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Multineuronal activity patterns identify selective synaptic connections under realistic experimental constraints.

Brendan Chambers1, Jason N MacLean2

  • 1Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois; and.

Journal of Neurophysiology
|July 24, 2015
PubMed
Summary

Researchers used a network model to map multineuronal activity patterns to synaptic connections. They found that an iterative Bayesian inference algorithm precisely identified causal connections, especially those related to synaptic integration.

Keywords:
activity mapscomputational neurosciencereliable timingtwo-photon calcium ion imaging

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Multineuronal activity patterns in the neocortex correlate with sensory input, motor output, and behavior.
  • These patterns arise from neural connectivity, not random chance.
  • Relating activity patterns to underlying synaptic structure is crucial but challenging.

Purpose of the Study:

  • To assess the feasibility of inferring causal synaptic connections from multineuronal activity patterns.
  • To compare the precision of Bayesian inference with correlation-based methods for synaptic connection mapping.
  • To understand how temporal dynamics, including synaptic integration, influence the inference of connectivity.

Main Methods:

  • Utilized a computational network model to simulate diverse multineuronal activity patterns under experimental constraints.
  • Employed an iterative Bayesian inference algorithm to detect monosynaptic connections.
  • Compared the performance of Bayesian inference against correlation-based inference methods.

Main Results:

  • The Bayesian inference algorithm identified a subset of monosynaptic connections with high precision, outperforming correlation-based methods.
  • Inference accuracy improved with a greater diversity of activity patterns, rather than increased observations of a single pattern.
  • Effective and precise detection of causal synaptic connectivity occurred when considering lags around the timescale of synaptic integration (~10 ms), not just transmission time (~2 ms).
  • Strong synaptic connections, critical for cortical computation, were preferentially detected.

Conclusions:

  • Top-down approaches mapping function (activity patterns) to structure (synaptic connections) are feasible, even with simulated experimental constraints.
  • The identified synaptic connections are closely linked to cortical processing.
  • Synaptic integration plays a critical role in shaping postsynaptic spiking and enabling precise inference of causal connectivity.