On the role of feedback in enhancing the computational power of generic neural microcircuits

P. Joshi

Abstract:

Circuits of neurons in the brain perform diverse cortical computations in parallel, endowing the organism with diverse cortical modalities, e.g. motor control, vision, and audition; and higher order cognitive processes, e.g. planning, and decision making. It is believed that these computations are carried out by network of neurons in cortical microcircuits, where each microcircuit is composed of rather stereotypical circuit of neurons within a cortical column. A characteristic property of these cortical circuits is the presence of abundant feedback connections, be it on the level of recurrent axon collaterals projecting back onto the same neuron, or on the network level between different cortical areas. This thesis explores the functional role of neural feedback in enhancing the computational power of generic neural microcircuits. It is shown that feedback endows standard models for neural circuits with the capability to emulate arbitrary Turing machines. In fact, with a suitable feedback such circuits can simulate any dynamical system, in particular any conceivable analog computer. Under realistic noise conditions the computational power of these circuits is obviously reduced. However it is demonstrated through computer simulations that feedback also provides a significant gain in computational power for quite detailed models of cortical microcircuits with in-vivo-like high levels of noise. Furthermore neurocomputational models using generic neural microcircuits with feedback are explored in the context of motor control, decision making, and ``action selection in presence of decisions''.

Reference: P. Joshi. On the role of feedback in enhancing the computational power of generic neural microcircuits. PhD thesis, Graz University of Technology, 2007.