ePoster · Computational Neuroscience
Sparse Component Analysis: An interpretable dimensionality reduction tool that identifies building blocks of neural computation
Joshua Glaser · Neuromatch 5
Wed, Sep 28, 2022
Behavior results from neural computations that occur at the level of populations of neurons, and understanding how a given computation produces a particular behavior requires a characterization of how distinct signals interact and evolve. For example, in motor cortex, discrete reaches are generated by a combination of ‘preparatory’ and ‘execution-related’ signals. While the number of computationally-relevant signals is much lower than the number of neurons in a population – and is therefore amenable to dimensionality-reduction methods – standard techniques, like principal components analysis,