Self-organization in Cortical Maps & EM-learning
Francesco Frisone, Pietro G. Morasso, and Luca Perico
University of Genova, Dept. of Informatics, Systems, lecommunications Via Opera Pia 13, 16145 Genova (IT)
Starting from the problem of density estimation, it is shown that Expectation Maximization (EM) learning can be considered a Hebbian mechanism. From this, it is possible to outline a theory of self-organization of cortical maps, which is based on a well-defined optimization process and preserves biologically desirable characteristics such as local computation and uniform treatment of input and lateral connections. A thalamocortical network is described that implements the theory in a fully distributed manner: it uses cortical dynamics for the E-step and Hebbian adaptation of cortico-cortical connections at steady state for the M-step.
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