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

This source preferred by Rick Stafford

Authors: Stafford, R. and Rind, F.C.

Journal: Neurocomputing

Volume: 70

Pages: 1079-1084

ISSN: 0925-2312

DOI: 10.1016/j.neucom.2006.09.011

This data was imported from Scopus:

Authors: Stafford, R. and Claire Rind, F.

Journal: Neurocomputing

Volume: 70

Issue: 4-6

Pages: 1079-1084

ISSN: 0925-2312

DOI: 10.1016/j.neucom.2006.09.011

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. © 2006 Elsevier B.V. All rights reserved.

This data was imported from Web of Science (Lite):

Authors: Stafford, R. and Rind, F.C.

Journal: NEUROCOMPUTING

Volume: 70

Issue: 4-6

Pages: 1079-1084

ISSN: 0925-2312

DOI: 10.1016/j.neucom.2006.09.011

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