A Computational Framework for Cortical Learning

Biol Cybern. 2004 Jun;90(6):400-9. doi: 10.1007/s00422-004-0487-1. Epub 2004 Jul 22.

Abstract

Recent physiological findings have revealed that long-term adaptation of the synaptic strengths between cortical pyramidal neurons depends on the temporal order of presynaptic and postsynaptic spikes, which is called spike-timing-dependent plasticity (STDP) or temporally asymmetric Hebbian (TAH) learning. Here I prove by analytical means that a physiologically plausible variant of STDP adapts synaptic strengths such that the presynaptic spikes predict the postsynaptic spikes with minimal error. This prediction error model of STDP implies a mechanism for cortical memory: cortical tissue learns temporal spike patterns if these spike patterns are repeatedly elicited in a set of pyramidal neurons. The trained network finishes these patterns if their beginnings are presented, thereby recalling the memory. Implementations of the proposed algorithms may be useful for applications in voice recognition and computer vision.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Action Potentials / physiology*
  • Algorithms
  • Animals
  • Cerebral Cortex / physiology
  • Excitatory Postsynaptic Potentials / physiology
  • Humans
  • Learning / physiology*
  • Nerve Net / physiology
  • Neural Networks, Computer*
  • Neural Pathways / physiology*
  • Neuronal Plasticity / physiology*
  • Pyramidal Cells / physiology*
  • Synapses / physiology
  • Synaptic Transmission / physiology
  • Time Factors