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Quantifying Synapses: an Immunocytochemistry-based Assay to Quantify Synapse Number
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Indistinguishable Synapses Lead to Sparse Networks.

Joseph Snider1

  • 1Institute for Neural Computation, University of California, San Diego, La Jolla, CA 90039, U.S.A. j1snider@ucsd.edu.

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|January 18, 2018
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Summary
This summary is machine-generated.

Neurons process information from indistinguishable inputs. Maximizing information storage leads to a specific distribution of synapse strengths, explaining neural network sparsity.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Statistical Mechanics

Background:

  • Neurons integrate numerous inputs, making individual signals indistinguishable.
  • Understanding synapse strength distribution is key to neural computation.

Purpose of the Study:

  • To investigate the constraints on synapse strength distribution based on information processing principles.
  • To model the relationship between indistinguishable inputs and neural network structure.

Main Methods:

  • Developed a theoretical model assuming neurons maximize information storage.
  • Derived the distribution of synapse strengths based on input indistinguishability.
  • Compared model predictions with experimental data from Caenorhabditis elegans.

Main Results:

  • Synapse strengths follow a modified Boltzmann distribution proportional to [Formula: see text].
  • The [Formula: see text] dependence explains the prevalence of weak or zero synaptic connections.
  • Model is consistent with empirical data on neural connectivity and synaptic strength.

Conclusions:

  • Input indistinguishability and information maximization impose strong constraints on neural architecture.
  • The derived synapse strength distribution provides a framework for understanding neural network sparsity.
  • This model offers insights into the fundamental principles governing neural connectivity.