Learning Mechanisms
Simon explains how learning mechanisms associate images for invariance learning, influenced by the natural world. Tim discusses transforming geometric problems into non-geometric ones, leading to biological plausibility in spiking nets.In this clip
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Machine Learning Street Talk (MLST)
#041 - Biologically Plausible Neural Networks - Dr. Simon Stringer
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