all 55 ROIs used for analysis. White numbers refer to the order of cells' activation determined in (D), not to the cell identity numbers commonly used in the standard ganglion map. (C) Activity map of all 55 recorded cells in response to control condition (no stimulation). The color of each pixel indicates in how many of the seven control trials the activity of a specific cell (row) at a specific recording frame (column) deviated significantly from baseline. Colors range from dark blue (0 deviations) to yellow (7 deviations). The absence of bright colors indicates that no consistent deviation from baseline occurred for any of the cells. Cell numbers correspond to (B,D). (D) Activity map in response to intracellular current stimulation of a P cell and a T cell (stimulus time courses shown above in red and blue). Cells were sorted and numbered by the timing of the first occurrence of consistent significant deviation (>5 of 7 trials) from baseline in this condition after stimulus onset (T cell is #1, P cell is #2). Cells not activated by the PT-stimulated condition remained in random order. For cell body locations see (B). (E,F) Activity maps in response to intracellular current stimulation of only the P cell (E) or the T cell (F). Cell numbers correspond to (B,D). (G-I) Cells activated by the specific stimulus conditions, P and T cell stimulation (G), only P cell stimulation (H), and only T cell stimulation (I). A cell was defined as stimulus activated, if its activity deviated significantly from baseline in more than 5 of 7 trials, in at least one time frame after stimulus onset (see Methods). Red ROIs show cells activated only during P stimulated condition, yellow ROIs during P and PT conditions, blue during T and PT conditions, magenta only during PT condition, cyan during all three conditions.</figcaption></figure>
26, 32, 42 (Figure 7H red), cell types remained to be identified] showed significant activity during P cell stimulation, but not in response to the combined PT-stimulated condition. This finding could indicate nonlinear interaction of inputs from different mechanoreceptors or inhibition by the T cell.

According to our classification criterion, eight cells responded with consistent significant activation to all three stimulated conditions (cyan borders in **Figures 7G-I**). Some of them were easy to identify by soma positions and sizes and by their characteristic response patterns: Both Retzius cells (numbers 6 and 13 in **Figure 7**) and both AP cells (numbers 4 and 8 in **Figure 7**) could be identified unambiguously across different preparations. The fastest postsynaptic responding cell, marked with number 3, could be the ipsilateral interneuron 162 (compare **Figures 5, 6**). The cell labeled with number 10 could putatively be interneuron 212 (compare **Figure 6D** and the ganglion map in **Figure 1B**). Cells labeled 12 and 17, which also were activated in all three stimulated conditions, still remained to be identified.

Some of the interneurons identified by the study of Lockery and Kristan (1990b) could correspond to the cells activated by PT- and P-stimulated conditions (cyan borders in **Figures 7G,H**). In particular, judging by position, the cells labeled with numbers 11 and 20 could correspond to the interneurons 157 and 159 analyzed in this study. These cells were not found in the map of the activated cells in the T-stimulated condition shown in **Figure 7I** even though the connection to a T cell was demonstrated both electrophysiologically (**Figure 5**) as well as anatomically (**Figure 6**). However, this discrepancy seemed to be due to the very strict criterion for the classification of T-condition activated cells requiring precisely timed and strong deviation from baseline activity in at least six of the seven trials. Since interneuron response amplitudes were small and VSD signal-to-noise ratio was low this criterion provided a conservative estimate of cells showing very clear responses. When this criterion was relaxed by a lower significance level or a lower number of significant trials used as threshold, more cells, including the interneurons under study, were classified as activated (results not shown). In future studies, network activation patterns obtained for varied classification criteria need to be compared across different preparations to reveal all members in the network and the interaction of different types of mechanosensory inputs.

DISCUSSION

After more than 30 years of research on the local bend reflex of the leech (Kristan, 1982), the perspective on the neuronal network controlling this seemingly simple behavior still gains complexity. In line with other recent findings (Gaudry and Kristan, 2009; Palmer et al., 2014; Baljon and Wagenaar, 2015; Pirschel and Kretzberg, 2016) the results of this study indicate that the model of the local bend network needs to be revised regarding the input signals provided by mechanoreceptors and the computation performed by interneurons.

