Learning multiple variable-speed sequences in striatum via cortical tutoring.

James M Murray,G Sean Escola

doi:10.7554/elife.26084

Abstract

Sparse, sequential patterns of neural activity have been observed in numerous brain areas during timekeeping and motor sequence tasks. Inspired by such observations, we construct a model of the striatum, an all-inhibitory circuit where sequential activity patterns are prominent, addressing the following key challenges: (i) obtaining control over temporal rescaling of the sequence speed, with the ability to generalize to new speeds; (ii) facilitating flexible expression of distinct sequences via selective activation, concatenation, and recycling of specific subsequences; and (iii) enabling the biologically plausible learning of sequences, consistent with the decoupling of learning and execution suggested by lesion studies showing that cortical circuits are necessary for learning, but that subcortical circuits are sufficient to drive learned behaviors. The same mechanisms that we describe can also be applied to circuits with both excitatory and inhibitory populations, and hence may underlie general features of sequential neural activity pattern generation in the brain.

Highlights

Understanding the mechanisms by which neural circuits learn and generate the complex, dynamic patterns of activity that underlie behavior and cognition remains a fundamental goal of neuroscience
The basic mechanisms that we propose may be realized in any brain area where sequences have been observed, for a specific example and to compare to experimental results, we focus on neural activity in striatum, where sparse activity sequences have been observed in recurrently connected populations of inhibitory medium spiny neurons (MSNs) in rodents during locomotion (Rueda-Orozco and Robbe, 2015) and lever-press delay tasks (Mello et al, 2015; Dhawale et al, 2015; Gouvea et al, 2015)
Motivated by experimental results involving the basal ganglia, we have developed a model of recurrently connected inhibitory units, the same basic mechanisms for sequence learning can be applied to obtain sparse sequential firing patterns with flexible time encoding in a network of excitatory units with shared inhibition, a common motif used to obtain sparse coding of both static and dynamic neural activity patterns in models of cortical circuits

Summary

Introduction

Understanding the mechanisms by which neural circuits learn and generate the complex, dynamic patterns of activity that underlie behavior and cognition remains a fundamental goal of neuroscience. MSNs, which constitute over 90% of the neurons in striatum (Gerfen and Surmeier, 2011), exhibit stereotyped sequential firing patterns during learned motor sequences and learned behaviors in which timing plays an important role, with sparse firing sequences providing a seemingly ideal representation for encoding time and providing a rich temporal basis that can be read out by downstream circuits to determine behavior (Jin et al, 2009; Mello et al, 2015; Rueda-Orozco and Robbe, 2015; Dhawale et al, 2015; Gouvea et al, 2015; Bakhurin et al, 2017) Such neural activity has been shown in rodents to strongly correlate with time judgement in a fixed-interval lever-press task (Gouvea et al, 2015), and with kinematic parameters such as the animal’s position and speed in a task in which the animal was trained to remain on a treadmill for a particular length of time (Rueda-Orozco and Robbe, 2015). We show that the same mechanisms can be applied to circuits with both excitatory and inhibitory units, and may provide an explanation for the sequential firing patterns that have been observed in other brain areas including hippocampus (Nadasdy et al, 1999; Pastalkova et al, 2008; MacDonald et al, 2013; Eichenbaum, 2014) and cortex (Luczak et al, 2007; Jin et al, 2009; Harvey et al, 2012)

Results

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Discussion

Materials and methods

G Sean Escola