Letters: Data mining neural spike trains for the identification of behavioural triggers using evolutionary algorithms

Authors:
Richard Stafford;F. Claire Rind
Affiliations:
School of Biology and Psychology, Ridley Building, University of Newcastle-upon-Tyne, NE1 7RU, UK;School of Biology and Psychology, Ridley Building, University of Newcastle-upon-Tyne, NE1 7RU, UK
Venue:
Neurocomputing
Year:
2007

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Abstract

We analysed spike trains from the descending contralateral movement detector (DCMD) neuron of locusts. The locusts either performed jumps or did not jump in response to visual looming stimuli. An evolutionary algorithm (EA) was employed to sort spike trains into the correct behavioural categories by optimising threshold parameters, so jump behaviour occurred if the spike-train data exceeded the threshold parameters from the EA. A candidate behavioural trigger appeared to be prolonged high-frequency spikes at a relatively early stage in the approach of the stimulus. This technique provides a useful precursor to a full biological analysis of the escape jump mechanism.