Encoding of Tactile Information by
Mechanoreceptors

On the level of mechanoreceptors our results suggest that three dogmata of leech tactile information processing should be revised:

Contrary to the common belief (and the cell's names), the three types of mechanoreceptors—touch, pressure and noxious cells—do not implement a labeled line code for tactile stimulus intensities. The ranges of constant pressure intensities encoded by these cell types overlapped quite substantially, with longer stimulus durations leading to higher spike counts (Pirschel and Kretzberg, 2016). T and P cells both reacted to the entire range of tested intensities from very light touch (5 mN) to moderate pressure (200 mN) with increasing spike counts and decreasing response latencies. In both cell types N cells generated spikes in response to moderate stimulus intensities. Even though the relatively weak tactile stimuli used in this study were clearly not in the optimal range for N cell stimulation, they elicited weak but reliable N cell responses. Indeed, a large range of intensities triggered spikes in all three cell types and also N cells might contribute to the local bend network by providing additional input to interneurons.

Despite the finding that T cell spikes increase muscle tension during the local bend response reported already in the first publication on leech local bending (Kristan, 1982) their contribution to the network was disregarded in most studies. Electrical stimulation of a single P cell was sufficient to elicit a local bend response, while a single T cell often failed to trigger an obvious muscle movement. It was therefore concluded that the local bend network relies on P cell rather than T cell input (Kristan, 1982; Lewis and Kristan, 1998b). However, recent results suggest that T cells encode tactile stimulus properties by relative response features of a cell pair with overlapping receptive fields (Pirschel and Kretzberg, 2016). Thus, electrical stimulation of a single T cell triggers a response that would not occur in natural situations. Each patch of skin is innervated by a pair of T cells and a pair of P cells with overlapping receptive fields. They all respond to tactile stimulation at this location (see **Figure 1A** bottom for a sketch of overlapping receptive field at ventral midline). Hence, even if spikes of a single stimulated cell fail to elicit the local bend response it cannot be concluded that this cell is not important for the response. Carlton and McVean (1995) pointed out that T cells provide behaviourally important input to the leech nervous system, in particular when acting as velocity detectors in exploration behavior. In a study comparing T and P cell encoding (Pirschel and Kretzberg, 2016), T cell responses were shown to allow higher percentages of tactile stimulus location estimation than P cell responses. Moreover, mixed populations of P and T cells considerably improved the combined estimation of stimulus location and intensity compared to each cell type separately. Here, we showed with three complementary methods that T cells provide synaptic input to several previously identified local bend interneurons (Figures 5-7). Hence, T cells should be considered as additional members of the local bend network.
As in most neuronal systems, the analysis of leech mechanoreceptor responses was restricted to spike counts of single cells for many decades. However, more recent studies showed that combining responses of two cells with overlapping receptive fields drastically improves stimulus estimation and that temporal response features contain more information about stimulus location than spike counts (Thomson and Kristan, 2006; Pirschel and Kretzberg, 2016). This study confirmed that spike counts and response latencies depended both on stimulus intensity and location for $T_v$ and $P_v$ cells and showed similar dependencies also for N cell responses (Figures 3, 4). Moreover, combined variation of stimulus location and intensities revealed that the dependency of both response features on stimulus location stayed the same for different stimulus intensities, leading to parallel shifted tuning curves in Figures 4C,D. Since $T_v$ cells produced transient responses at stimulus on- and offset, encoding properties of off-responses occurring after stimulus offset were additionally analyzed. Interestingly, the off-response spike count showed a much stronger dependency on stimulus force intensity than on location—at least for light and moderate tactile stimulation. In contrast, off-response latency depended almost exclusively on stimulus location. This finding suggests that T cell off-spikes could play an additional role in tactile information encoding that should be considered in future studies. For primate afferents, on-off-response patterns were proposed to play a role in the encoding of object contact and release during active touching (Johnson, 2001; Johansson and Flanagan, 2009). Hence, the importance of T cells during exploration (Carlton and McVean, 1995)—actively touching the environment—might indicate a general mechanism of tactile stimulus encoding shared by man and worm.

Taken together, these results suggest that encoding of tactile stimulation on the mechanoreceptor level can be explained neither by a labeled line of different cell types encoding distinct ranges of mechanical force, nor by symmetrical spike count tuning curves representing stimulus location. Instead, we propose a mixed-type population of mechanoreceptors performing simultaneous encoding of stimulus location and intensity by multiplexing temporal response features and spike counts. Since mixed-type combination of multiple afferent classes and multiplexed encoding of several stimulus properties were also proposed as underlying mechanisms of touch perception in primates (Saal and Bensmaia, 2014), these encoding principles might be fundamental mechanisms of tactile information processing. For the leech, future studies are needed to investigate how additional stimulus properties like probe shape and velocity are represented in this mixed-type, multiplexed coding scheme.

