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Analysis of Neural Data

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Integrator or Coincidence Detector - a Novel Measure Based on the Discrete Reverse Correlation to Determine a Neuron's Operational Mode
Citation key Kanev2016
Author Kanev, J. and Koutsou, A. and Christodoulou, C. and Obermayer, K.
Pages 1-38
Year 2016
DOI 10.1162/NECO_a_00875
Journal Neural Computation
Volume 28
Publisher MIT Press
Abstract In this letter, we propose a definition of the operational mode of a neuron, that is, whether a neuron integrates over its input or detects coincidences. We complete the range of possible operational modes by a new mode we call gap detection, which means that a neuron responds to gaps in its stimulus. We propose a measure consisting of two scalar values, both ranging from -1 to +1. The neural drive, which indicates whether its stimulus excites the neuron, serves as background noise, or inhibits it; the neural mode, which indicates whether the neuron's response is the result of integration over its input, of coincidence detection, or of gap detection, with all three modes possible for all neural drive values. This is a pure spike-based measure and can be applied to measure the influence of either all or subset of a neuron's stimulus. We derive the measure by decomposing the reverse correlation, test it in several artificial and biological settings, and compare it to other measures, finding little or no correlation between them. We relate the results of the measure to neural parameters and investigate the effect of time delay during spike generation. Our results suggest that a neuron can use several different modes simultaneously on different subsets of its stimulus to enable it to respond to its stimulus in a complex manner.
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