Superlinear Summation of Information in Premotor Neuron Pairs

Int J Neural Syst. 2017 Mar;27(2):1650009. doi: 10.1142/S012906571650009X. Epub 2015 Dec 20.

Abstract

Whether premotor/motor neurons encode information in terms of spiking frequency or by their relative time of firing, which may display synchronization, is still undetermined. To address this issue, we used an information theory approach to analyze neuronal responses recorded in the premotor (area F5) and primary motor (area F1) cortices of macaque monkeys under four different conditions of visual feedback during hand grasping. To evaluate the sensitivity of spike timing correlation between single neurons, we investigated the stimulus dependent synchronization in our population of pairs. We first investigated the degree of correlation of trial-to-trial fluctuations in response strength between neighboring neurons for each condition, and second estimated the stimulus dependent synchronization by means of an information theoretical approach. We compared the information conveyed by pairs of simultaneously recorded neurons with the sum of information provided by the respective individual cells. The information transmission across pairs of cells in the primary motor cortex seems largely independent, whereas information transmission across pairs of premotor neurons is summed superlinearly. The brain could take advantage of both the accuracy provided by the independency of F1 and the synergy allowed by the superlinear information population coding in F5, distinguishing thus the generalizing role of F5.

Keywords: Cortex; Neural code; information theory; motor control; neural networks; spike synchronization.

Publication types

  • Evaluation Study

MeSH terms

  • Action Potentials / physiology
  • Algorithms
  • Animals
  • Female
  • Hand / physiology
  • Information Theory
  • Linear Models
  • Macaca fascicularis
  • Male
  • Microelectrodes
  • Models, Neurological*
  • Motor Activity / physiology
  • Motor Cortex / physiology*
  • Neural Pathways / physiology
  • Neurons / physiology*
  • Periodicity