Neural activity shows substantial variability in response to repeated presentations of the same stimulus (called trial-to-trial variability), and it is unclear how a population of neurons can reliably discriminate between different stimuli with such trial-to-trial variability. To analyze population activity, we define a multi-neuronal firing rate space where each axis represents the firing rate of one neuron. In this talk, I discuss the hypothesis that, in the firing rate space, trial-to-trial variability and stimulus information lie in mostly orthogonal subspaces. The stimulus information can then be extracted with a simple linear projection, independent of the amount of trial-to-trial variability in the orthogonal subspace. I also present a framework that incorporates dimensionality reduction to analyze the activity of many tens of neurons recorded simultaneously, and preliminary data from the visual cortex (V1) that supports our hypothesis.
ML Speaking Skills Talk
Machine Learning Department
Carnegie Mellon University
Orthogonality as a Neural Mechanism to Separate Signal from Noise
Thursday, March 27, 2014 - 3:45pm
McWilliams Classroom 4303
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