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. (240 words)
Significance Statement Networks of neurons in the brain do not have a fixed connectivity. We address the question how stable computational performance can be achieved by continuously changing neural circuits, and how these networks could even benefit from these changes. We show that the stationary distribution of network configurations provides a level of analysis where these issues can be addressed in a perspicuous manner. In particular, this theoretical framework allows us to address analytically the questions which rules for reward-gated synaptic rewiring and plasticity would work best in this context, and what impact different levels of activity-independent synaptic processes are likely to have. We demonstrate the viability of this approach through computer simulations and links to experimental data. (116 words)
- reward-Modulated STDP
- Spine Dynamics
- Stochastic Synaptic Plasticity
- Synapse-Autonomous Processes
- Synaptic Rewiring
- Task-Irrelevant Dimensions in Motor Control
Footnotes
Authors report no conflict of interest.
Written under partial support by the Human Brain Project of the European Union #604102 and #720270.
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
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