A dynamic connectome supports the emergence of stable computational function of neural circuits through reward-based learning

TL;DR

This paper presents a theoretical framework showing how spontaneous synaptic dynamics, combined with reward signals, enable neural circuits to maintain stable computational functions despite ongoing structural changes.

Contribution

It introduces a novel model integrating spontaneous synapse-autonomous processes with reward-based learning, explaining stability and adaptability in neural networks.

Findings

Spontaneous synaptic changes can support stable computation.

Reward signals guide network reconfiguration for specific tasks.

Network architecture drifts slowly in task-irrelevant dimensions.

Abstract

Synaptic connections between neurons in the brain are dynamic because of continuously 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 continuously 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 reward 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 understanding of reward-driven network plasticity as continuous sampling of network configurations.