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Conditioned DFA (nDFA), a family that adapts established inverse-moment preconditioning to either side of this update, is studied, establishing practical benefits and important limits of conditioning learning with fixed random feedback.
A exact mean-covariance decomposition separates a rank-one update formed by the mean teaching signal and mean presynaptic activity, which prevents sustained collapse and improves learning in the tested setting.
DendriNet, a trainable framework that varies integration rule, morphology, synaptic allocation, divisor locality, and dendritic nonlinearities is introduced, a trainable framework that varies integration rule, morphology, synaptic allocation, divisor locality, and dendritic nonlinearities.
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