Hierarchial self-organization of minicolumnar receptive fields
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Rapid convergence to feature layer correspondences
Neural Computation
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We consider a cortical macrocolumn as a collection of inhibitorily coupled minicolumns of excitatory neurons and show that its dynamics is determined by a number of stationary points, which grows exponentially with the number of minicolumns.The stability of the stationary points is governed by a single parameter of the network, which determines the number of possibly active minicolumns. The dynamics symmetrizes the activity distributed among the active columns but if the parameter is increased, it forces this symmetry to break by switching off a minicolumn. If, for a state of maximal activity, the parameter is slowly increased the symmetry is successively broken until just one minicolumn remains active.During such a process minor differences between the inputs result in the activation of the minicolumn with highest input, a feature which shows that a macrocolumn can serve as decision and amplification unit for its inputs. We present a complete analysis of the dynamics along with computer simulations, which support the theoretical results.