A dynamic connectome supports the emergence of stable computational
function of neural circuits through reward-based learning
Synaptic connections between neurons in the brain are synamic because of
continuoisly ongoing spine dynamics, axonal sprouting, and other processes.
In fact, it was recently shown that the spontaneous synapse-autonomous
component of spine dynamics is at least as large as the component that
depends on the history of pre- and postsynaptic neural activity. These data
are inconsistent with common models for network plasticity, and raise the
questions how neural circuits can maintain a stable computational function in
spite of these continuoisly ongoing processes, and what functional uses these
ongoing processes might have. Here, we present a rigorous theoretical
framework for these seemingly stochastic spine dynamics and rewiring
processes in the context of reward-based learning tasks. We show that
spontaneous synapse-autonomous processes, in combination with rewards signals
such as dopamine, can explain the capability of networks of neurons in the
brain to configure themselves for specific computational tasks, and to
compensate automatically for later changes in the network or task.
Furthermore we show theoretically and through computer simulations that
stable computational performance is compatible with continuously ongoing
synapse-autonomous changes. After reaching good computational performance it
causes primarily a slow drift of network architecture and dynamics in
task-irrelevant dimensions, as observed for neural activity in motor cortex
and other areas. On the more abstract level of reinforcement learning the
resulting model gives rise to an understnading of reward-driven network
plasticity, as continuous sampling of network configurations.
Reference: D. Kappel, R. Legenstein, S. Habenschuss, M. Hsieh, and
A dynamic connectome supports the emergence of stable computational function
of neural circuits through reward-based learning.
eNeuro, 2 April, 2018.