Abstract

Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, by allowing the imitation of arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions. Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird's own song (BOS) stimuli.

Highlights

  • Complex vertebrate motor behaviors are generated by dedicated cortical circuits

  • Mirroring properties depend on the variability of the neural motor code which may be dissociated from apparent variability of the motor behavior as is the case in lateral magnocellular nucleus of the anterior nidopallium (LMAN) neurons that fire highly variable spike patterns despite high song stereotypy in adults

  • Our conclusions are valid for arbitrary sensory systems, provided they are able to signal sensory feedback from motor actions with sufficient sensitivity matched to the behavioral richness generated by the motor system

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Summary

INTRODUCTION

Complex vertebrate motor behaviors are generated by dedicated cortical circuits. The organization of these circuits and the plasticity rules that lead to their development and that guarantee their maintenance are functionally related to neural activity in single units and across larger populations (Gallese et al, 1996; Rizzolatti et al, 1996; Rizzolatti and Craighero, 2004; Harvey et al, 2012). Sensory responses in mirror neurons could develop from the contingency of motor-related firing and its sensory consequences feeding back to motor areas We develop this idea and propose a simple mathematical theory of mirror neuron formation from correlational learning rules. Our work provides an interesting link between the correlational structure of motor behavior, its underlying neural code, and fine-grained temporal properties of mirror neuron responses and their suitability for flexible action imitation These conceptual connections suggest a set of natural experiments designed to probe for the existence, and characterize the causal nature of, inverse models by measuring the fine grained temporal properties of the sensory and motor responses of mirror neurons. As we discuss below, when applied to the bird song system, these experiments make a specific, testable prediction about the existence and temporal properties of mirror neurons in the variable motor circuit LMAN, as well as explain the origin of previously observed temporal properties of mirror neurons in the stereotyped motor circuit HVC

A LINEAR FRAMEWORK
A VARIABLE NEURAL CODE YIELDS CAUSAL INVERSES
DISCUSSION
GRADIENT DESCENT DERIVATION OF ELIGIBILITY-WEIGHTED HEBBIAN LEARNING
CORRELATION OF MOTOR ACTIVITY DETERMINES AVERAGE SYNAPTIC CHANGE
Variable motor codes are associated with large mirroring offsets
V is a predictive inverse
MIRRORED RESPONSE STRENGTH IN A PROBABILISTIC MODEL
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