Abstract
Weakly electric fish generate an electric field, called electric organ discharge (EOD), that they use for active electrosensation. This system is used for both object localisation and electrocommunication. Both, objects that are close to the fish and the EODs of other nearby electric fish, modulate the amplitude of a fish’s EOD. Localisation signals are low in amplitude and frequency whereas electrocommunication signals are large amplitude signals with higher frequencies. Electroreceptor neurons are tuned to the frequency of the fish’s own EOD. This tuning, however, is rather broad to allow for the reception of EODs of other fish with different frequencies. This is the basis for electrocommunication. Spike trains of electroreceptor afferents are surprisingly noisy even in the absence of any external signal. From theoretical studies it is known that in populations of spiking neurons such internal noise can improve the information carried about a common input signal in comparison to the noiseless case. In particular, the processing of high-frequency signals benefits from internal noise and the convergence of large populations of neurons. The target neurons of the electroreceptor afferents, the pyramidal cells in the electrosensory lateral line lobe, are organised in three distinct maps of the electroreceptive body surface that are characterised by different receptive field sizes, i.e. the number of afferents that converge on them, and frequency tuning. The properties of these three maps can be understood based on the differential impact of the noise in the electroreceptor afferent spike trains on the processing of the distinct types of signals arising in localisation and communication contexts. Further, the noise in the electroreceptors allows for the discrimination of synchronous spikes from all spikes fired by the afferent population. The level of synchrony seems particularly important for encoding high-frequency communication signals. The electrosensory system is thus a showcase for demonstrating how neural systems actually use noise to enhance processing of signals.
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