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Related Experiment Videos

Learning with two sites of synaptic integration.

K P Körding1, P König

  • 1Institute of Neuroinformatics, ETH/UNI Zürich, Switzerland. koerding@ini.phys.ethz.ch

Network (Bristol, England)
|March 29, 2000
PubMed
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Abstracts of Presentations at the International Conference on Basic and Clinical Multimodal Imaging (BaCI), a Joint Conference of the International Society for Neuroimaging in Psychiatry (ISNIP), the International Society for Functional Source Imaging (ISFSI), the International Society for Bioelectromagnetism (ISBEM), the International Society for Brain Electromagnetic Topography (ISBET), and the EEG and Clinical Neuroscience Society (ECNS), in Geneva, Switzerland, September 5-8, 2013.

Clinical EEG and neuroscience·2013

This study introduces a novel neural network model where distinct synaptic pathways control neuronal activity and plasticity. This allows for optimized information processing and introduces a new performance measure called relevant infomax.

Area of Science:

  • Computational neuroscience
  • Neural network modeling
  • Synaptic plasticity

Background:

  • Traditional models assume synaptic plasticity depends solely on pre- and postsynaptic activity.
  • This dependency confounds synaptic plasticity with neuronal activation, hindering network performance optimization.
  • Apical dendrite research inspires a new model for separating these influences.

Purpose of the Study:

  • To investigate a neural network with two distinct synaptic integration sites.
  • To explore how separating activity and plasticity control can optimize network function.
  • To introduce and analyze the 'relevant infomax' performance criterion.

Main Methods:

  • Developed a computational model of neurons with two sites of synaptic integration.

Related Experiment Videos

  • Analyzed network behavior with a constant parameter set under varying stimuli.
  • Simulated synaptic plasticity gating and neuronal activity independently.

    • Main Results:

    • Identified 'supervisor' afferents that gate plasticity individually for each neuron.
    • Demonstrated constant net activity across stimuli due to specific receptive field acquisition, maximizing information.
    • Showcased implementation of coherent information maximization and 'relevant infomax'.

    Conclusions:

    • The proposed model effectively separates neuronal activity and plasticity control.
    • This separation enables enhanced information processing, including maximization of coherent and relevant information.
    • The 'relevant infomax' criterion offers a new way to measure network performance based on input relevance.