## Interneurons Involved in Tactile Information Processing

Any sensory system relies on receptors conveying all available information about the stimulus to the next network level. In many systems, including the mechanoreceptors of primates (Saal and Bensmaia, 2014) and leeches (Nicholls and Baylor, 1968; Carlton and McVean, 1995), this input layer of the sensory processing network contains different receptor types (Smith and Lewin, 2009), which specifically react to certain types of stimulation. However, for exploiting the information about

the stimulus encoded by receptors, this information must be transferred to and processed by the next network layers. While it is difficult to study directly connected pre- and postsynaptic cells in complex sensory systems in vertebrates like the primate, the individually characterized cells in the simple nervous system of the leech are optimally suited for this question.

As discussed in section Encoding of Tactile Information by Mechanoreceptors, our hypothesis is that the individual sensory cells send multiplexed signals, containing a combination of temporal response features and spike rate, which simultaneously represent multiple stimulus properties. The ensemble of interneurons has the task to integrate and process these ambiguous signals coming from the 10 mechanoreceptors (6 T, 4 P, 2 N), which are present in each ganglion. Our preliminary results suggest that the individual interneurons have spatial receptive fields as was also found by Lockery and Kristan (1990b), but additionally differ in their integration properties. At least one type of interneuron (cell 157, Figure 5A) seemed to act as slow integrator, presumably reacting mainly to the spike count of all presynaptic cells. The membrane potentials of other interneurons (cells 159 and 162, Figures 5B,C) showed more complex temporal response structures, suggesting temporal information processing, e.g., as coincidence detectors. Furthermore, these results indicate that responses of individual interneurons could be influenced to different extents by responses of the three mechanosensory cell types. While the responses of slow integrators probably follow mainly the sustained P cell spikes, the more complex interneuron response patterns could stem from the transient T cell responses to stimulus changes and the sparse N cell spikes.

Interneuron responses found in this study matched and complemented previous findings. The three interneurons considered here in more detail, cells 157, 159, 162, were identified as local bend interneurons, receiving P cell input and influencing motor neuron activity (Lockery and Kristan, 1990b). Judging by locations of cells' somata, all of these three interneurons also significantly changed their membrane potentials when a P cell was stimulated in our voltage sensitive dye recordings (Figures 7G,H). Furthermore, our physiological and anatomical results (Figures 5, 6) showed that these three cells also receive input from T cells.

In addition to these three interneurons, which we chose to study in detail, our results showed several other cells receiving mechanoreceptor inputs, confirming results from previous studies. Judging by location of their cell bodies, our VSD experiments yielded at least two more previously identified local bend interneurons, cells 161 and 212 (Lockery and Kristan, 1990b), reacting to T-cell stimulation (Figure 7I). Cell 212 might also be one of the cells visible in the Neurobiotin staining of a T cell, indicating electrical coupling (Figure 6D). Another interneuron, cell 61, for which we found a putative electrical coupling to the Neurobiotin-filled T cell and an activation in the VSD experiments, also was reported before to receive mechanoreceptor input (Nusbaum and Kristan, 1986). Activity of this serotonin-containing cell was associated with modification of the local bend behavior and initiation of swimming (Nusbaum and Kristan, 1986; Kristan et al., 1988; Lockery and Kristan, 1991). Moreover, the activation of Retzius and AP cell pairs in
our VSD experiments (Figures 7G-I) was also consistent with previous findings that both cell types react to mechanoreceptor responses and pressure applied to the skin (Zhang et al., 1990; Lockery and Kristan, 1991; Zhang et al., 1995; Jin and Zhang, 2002; Fathiazar et al., 2016).

Despite this updated list of candidate cells revealed in this study, we assume that not all interneurons involved in processing of tactile information showed up as stimulus activated cells in the VSD experiments (Figures 7G-I), because of three technical reasons: (1) the restricted visibility of cells in preparations, (2) the statistical selection criterion, and (3) the type of stimulation used in this study.

Firstly, visibility of cells in VSD recordings varies from preparation to preparation. VSD experiments require removal of the glia sheath from the ganglion to ensure that the dye reaches all neuronal membranes. However, this procedure led to displacement of the cell bodies. Some cell bodies moved out of focus of the microscope. Proximate cells, which are usually well visible in the ganglion before de-sheathing, might overlap or even completely occlude each other after that dissection procedure. These effects led to a lower number of ROIs (55 in Figure 7B) visible in the VSD images than cells located at the ventral side of the ganglion (approximately 200). Moreover, even though the positions of cell bodies in the ganglion are relatively fixed, they sometimes switch positions, requiring additional physiological or anatomical evidence for definite cell type classification. Hence, it is of general concern that not all stimulus-activated interneurons can be found in all VSD preparations.

