title: Channel identification machines for multidimensional receptive fields.
abstract: 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.
authors.affiliation: Bionet Group, Department of Electrical Engineering, Columbia University in the City of New York New York, NY, USA.
authors.forename: Aurel A
authors.initials: AA
authors.lastname: Lazar
authors.affiliation: Bionet Group, Department of Electrical Engineering, Columbia University in the City of New York New York, NY, USA.
authors.forename: Yevgeniy B
authors.initials: YB
authors.lastname: Slutskiy
keywords: RKHS; biophysical neuron models; channel identification machines; multidimensional receptive fields; spiking neural circuits; system identification; time encoding