JACIII Vol.2 No.6 pp. 178-184
doi: 10.20965/jaciii.1998.p0178


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)

April 28, 1998
August 29, 1998
December 20, 1998
Self-organization, Hebbian learning, Cortical maps, Population code, EM-learning
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.
Cite this article as:
F. Frisone, P. Morasso, and L. Perico, “Self-organization in Cortical Maps & EM-learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.2 No.6, pp. 178-184, 1998.
Data files:

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 12, 2024