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Spike-timing-dependent plasticity in balanced random networks.

Abigail Morrison1, Ad Aertsen, Markus Diesmann

  • 1Computational Neuroscience Group, RIKEN Brain Science Institute, Wako City, Saitama 351-0198, Japan abigail@brain.riken.jp

Neural Computation
|April 21, 2007
PubMed
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This study introduces a new spike-timing-dependent plasticity (STDP) rule compatible with balanced random network models. The findings explain neural activity and synaptic weight dynamics in the brain.

Area of Science:

  • Computational Neuroscience
  • Neural Plasticity

Background:

  • Balanced random network models explain irregular neural firing and membrane potential fluctuations in cortical neurons.
  • Spike-timing-dependent plasticity (STDP) is a key mechanism for synaptic modification, but its compatibility with these network models is unclear.

Purpose of the Study:

  • To investigate the compatibility of balanced random network models with experimentally observed STDP.
  • To propose and validate a novel STDP update rule within these network models.

Main Methods:

  • Re-examination of experimental STDP data.
  • Development of a new STDP update rule with multiplicative depression and power-law potentiation.
  • Implementation of the rule in large, balanced networks with realistic connectivity.

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Main Results:

  • The proposed STDP rule is compatible with asynchronous irregular activity in balanced networks.
  • The model produces a unimodal synaptic weight distribution with fluctuating individual weight trajectories.
  • Synchronous stimulation of a neuronal group leads to severe decoupling, hindering control over other neurons.

Conclusions:

  • The novel STDP rule supports the balanced random network model's ability to replicate in vivo cortical activity.
  • Synaptic weight dynamics remain flexible without developing structure under this plasticity rule.
  • Neuronal groups can become functionally decoupled, impacting network control mechanisms.