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Resources for stimulus-response function modelling

 

Review (2016)

Models of neuronal stimulus-response functions: elaboration, estimation and evaluation

Arne F. Meyer, Ross S. Williamson, Jennifer F. Linden, and Maneesh Sahani
Front. Syst. Neurosci.
doi: 10.3389/fnsys.2016.00109

Abstract
Rich, dynamic, and dense sensory stimuli are encoded within the nervous system by the time-varying activity of many individual neurons. A fundamental approach to understanding the nature of the encoded representation is to characterise the function that relates the moment-by-moment firing of a neuron to the recent history of a complex sensory input. This review provides a unifying and critical survey of the techniques that have been brought to bear on this effort thus far — ranging from the classical linear receptive field model to modern approaches incorporating normalisation and other nonlinearities. We address separately the structure of the models; the criteria and algorithms used to identify the model parameters; and the role of regularising terms or "priors". In each case we consider benefits or drawbacks of various proposals, providing examples for when these methods work and when they may fail. Emphasis is placed on key concepts rather than mathematical details, so as to make the discussion accessible to readers from outside the field. Finally, we review ways in which the agreement between an assumed model and the neuron's response may be quantified. Re-implemented and unified code for many of the methods, and example data sets, are made freely available.

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Software

Python repository for neuronal stimulus-response function estimation.