Rapid Processing and Receptive Field Self-Organization in Column Based Neural Networks

A neural network is presented which is based on a columnar interconnection architecture. Motivated by neuroanatomical and neurophysiological findings we model a cortical macrocolumn as a collection of inhibitorily coupled minicolumns, which themselves consist of randomly interconnected spiking neurons. A stability analysis of the system's dynamical equations shows that minicolumns can act as monolithic functional units for purposes of critical, fast decisions and learning. Oscillating inhibition (in the gamma frequency range) leads to a phase-coupled population rate code and high sensitivity to small imbalances in minicolumn inputs. If afferent fibers to the minicolumns are subject to Hebbian plasticity, minicolumns self-organize their receptive fields to become classifiers for the input patterns.

The presentation will include the analytical treatment of the dynamics along with bifurcation diagrams and various computer simulations.