Marsat and Pollack Bursting and ultrasound avoidance becomes obvious in a growing number of cases. In locusts, the presence of a looming stimulus is detected through sophisticated computation carried out at the level of the sensory neuron LGMD (Fotowat and Gabbiani, 2011) which triggers an escape response (Fotowat et al., 2011). LGMD exhibits intrinsic bursting properties and strong spike-frequency adaptation. LGMD is pre-synaptic to the descending neuron DCMD, which provides the motor com- mand. DCMD mirrors LGMD’s spiking pattern; thus LGMD’s bursting properties are likely to have a crucial inﬂuence on behav- ior. Heat sensitive neurons of the beetle provide another example of a probable link between sensory bursts and behavior in inverte- brates (Must et al., 2010). In this case burst timing does not relate to the temporal features of the stimulus; rather, burst rate indicates the presence of dangerously high temperatures that the beetle tries to avoid. Examples of the relevance of sensory bursts for behavior also abound in vertebrate model systems. Synchronized bursts in a subset of cells in a sensory processing area signal the occurrence of aggressive communication signals in electric ﬁsh, whereas in a different subset of cells, bursts code for prey (Gabbiani et al., 1996; Oswald et al., 2004; Marsat et al., 2009; Marsat and Maler, 2010, 2012). In the retina and the lateral geniculate nucleus of mammals, bursts also reliably signal the occurrence of speciﬁc behaviorally relevant features of visual inputs such as a sharp contrast or motion reversal (Lesica and Stanley, 2004; Schwartz et al., 2007b). Thus in many model systems and modalities, sensory bursts seem to serve similar purposes: extracting from the sensory environment fea- tures that are of particular relevance to the organism and signaling their occurrence reliably to post-synaptic networks. Burst coding is well suited to this task. Bursts can be seen as self- contained units of information because they capture, with high reliability, the timing and the identity of speciﬁc stimulus features, making this information readily available to decoding circuits. This property is particularly advantageous when a signal must trigger a response very quickly, as in escape behaviors. Also, the fact that bursts consist of several closely spaced spikes suggests that they will have strong effects on post-synaptic cells, where a simple threshold mechanism could separate relevant signals encoded by bursts from background noise. Moreover, frequency-dependent mechanisms, such as facilitation, depression, or resonance, can allow post-synaptic cells to respond differently to bursts and to non-burst spikes (Izhikevich et al., 2003). It is known for example that burst-induced facilitation can lead to an increase in the reli- ability of synaptic transmission (Lisman, 1997; see also: Harvey- Girard et al., 2010; Bol et al., 2011). Therefore bursts can carry reliable information quickly and efﬁciently to the next stage of processing. On the other hand bursts might be limited in their capacity to carry information in their internal structure about ﬁne-scale variations in the feature that they represent. In partic- ular we note that because intra-burst ISIs are small, a decoder would have to be sensitive to minute differences to detect varia- tions and might thus be more sensitive to noise (Avila-Akerberg and Chacron, 2011). Furthermore, the internal structure of bursts is often determined by neuron-intrinsic burst-generating mecha- nisms rather than by subtle stimulus features, thus limiting their coding capacity. In conclusion we suggest that, because of the properties out- lined above, bursts are frequently observed in networks involved in behaviors requiring quick stereotyped responses. In crickets, the use of bursts as an indicator of impending danger allows the decision to take evasive action to be made very early in the sensory system, promoting reliable and rapid responses. We argue that the same strategy may be used by other systems as a way to reduce the need for temporally costly decision networks. REFERENCES Avila-Akerberg, O., and Chacron, M. (2011). In vivo conditions inﬂuence the coding of stimulus features by bursts of action potentials. J. Com- put. Neurosci. 