title: Mapping visual stimuli to perceptual decisions via sparse decoding of mesoscopic neural activity.
abstract: In this talk I will describe our work investigating sparse decoding of neural activity, given a realistic mapping of the visual scene to neuronal spike trains generated by a model of primary visual cortex (V1). We use a linear decoder which imposes sparsity via an L1 norm. The decoder can be viewed as a decoding neuron (linear summation followed by a sigmoidal nonlinearity) in which there are relatively few non-zero synaptic weights. We find: (1) the best decoding performance is for a representation that is sparse in both space and time, (2) decoding of a temporal code results in better performance than a rate code and is also a better fit to the psychophysical data, (3) the number of neurons required for decoding increases monotonically as signal-to-noise in the stimulus decreases, with as little as 1% of the neurons required for decoding at the highest signal-to-noise levels, and (4) sparse decoding results in a more accurate decoding of the stimulus and is a better fit to psychophysical performance than a distributed decoding, for example one imposed by an L2 norm. We conclude that sparse coding is well-justified from a decoding perspective in that it results in a minimum number of neurons and maximum accuracy when sparse representations can be decoded from the neural dynamics.
authors.affiliation: Department of Biomedical Engineering, Columbia University, New York, 410027, USA. psajda@columbia.edu
authors.forename: Paul
authors.initials: P
authors.lastname: Sajda
mesh_terms: D000818:Animals; D003198:Computer Simulation; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D010775:Photic Stimulation / Q000379:methods*; D014793:Visual Cortex / Q000502:physiology*; D014795:Visual Pathways / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*