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Author Notes:

Matthew JM Rowan, mjrowan@emory.edu

Author contributions Conceptualization, Resources, Software, Formal analysis, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review and editing. Conceptualization, Resources, Software, Formal analysis, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review and editing. Resources, Software, Funding acquisition, Writing – original draft, Writing – review and editing.

This work was supported by NIH grants R56-AG072473 (MJMR) and the Emory Alzheimer’s Disease Research Center Grant 00100569 (MJMR) with partial support (NPP) provided by CURE Epilepsy and the National Institutes of Health K08NS105929.

Subject:

Research Funding:

This paper was supported by the following grants:

National Institutes of Health R56-AG072473 to Matthew JM Rowan.

Emory Alzheimer's Disease Research Center 00100569 to Matthew JM Rowan.

CURE Epilepsy and the NIH K08NS105929 to Nigel P Pedersen.

National Institutes of Health RF1-AG079269 to Matthew JM Rowan.

Emory/Georgia Tech I3 Computational and Data analysis to Advance Single Cell Biology Research Award to Matthew JM Rowan.

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Biology
  • Life Sciences & Biomedicine - Other Topics
  • computational model
  • artificial neural net
  • NMDA
  • cortex
  • deep learning
  • None
  • ARTIFICIAL NEURAL-NETWORKS
  • 5 PYRAMIDAL NEURONS
  • RETT-SYNDROME
  • MOUSE MODEL
  • ACTION-POTENTIALS
  • SYNAPTIC PLASTICITY
  • GAMMA-OSCILLATIONS
  • DENDRITIC SPIKES
  • BASAL DENDRITES
  • SPIKING NEURONS

Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons

Tools:

Journal Title:

ELIFE

Volume:

Volume 11

Publisher:

Type of Work:

Article | Final Publisher PDF

Abstract:

Understanding the activity of the mammalian brain requires an integrative knowledge of circuits at distinct scales, ranging from ion channel gating to circuit connectomics. Computational models are regularly employed to understand how multiple parameters contribute synergistically to circuit behavior. However, traditional models of anatomically and biophysically realistic neurons are computationally demanding, especially when scaled to model local circuits. To overcome this limitation, we trained several artificial neural network (ANN) architectures to model the activity of realistic multicompartmental cortical neurons. We identified an ANN architecture that accurately predicted subthreshold activity and action potential firing. The ANN could correctly generalize to previously unobserved synaptic input, including in models containing nonlinear dendritic properties. When scaled, processing times were orders of magnitude faster compared with traditional approaches, allowing for rapid parameter-space mapping in a circuit model of Rett syndrome. Thus, we present a novel ANN approach allowing for rapid, detailed network experiments using inexpensive and commonly available computational resources.
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