Abstract

There is considerable debate over whether the brain codes information using neural firing rate or the fine-grained structure of spike timing. We investigated this issue in spike discharge recorded from single units in the sensorimotor cortex, deep cerebellar nuclei, and dorsal root ganglia in macaque monkeys trained to perform a finger flexion task. The task required flexion to four different displacements against two opposing torques; the eight possible conditions were randomly interleaved. We used information theory to assess coding of task condition in spike rate, discharge irregularity, and spectral power in the 15- to 25-Hz band during the period of steady holding. All three measures coded task information in all areas tested. Information coding was most often independent between irregularity and 15–25 Hz power (60% of units), moderately redundant between spike rate and irregularity (56% of units redundant), and highly redundant between spike rate and power (93%). Most simultaneously recorded unit pairs coded using the same measure independently (86%). Knowledge of two measures often provided extra information about task, compared with knowledge of only one alone. We conclude that sensorimotor systems use both rate and temporal codes to represent information about a finger movement task. As well as offering insights into neural coding, this work suggests that incorporating spike irregularity into algorithms used for brain-machine interfaces could improve decoding accuracy.

Highlights

  • A KEY CONTROVERSY IN NEUROSCIENCE is the extent to which neurons use a rate-based or temporal code (Shadlen and Newsome 1998; Softky and Koch 1993)

  • We have provided evidence that both rate and temporal codes can represent task parameters in the primate sensorimotor system

  • The same general pattern of results was seen both in the cerebral cortex, deep cerebellar nuclei (DCN), and peripheral afferents of the dorsal root ganglia (DRG)

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Summary

Introduction

A KEY CONTROVERSY IN NEUROSCIENCE is the extent to which neurons use a rate-based or temporal code (Shadlen and Newsome 1998; Softky and Koch 1993). Recent attention has switched to candidate codes which generate their own internal temporal references (Panzeri and Diamond 2010). These could include population responses where synchronous activity between neurons is important (Hatsopoulos et al 1998), the phase relationship between spikes and local oscil-. Rate and temporal coding take fundamentally opposing views of the variability of ISIs. From the perspective of rate coding, fluctuations represent noise, which can only be overcome by averaging sufficient intervals. If precise spike timing can be used as a neural code, such “rate-corrected” variability measures should convey additional information

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