Abstract:
The paper overviews a computational framework for characterizing the cortical representations and processes which underly the kinematic invariances of movements, taking into account the new understanding of the cortex as a continuously adapting dynamical system, shaped by competitive and cooperative lateral connections. We show how a coordinate-free representation of sensorimotor spaces can emerge from self-organized learning which builds a topological representing structure. This implies a mixture of local and long-range lateral connections, consistent with known anatomical facts, thus allowing the representation of high-dimensional spaces in an apparently flat anatomy. The dynamics of cortical maps is analyzed taking into account the excitatory nature of the majority of cortical synapses and the puzzling presence of long-range competition without long-range inhibition. A model is proposed which combines a process of diffusion (via the excitatory topologically organized connections) and a process of competitive distribution of activation which tends to sharpen the active map region.