Abstract

Multiple neural and synaptic phenomena take place in the brain. They operate over a broad range of timescales, and the consequences of their interplay are still unclear. In this work, I study a computational model of a recurrent neural network in which two dynamic processes take place: sensory adaptation and synaptic plasticity. Both phenomena are ubiquitous in the brain, but their dynamic interplay has not been investigated. I show that when both processes are included, the neural circuit is able to perform a specific computation: it becomes a generative model for certain distributions of input stimuli. The neural circuit is able to generate spontaneous patterns of activity that reproduce exactly the probability distribution of experienced stimuli. In particular, the landscape of the phase space includes a large number of stable states (attractors) that sample precisely this prior distribution. This work demonstrates that the interplay between distinct dynamical processes gives rise to useful computation, and proposes a framework in which neural circuit models for Bayesian inference may be developed in the future.

Highlights

  • The main goal of Computational Neuroscience is to uncover the kinds of computation implemented by neurons and neural circuits, and to identify the biological mechanisms underlying these computations

  • A simulation of neural circuit dynamics is divided in two separate stages: a stimulus-driven stage and a spontaneous stage

  • Synaptic plasticity and sensory adaptation occur during the stimulus-driven stage

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Summary

Introduction

The main goal of Computational Neuroscience is to uncover the kinds of computation implemented by neurons and neural circuits, and to identify the biological mechanisms underlying these computations. Among the numerous biological phenomena observed in the brain, sensory adaptation and synaptic plasticity stand out as two of the most studied, since they are observed ubiquitously across most brain regions and animal species. Both phenomena give rise to specific types of computation, but the functional implications of their interaction remain unclear. Different types of plasticity are believed to underlie a broad range of functions, including: memory formation and storage (Martin et al, 2000; Lamprecht and LeDoux, 2004; Seung, 2009), nervous system development (Katz and Shatz, 1996; Miller, 1996; Sanes and Lichtman, 1999; Song and Abbott, 2001), recovery after brain injury (Buonomano and Merzenich, 1998; Feldman and Brecht, 2005), classical conditioning (Wickens et al, 2003; Calabresi et al, 2007; Surmeier et al, 2009; Pawlak et al, 2010; Gallistel and Matzel, 2013), operant conditioning (Seung, 2003; Montague et al, 2004; Daw and Doya, 2006; Doya, 2007; Soltani and Wang, 2008), spatial navigation (Blum and Abbott, 1996; Mehta et al, 2002), efficient coding of sensory stimuli (Toyoizumi et al, 2005; Savin et al, 2010; Bernacchia and Wang, 2013), homeostatic regulation of neuronal excitability (Royer and Paré, 2003; Turrigiano and Nelson, 2004; Williams et al, 2013), sound localization (Gerstner et al, 1996) and production of behavioral sequences (Fiete et al, 2010)

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