REVIEW ARTICLE published: 02 July 2012 doi: 10.3389/fnins.2012.00095 Bursting neurons and ultrasound avoidance in crickets Gary Marsat 1,2* and Gerald S. Pollack 3 1 Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada 2 Center for Neural Dynamics, University of Ottawa, Ottawa, ON, Canada 3 Department of Biology, McGill University, Montreal, QC, Canada Edited by: Björn Brembs, Freie Universität Berlin, Germany Reviewed by: Philippe N. Tobler, University of Zurich, Switzerland Shunsuke Kobayashi, Fukushima Medical University, Japan *Correspondence: Gary Marsat, Department of Cellular and Molecular Medicine, University of Ottawa, 451 Smyth Road, Ottawa, Canada, K1H8M5. e-mail: gmarsat@gmail.com Decision making in invertebrates often relies on simple neural circuits composed of only a few identiﬁed neurons. The relative simplicity of these circuits makes it possible to identify the key computation and neural properties underlying decisions. In this review, we summa- rize recent research on the neural basis of ultrasound avoidance in crickets, a response that allows escape from echolocating bats. The key neural property shaping behavioral output is high-frequency bursting of an identiﬁed interneuron, AN2, which carries information about ultrasound stimuli from receptor neurons to the brain. AN2’s spike train consists of clusters of spikes – bursts – that may be interspersed with isolated, non-burst spikes. AN2 ﬁring is necessary and sufﬁcient to trigger avoidance steering but only high-rate ﬁring, such as occurs in bursts, evokes this response. AN2 bursts are therefore at the core of the com- putation involved in deciding whether or not to steer away from ultrasound. Bursts in AN2 are triggered by synaptic input from nearly synchronous bursts in ultrasound receptors. Thus the population response at the very ﬁrst stage of sensory processing – the auditory receptor – already differentiates the features of the stimulus that will trigger a behavioral response from those that will not. Adaptation, both intrinsic to AN2 and within ultrasound receptors, scales the burst-generating features according to the stimulus statistics, thus ﬁl- tering out background noise and ensuring that bursts occur selectively in response to salient peaks in ultrasound intensity. Furthermore AN2’s sensitivity to ultrasound varies adaptively with predation pressure, through both developmental and evolutionary mechanisms. We discuss how this key relationship between bursting and the triggering of avoidance behav- ior is also observed in other invertebrate systems such as the avoidance of looming visual stimuli in locusts or heat avoidance in beetles. Keywords: burst, feature extraction, insect ﬂight, phonotaxis, predator-avoidance, sensory coding BURSTING AS A NEURAL CODE Behavior, including decision making, is an inherently temporal process so it goes without saying that the underlying neural activ- ity is also temporally patterned. The study of the activity patterns of neurons has revealed clear links between spiking patterns and functions of neurons (Gerstner et al., 1997; Rieke, 1997; Decharms and Zador, 2000). As one of the most easily recognizable spik- ing patterns, bursting has been suggested to play a crucial role in many systems (Connors and Gutnick, 1990; Gabbiani et al., 1996; Martinez-Conde et al., 2002; Marsat and Pollack, 2006; Schwartz et al., 2007a; Lin and Nicolelis, 2008; Marsat et al., 2009). Bursts are clusters of two or more spikes occurring in quick succession separated from other spikes by longer inter-spike intervals (ISI). Several techniques can be used to determine objectively if a cell is bursting and which spikes are parts of bursts (Cocatre-Zilgien and Delcomyn, 1992; Bastian and Nguyenkim, 2001). Such analyses are particularly important to differentiate cells that are simply ﬁring at high rates or responses with clustered spikes caused by a pul- satile stimulus from truly bursting cells in the narrow sense of the term. The simplest methods, based on ISIs histograms or spike- train autocorrelation functions, identify the range of ISIs typical of bursts (Figure 1). As shown in Figure 1, bursting neurons have speciﬁc spiking patterns that reﬂect the presence of a non-linear transformation between input and output (Chacron et al., 2004). We will not discuss the cellular mechanisms underlying bursting as several reviews are available on the subject (Krahe and Gabbiani, 2004; Izhikevich, 2006, 2007). Studying spiking patterns became crucial once it was real- ized that information was contained not only in the mean ﬁring rate of a neuron but also in the detailed temporal structure of its spike train (Gerstner et al., 1997; Rieke, 1997; Eggermont, 1998; Borst and Theunissen, 1999). The instantaneous ﬁring rate of a neuron can sometimes be linearly related to the input signal but the spiking pattern of bursting neurons relates non- linearly to the input signal (Chacron et al., 2004; Marsat and Pollack, 2004; Lesica et al., 2006). Studies in several sensory systems showed that bursting neurons act as feature detectors, where the occurrence of speciﬁc patterns of stimulation results in bursts (Gabbiani et al., 1996; Lesica et al., 2006; Marsat and Pollack, 2006). Sensory bursts are often described as units of neuronal information that exhibit several advantages over sin- gle spikes (i.e., spikes that are not part of a burst) from the point of view of information coding and neural dynamics (see last section). www.frontiersin.org July 2012 | Volume 6 | Article 95 | 1