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A Biologically Plausible Neural Network for Multichannel Canonical Correlation Analysis
2021
Neural Computation
Cortical pyramidal neurons receive inputs from multiple distinct neural populations and integrate these inputs in separate dendritic compartments. We explore the possibility that cortical microcircuits implement canonical correlation analysis (CCA), an unsupervised learning method that projects the inputs onto a common subspace so as to maximize the correlations between the projections. To this end, we seek a multichannel CCA algorithm that can be implemented in a biologically plausible neural
doi:10.1162/neco_a_01414
pmid:34412114
fatcat:nqx6a4koovb6dg7nfq6cpzlttq