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Computational neuroscience
and Neurocomputing


KTH CSC CB

Associative memory

One theme studied for a long time within SANS concerns cortical associative memory function. Previously we have shown that a recurrent attractor network model with cortical minicolumns as its computational units is a biologically plausible model of a patch of cortex. We have demonstrated that such a network is compatible with cortical dynamics, network organization, and microarchitecture. A Bayesian-Hebbian learning rule developed at SANS provides a functional model of synaptic plasticity. The biologically inspired network architecture and learning rule has further been formalized as an abstract learning paradigm, BCPNN (Bayesian Confidence Propagation Neural Network). This can be seen both as an abstract learning model and a working hypothesis regarding cortical associative memory function, along the lines suggested by Donald Hebb some fifty years ago. There is a close collaboration with SICS with regard to algorithm development and industrial applications of BCPNN.

Published by: Informationsred <infomaster@nada.kth.se>
Last update 2008-09-29