31, 369–383. Bastian, J., and Nguyenkim, J. (2001). Dendritic modulation of burst-like ﬁring in sensory neurons. J. Neuro- physiol. 85, 10–22. Bol, K., Marsat, G., Harvey-Girard, E., Longtin, A., and Maler, L. (2011). Frequency-tuned cerebellar chan- nels and burst-induced ltd lead to the cancellation of redundant sensory inputs. J. Neurosci. 31, 11028–11038. Borst, A., and Theunissen, F. E. (1999). Information theory and neural coding. Nat. Neurosci. 2, 947–957. Boyan, G. S. (1980). Auditory neurones in the brain of the cricket Gryl- lus bimaculatus (De Geer). J. Comp. Physiol. A 140, 81–93. Boyan, G. S. (1981). Two-tone sup- pression of an identiﬁed auditory www.frontiersin.org neurone in the brain of the cricket Gryllus bimaculatus (De Geer). J. Comp. Physiol. A 144, 117–125. Boyan, G. S., and Williams, J. L. D. (1981). Descending interneurones in the brain of the cricket. Naturwis- senschaft 68, 486–487. Brodfuehrer, P. D., and Hoy, R. R. (1990). Ultrasound sensitive neu- rons in the cricket brain. J. Comp. Physiol. A 166, 651–662. Brodfuhrer, P. D., and Hoy, R. R. (1989). Integration of ultrasound and ﬂight inputs on descending neurons in the cricket brain. J. Exp. Biol. 145, 157–171. Butts, D. A., Desbordes, G., Weng, C., Jin, J. Z., Alonso, J. M., and Stanley, G. B. (2010). The episodic nature of spike trains in the early visual pathway. J. Neurophysiol. 104, 3371–3387. Chacron, M. J., Longtin, A., and Maler, L. (2004). To burst or not to burst? J. Comput. Neurosci. 17, 127–136. Cocatre-Zilgien, J. H., and Delcomyn, F. (1992). Identiﬁcation of bursts in spike trains. J. Neurosci. Methods 41, 19–30. Connors, B. W., and Gutnick, M. J. (1990). Intrinsic ﬁring patterns of diverse neocortical neurons. Trends Neurosci. 13, 99–104. Decharms, R. C., and Zador, A. (2000). Neural representation and the cor- tical code. Annu. Rev. Neurosci. 23, 613–647. Edwards, D. H., Heitler, W. J., and Krasne, F. B. (1999). Fifty years of a command neuron: the neu- robiology of escape behavior in the crayﬁsh. Trends Neurosci. 22, 153–161. Eggermont, J. J. (1998). Is there a neural code? Neurosci. Biobehav. Rev. 22, 355–370. Fotowat, H., and Gabbiani, F. (2011). Collision detection as a model for sensory-motor integration. Annu. Rev. Neurosci. 34, 1–19. Fotowat, H., Harrison, R. R., and Gab- biani, F. (2011). Multiplexing of motor information in the discharge of a collision detecting neuron during escape behaviors. Neuron 69, 147–158. Fullard, J. H., Dawson, J. W., and Jacobs, D. S. (2003). Auditory encoding dur- ing the last moment of a moth’s life. J. Exp. Biol. 206, 281–294. Fullard, J. H., Ratcliffe, J. M., and Guignion, C. (2005). Sensory ecol- ogy of predator-prey interactions: responses of the AN2 interneuron in the ﬁeld cricket, Teleogryllus oceani- cus to the echolocation calls of sym- patric bats. J. Comp. Physiol. A 191, 605–618. Fullard, J. H., Ter Hofstede, H. M., Ratcliffe, J. M., Pollack, G. S., Brigidi, G. S., Tinghitella, R. M., and Zuk, M. (2010). Release from bats: genetic distance and sensoribehav- ioural regression in the Paciﬁc ﬁeld cricket, Teleogryllus oceanicus. Naturwissenschaften 97, 53–61. Gabbiani, F., Metzner, W., Wessel, R., and Koch, C. (1996). From stimu- lus encoding to feature extraction in weakly electric ﬁsh. Nature 384, 564–567. July 2012 | Volume 6 | Article 95 | 7