Purchase this article with an account.
Jonathan W. Pillow, Eero P. Simoncelli; Dimensionality reduction in neural models: An information-theoretic generalization of spike-triggered average and covariance analysis. Journal of Vision 2006;6(4):9. doi: https://doi.org/10.1167/6.4.9.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
We describe an information-theoretic framework for fitting neural spike responses with a Linear–Nonlinear–Poisson cascade model. This framework unifies the spike-triggered average (STA) and spike-triggered covariance (STC) approaches to neural characterization and recovers a set of linear filters that maximize mean and variance-dependent information between stimuli and spike responses. The resulting approach has several useful properties, namely, (1) it recovers a set of linear filters sorted according to their informativeness about the neural response; (2) it is both computationally efficient and robust, allowing recovery of multiple linear filters from a data set of relatively modest size; (3) it provides an explicit “default” model of the nonlinear stage mapping the filter responses to spike rate, in the form of a ratio of Gaussians; (4) it is equivalent to maximum likelihood estimation of this default model but also converges to the correct filter estimates whenever the conditions for the consistency of STA or STC analysis are met; and (5) it can be augmented with additional constraints on the filters, such as space–time separability. We demonstrate the effectiveness of the method by applying it to simulated responses of a Hodgkin–Huxley neuron and the recorded extracellular responses of macaque retinal ganglion cells and V1 cells.
This PDF is available to Subscribers Only