Emergence of complex computational structures from chaotic neural
networks through reward-modulated Hebbian learning
G. M. Hoerzer, R. Legenstein, and W. Maass
This article addresses the question how generic microcircuits of neurons in
different parts of the cortex can attain and maintain different computational
specializations. We show that if stochastic variations in the dynamics of
local microcircuits are correlated with signals related to functional
improvements of the brain (e.g. in the control of behavior), the
computational operation of these microcircuits can become optimized for
specific tasks such as the generation of specific periodic signals, and
task-dependent routing of information. Furthermore, we show that working
memory can emerge autonomously through reward-modulated Hebbian learning, if
needed for specific tasks. Altogether our results suggest that
reward-modulated synaptic plasticity can not only optimize network parameters
for specific computational tasks, but can also initiate a functional rewiring
that re-programs microcircuits, thereby generating diverse computational
functions in different generic cortical microcircuits. On a more general
level this article provides a new perspective for a standard model for
computations in generic cortical microcircuits (liquid computing model). It
shows that the arguably most problematic assumption of this model, the
postulate of a teacher that trains neural readouts through supervised
learning, can be eliminated. We show that generic networks of neurons can
learn numerous biologically relevant computations through trial and error.
Reference: G. M. Hoerzer, R. Legenstein, and W. Maass.
Emergence of complex computational structures from chaotic neural networks
through reward-modulated Hebbian learning.
Cerebral Cortex, 2012.
doi:10.1093/cercor/bhs348, incl. suppl.