Neural Comput - Functional identification of spike-processing neural circuits.

Tópicos

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Resumo

We introduce a novel approach for a complete functional identification of biophysical spike-processing neural circuits. The circuits considered accept multidimensional spike trains as their input and comprise a multitude of temporal receptive fields and conductance-based models of action potential generation. Each temporal receptive field describes the spatiotemporal contribution of all synapses between any two neurons and incorporates the (passive) processing carried out by the dendritic tree. The aggregate dendritic current produced by a multitude of temporal receptive fields is encoded into a sequence of action potentials by a spike generator modeled as a nonlinear dynamical system. Our approach builds on the observation that during any experiment, an entire neural circuit, including its receptive fields and biophysical spike generators, is projected onto the space of stimuli used to identify the circuit. Employing the reproducing kernel Hilbert space (RKHS) of trigonometric polynomials to describe input stimuli, we quantitatively describe the relationship between underlying circuit parameters and their projections. We also derive experimental conditions under which these projections converge to the true parameters. In doing so, we achieve the mathematical tractability needed to characterize the biophysical spike generator and identify the multitude of receptive fields. The algorithms obviate the need to repeat experiments in order to compute the neurons' rate of response, rendering our methodology of interest to both experimental and theoretical neuroscientists.

Resumo Limpo

introduc novel approach complet function identif biophys spikeprocess neural circuit circuit consid accept multidimension spike train input compris multitud tempor recept field conductancebas model action potenti generat tempor recept field describ spatiotempor contribut synaps two neuron incorpor passiv process carri dendrit tree aggreg dendrit current produc multitud tempor recept field encod sequenc action potenti spike generat model nonlinear dynam system approach build observ experi entir neural circuit includ recept field biophys spike generat project onto space stimuli use identifi circuit employ reproduc kernel hilbert space rkhs trigonometr polynomi describ input stimuli quantit describ relationship under circuit paramet project also deriv experiment condit project converg true paramet achiev mathemat tractabl need character biophys spike generat identifi multitud recept field algorithm obviat need repeat experi order comput neuron rate respons render methodolog interest experiment theoret neuroscientist

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