Methods in neuronal modeling: From synapses to networks
Methods in neuronal modeling: From synapses to networks
Single neuron computation: From dynamical system to feature detector
Neural Computation
Reconstruction of sensory stimuli encoded with integrate-and-fire neurons with random thresholds
EURASIP Journal on Advances in Signal Processing - Special issue on statistical signal processing in neuroscience
Consistent recovery of sensory stimuli encoded with MIMO neural circuits
Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
Population encoding with Hodgkin-Huxley neurons
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
Modeling the impact of common noise inputs on the network activity of retinal ganglion cells
Journal of Computational Neuroscience
Channel identification machines
Computational Intelligence and Neuroscience
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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.