Abstract:
Sharks detect their prey using an extremely sensitive electrosensory system which is capable of distinguishing weak external stimuli from a relatively strong background noise generated by the animal’s own breathing. Experiments indicate that an organ within the shark’s hindbrain, the dorsal octavolateralis nucleus (DON), is responsible for extracting the external stimulus using an adaptive filter mechanism which suppresses any signal correlated with the shark’s breathing motion. The DON’s principal neuron integrates input from a single electroreceptor as well as many thousands of parallel fibres transmitting breathing-correlated motor command signals. There are a number of models in the literature, studying how this adaptive filtering mechanisms occurs, but most of them are based on a spike-train model approach (in which only the frequency of the spike train is monitored). This paper presents a biophysically-based computational simulation which demonstrates a mechanism for adaptive noise filtering in the DON. A spatial model of the neuron uses the Hodgkin-Huxley equations to simulate the propagation of action potentials along the dendrites. Synaptic inputs are modelled by applied currents at various positions along the dendrites, whose input conductances are varied according to a simple learning rule. Simulations results show that the model is able to demonstrate adaptive filtering in agreement with previous experimental and modelling studies. Furthermore, the spatial nature of the model does not greatly affect its learning properties, and it is effectively equivalent to an isopotential model which does not incorporate a spatial element.
