Sparse analog associative memory via L1-regularization and thresholding

Abstract

The CA3 region of the hippocampus acts as an auto-associative memory and is responsible for the consolidation of episodic memory. Two important characteristics of such a network is the sparsity of the stored patterns and the nonsaturating firing rate dynamics. To construct such a network, here we use a maximum a posteriori based cost function, regularized with L1-norm, to change the internal state of the neurons. Then a linear thresholding function is used to obtain the desired output firing rate. We show how such a model leads to a more biologically reasonable dynamic model which can produce a sparse output and recalls with good accuracy when the network is presented with a corrupted input.