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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

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Published on: November 1, 2019

Using evolutionary algorithms for fitting high-dimensional models to neuronal data.

Carl-Magnus Svensson1, Stephen Coombes, Jonathan Westley Peirce

  • 1School of Psychology, University Park, University of Nottingham, NG7 2RD, Nottingham, UK. pmxcms1@gmail.com

Neuroinformatics
|January 20, 2012
PubMed
Summary
This summary is machine-generated.

Evolutionary algorithms (EA) outperform gradient following (GF) methods for fitting complex neuroscience models. EAs find better solutions for visual neuron models, independent of initial parameters, unlike GF methods susceptible to local minima.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Complex biological systems often require fitting mathematical models with numerous parameters to intricate datasets.
  • Accurate model fitting is crucial for understanding neural computation and biological processes.

Purpose of the Study:

  • To compare the performance of gradient following (GF) and evolutionary algorithms (EA) for fitting a 9-parameter model of a filter-based visual neuron.
  • To evaluate the efficacy of these algorithms in fitting real neurophysiological data from macaque primary visual cortex (V1).

Main Methods:

  • Fitting a 9-parameter model of a visual neuron to 107 macaque V1 neuron recordings.
  • Comparative analysis of gradient following (GF) methods and evolutionary algorithms (EA) for model parameter estimation.
  • Assessment of algorithm performance based on solution quality, convergence speed, and susceptibility to local minima.

Main Results:

  • Gradient following (GF) methods converged rapidly but were highly sensitive to initial parameter estimates and local minima, yielding suboptimal fits.
  • Evolutionary algorithms (EA) required more iterations but consistently found superior solutions, demonstrating independence from initial parameter values.
  • EA performance was robust, providing trustworthy near-optimal fits for the complex visual neuron model.

Conclusions:

  • Evolutionary algorithms (EA) are superior to gradient following (GF) methods for fitting complex neuroscience models, particularly when dealing with large parameter spaces and real-world data.
  • The robustness and reliability of EA make them a preferred choice for parameter estimation in computational neuroscience and other complex biological systems.
  • Future applications in neuroscience can benefit from the trustable and optimal fitting capabilities of evolutionary algorithms.