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Nonrandom network connectivity comes in pairs.

Felix Z Hoffmann1,2, Jochen Triesch1

  • 1Frankfurt Institute for Advanced Studies (FIAS), Johann Wolfgang Goethe University, Frankfurt am Main, Germany.

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|March 31, 2018
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Summary
This summary is machine-generated.

Bidirectional connections in neural networks are inherently linked to nonrandom structures. Even with approximate symmetry, an overabundance of reciprocal connections arises when certain neuron pairs have higher connection probabilities.

Keywords:
Bidirectional connectionsCortical circuitNonrandom connectivityRandom graph model

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Network Science

Background:

  • Overrepresentation of bidirectional connections in local cortical networks is frequently observed.
  • This finding is central to discussions about nonrandom connectivity patterns in the brain.

Purpose of the Study:

  • To mathematically analyze the relationship between symmetric connection probabilities and the emergence of bidirectional connections.
  • To investigate whether nonrandom connectivity structures inherently lead to an overabundance of reciprocal connections.

Main Methods:

  • Mathematical analysis of network connectivity.
  • Numerical simulations to explore connection probability symmetries.

Main Results:

  • Demonstrated an inherent link between symmetric connection probabilities (Pij = Pji) and the occurrence of bidirectional connections.
  • Showed that an overabundance of reciprocal connections necessarily emerges when connection probabilities are not uniform across all pairs.
  • Numerical results suggest this overrepresentation persists even with approximate symmetry in connection probabilities.

Conclusions:

  • Bidirectional connections and nonrandom network structures are intrinsically linked.
  • The probability of connection between neuron pairs is a key factor driving the overrepresentation of bidirectional connections.
  • Findings hold implications for understanding the structural organization of cortical networks.