Channel Identification Machines for Multidimensional Receptive Fields

A.A. Lazar and Y.B. Slutskiy, Frontiers in Computational Neuroscience. DOI: 10.3389/fncom.2014.00117, September 2014.

We present algorithms for identifying multidimensional receptive fields directly from spike trains produced by biophysically-grounded neuron models. We demonstrate that only the projection of a receptive field onto the input stimulus space may be perfectly identified and derive conditions under which this identification is possible. We also provide detailed examples of identification of neural circuits incorporating spatiotemporal and spectrotemporal receptive fields.


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