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Identification of functional synaptic plasticity from spiking activities using nonlinear dynamical modeling.

Dong Song1, Rosa H M Chan2, Brian S Robinson1

  • 1Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA.

Journal of Neuroscience Methods
|October 5, 2014
PubMed
Summary

This study introduces a systems identification method to analyze long-term synaptic plasticity using natural neural activity. It models synaptic strength and extracts learning rules, aiding understanding of memory and brain-computer interfaces.

Keywords:
Learning ruleSpatio-temporal patternSpikeSpike-timing-dependent plasticity

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Biology

Background:

  • Long-term synaptic plasticity is crucial for learning and memory.
  • Understanding synaptic plasticity requires analyzing complex neural activity patterns.
  • Existing models often simplify natural spiking activities.

Purpose of the Study:

  • To develop a systems identification framework for studying long-term synaptic plasticity.
  • To model synaptic strength and extract synaptic learning rules from natural spiking data.
  • To provide a computational basis for understanding learning and memory and for developing adaptive neural prostheses.

Main Methods:

  • Formulation of a multi-input, single-output (MISO), nonlinear dynamical spiking neuron model.
  • Extension of the MISO model to a nonstationary form to track time-varying synaptic strength.
  • Application of Volterra modeling to extract synaptic learning rules, such as spike-timing-dependent plasticity.

Main Results:

  • The proposed framework effectively estimates synaptic strength as functional connectivity.
  • The nonstationary model successfully tracks dynamic changes in synaptic strength.
  • The Volterra method identified synaptic learning rules explaining input-output nonstationarity.

Conclusions:

  • The developed systems identification approach offers a robust method for studying synaptic plasticity with natural spiking data.
  • This framework enhances our understanding of the computational mechanisms underlying learning and memory.
  • The approach has potential applications in designing advanced adaptive cortical prostheses.