Maximally informative dimensions: Analyzing neural responses to natural signals

Simple item page
dc.creatorSharpee, Tatyana
dc.creatorRust, Nicole C.
dc.creatorBialek, William
dc.date2002-08-14
dc.date.accessioned2026-07-07T05:48:01Z
dc.date.available2026-07-07T05:48:01Z
dc.descriptionWe propose a method that would allow for a rigorous statistical analysis of neural responses to natural stimuli, which are non-Gaussian and exhibit strong correlations. We have in mind a model in which neurons are selective for a small number of stimulus dimensions out of the high dimensional stimulus space, but within this subspace the responses can be arbitrarily nonlinear. Existing analysis methods are based on correlation functions between stimuli and responses, but these methods are guaranteed to work only in the case of Gaussian stimulus ensembles. As an alternative to correlation functions, we maximize the mutual information between the neural responses and projections of the stimulus onto low dimensional subspaces. The procedure can be done iteratively by increasing the dimensionality of this subspace. Those dimensions that allow the recovery of all of the information between spikes and the full unprojected stimuli describe the relevant subspace. If the dimensionality of the relevant subspace indeed is small, it becomes feasible to map out the neuron's input-output function even under fully natural stimulus conditions. These ideas are illustrated in simulations on model visual neurons responding to natural scenes.
dc.description12 pages, 3 figures
dc.identifierhttps://arxiv.org/abs/physics/0208057
dc.identifierhttp://arxiv.org/abs/physics/0208057
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/84870
dc.subjectBiological Physics
dc.subjectData Analysis, Statistics and Probability
dc.subjectNeurons and Cognition
dc.titleMaximally informative dimensions: Analyzing neural responses to natural signals
dc.typetext

Files

Collections

arXiv