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Found 4 papers, 2 papers with code
A Hippocampus Model for Online One-Shot Storage of Pattern Sequences
To our knowledge this is the first model of the hippocampus that allows to store correlated pattern sequences online in a one-shot fashion without a consolidation process, which can instantaneously be recalled later.
Hebbian-Descent
Hebbian-descent addresses these problems by getting rid of the activation function's derivative and by centering, i. e. keeping the neural activities mean free, leading to a biologically plausible update rule that is provably convergent, does not suffer from the vanishing error term problem, can deal with correlated data, profits from seeing patterns several times, and enables successful online learning when centering is used.
Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image Statistics
We present a theoretical analysis of Gaussian-binary restricted Boltzmann machines (GRBMs) from the perspective of density models.
How to Center Binary Deep Boltzmann Machines
This work analyzes centered binary Restricted Boltzmann Machines (RBMs) and binary Deep Boltzmann Machines (DBMs), where centering is done by subtracting offset values from visible and hidden variables.
