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Computational Inference of Synaptic Polarities in Neuronal Networks.

Michael R Harris1,2, Thomas P Wytock1, István A Kovács1,3

  • 1Department of Physics and Astronomy, Northwestern University, Evanston, IL, 60208, USA.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|March 31, 2022
PubMed
Summary
This summary is machine-generated.

Mapping synaptic polarity (inhibitory/excitatory) is crucial for brain function. This study computationally infers synaptic polarity using connectome data and gene expression, successfully predicting unknown connections.

Keywords:
Caenorhabditis eleganscomplex networksconnectomelink predictionsynaptic polarity

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Systems Biology

Background:

  • Synaptic polarity (inhibitory/excitatory) is fundamental to neural circuit function but challenging to map.
  • Understanding synaptic polarity is key to deciphering complex brain functions and building accurate brain models.

Purpose of the Study:

  • To computationally infer synaptic polarity using the Caenorhabditis elegans connectome.
  • To develop and test three distinct computational approaches for predicting synaptic polarity based on available data.

Main Methods:

  • Integration of neurotransmitter (NT) and receptor (R) gene expression data with the connectome model (CM) and wiring rules.
  • Training a spatial connectome model using known polarities and gene expression data.
  • Employing network sign prediction for polarity inference without prior expression or wiring rules.

Main Results:

  • The connectome model resolved 356 additional synaptic polarities.
  • The spatial connectome model inferred polarity for 81% of connections with high precision and identified 147 new polarities.
  • A generalized CM demonstrated high performance in polarity prediction without prior data.

Conclusions:

  • Computational inference of synaptic polarity is feasible using connectome data and gene expression.
  • The developed methods significantly expand the mapping of synaptic polarities in neural circuits.
  • These findings advance the creation of more comprehensive and realistic models of brain function.