Cerebellar learning using perturbations

Elife. 2018 Nov 12;7:e31599. doi: 10.7554/eLife.31599.

Abstract

The cerebellum aids the learning of fast, coordinated movements. According to current consensus, erroneously active parallel fibre synapses are depressed by complex spikes signalling movement errors. However, this theory cannot solve the credit assignment problem of processing a global movement evaluation into multiple cell-specific error signals. We identify a possible implementation of an algorithm solving this problem, whereby spontaneous complex spikes perturb ongoing movements, create eligibility traces and signal error changes guiding plasticity. Error changes are extracted by adaptively cancelling the average error. This framework, stochastic gradient descent with estimated global errors (SGDEGE), predicts synaptic plasticity rules that apparently contradict the current consensus but were supported by plasticity experiments in slices from mice under conditions designed to be physiological, highlighting the sensitivity of plasticity studies to experimental conditions. We analyse the algorithm's convergence and capacity. Finally, we suggest SGDEGE may also operate in the basal ganglia.

Keywords: Purkinje cell; cerebellum; credit assignment; learning; mouse; neuroscience; stochastic gradient descent; synaptic plasticity.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Action Potentials / physiology
  • Algorithms
  • Animals
  • Cerebellum / physiology*
  • Computer Simulation
  • Female
  • Learning*
  • Long-Term Potentiation
  • Mice, Inbred C57BL
  • Neural Networks, Computer
  • Neuronal Plasticity / physiology
  • Purkinje Cells / physiology
  • Time Factors

Grant support

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.