The second reason for the low number of interneurons classified as stimulus-activated (in particular for separate T-cell stimulation, Figures 7F,I) is the strict criterion we applied. A cell's activity needed to deviate significantly (α = 0.05) from baseline activity in at least in six out of seven stimulated trials in exactly the same frame. Hence, in this time frame the cell had to be consistently more depolarized or more hyperpolarized than 97.5% of the values obtained under control conditions. Relaxing this criterion led to a higher number of cells classified as stimulus-activated. Example, for a level of α = 0.1 and the same threshold (six out of seven active trials), 40 cells were marked as stimulus-activated in the PT-activated condition, 30 cells in the P-activated and 28 in the T-activated conditions (results not shown). Judged by location, these populations included the three interneurons 157, 159, 162 that we studied in more detail and also several other interneurons previously identified as members of the local bend network (Lockery and Kristan, 1990b). However, since many additional cells were also classified as activated, we decided to present strictly restricted populations of clearly stimulus-activated cells in this study. In future studies, effects of statistical selection criteria should be compared across preparations to optimize the detection of stimulus-activated cells, which would lead to a more consistent picture of the network for tactile information processing.

The third reason for the incomplete activation maps could be the stimulation used in the experiments presented here. Even though the electrical stimulation of the P and/or T cell elicited spike trains mimicking typical responses to tactile skin stimulation (Pirschel and Kretzberg, 2016), the network received inputs from only one or two mechanoreceptors. In contrast,

tactile stimulation always elicits responses of at least four mechanoreceptors, because each patch of the skin is covered by the overlapping receptive fields of two P cells and two T cells. For higher stimulus intensities, at least one N cell will react additionally. Since our VSD setup was limited to two intracellular electrodes, a complete simulation of the natural input to the tactile network by intracellular stimulation of four (or five) mechanoreceptors was not possible. Hence, if some interneurons are specifically tuned to relative temporal features of mechanoreceptor spike trains, e.g., coincidence detection they would not (or at least not optimally) respond to electrical stimulation of one P and one T cell, even though the timing of their spikes matches realistic skin stimulation. Hence, additional VSD experiments are needed with the skin attached to the ganglion to reveal a more complete network structure. Comparison of activity maps obtained for tactile stimulation to electrical stimulation of mechanoreceptor pairs or single mechanoreceptors can test our hypothesis of temporal processing on the level of interneurons.

Once these issues will be settled, combined electrophysiological, anatomical, and VSD studies applied to this small nervous system consisting of individually characterized cells can yield conclusive answers to fundamental questions of neural coding including the roles of spike counts versus spike timing, population coding and multiplexing. In particular, the analysis of combined encoding of multiple stimulus properties should be extended to a larger space of stimulus dimensions (e.g., velocity, shape, application angle, indentation depth, vibration, duration additionally to location and intensity). Moreover, the local bend response was reported to be modulated by feedback-loops in the network (Baljon and Wagenaar, 2015), by neuromodulators (Lockery and Kristan, 1991; Gaudry and Kristan, 2009), as well as by feeding status and environmental factors like water depth (Palmer et al., 2014). Hence, despite the low number of neurons involved in this seemingly so hard-wired network, the leech tactile system is also well suited for studies on general mechanisms underlying the flexibility of neural activity and behavior.

## AUTHOR CONTRIBUTIONS

All authors contributed to data analysis, interpretation of results, writing the manuscript and designing the figures. In addition, JK designed and coordinated the studies and drafted the text; FP performed intracellular recordings and skin stimulation, EF and GH performed VSD experiments, GH performed cell staining and confocal microscopy.

## FUNDING

Funding was provided by "Professorinnenprogramm" of Bundesministerium für Bildung und Forschung/ Niedersächsisches Ministerium für Wissenschaft und Kultur (JK, GH, FP), by a fellowship of the graduate school "Neurosenses" of Niedersächsisches Ministerium für Wissenschaft und Kultur (FP), and by a fellowship of German Academic Exchange Service (EF).
ACKNOWLEDGMENTS

We thank William Kristan and Paxon Frady for teaching how
to perform VSD experiments and leech skin preparation to
FP and GH and Daniel Wagenaar for sharing his construction
plans and software for the VSD setup, as well as Evan Miller
for providing the dye. Thanks to Go Ashida and all members
of the computational neuroscience group for critically reading

the manuscript and to Lena Koepcke for literally fruitful
discussions.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found
online at: http://journal.frontiersin.org/article/10.3389/fphys.
2016.00506/full#supplementary-material

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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 Kretzberg, Pirschel, Fathiazar and Hilgen. 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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