Explaining Neural Spike Activity for Simulated Bio-plausible Network through Deep Sequence Learning

Shruti R. Kulkarni, Anika Tabassum, Seung Hwan Lim, Catherine D. Schuman, Bradley H. Theilman, Fred Rothganger, Felix Wang, James B. Aimone

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

With significant improvements in large-scale simulations of brain models, there is a growing need to develop tools for rapid analysis and interpreting the simulation results. In this work, we explore the potential of sequential deep learning models to understand and explain the network dynamics among the neurons extracted from a large-scale neural simulation in STACS (Simulation Tool for Asynchronous Cortical Stream). Our method employs a representative neuroscience model that abstracts the cortical dynamics with a reservoir of randomly connected spiking neurons with a low stable spike firing rate throughout the simulation duration. We subsequently analyze the spike dynamics of the simulated spiking neural network through an autoencoder model and an attention-based mechanism.

Original languageEnglish
Title of host publication2024 IEEE Neuro Inspired Computational Elements Conference, NICE 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350390582
DOIs
StatePublished - 2024
Event2024 IEEE Neuro Inspired Computational Elements Conference, NICE 2024 - La Jolla, United States
Duration: Apr 23 2024Apr 26 2024

Publication series

Name2024 IEEE Neuro Inspired Computational Elements Conference, NICE 2024 - Proceedings

Conference

Conference2024 IEEE Neuro Inspired Computational Elements Conference, NICE 2024
Country/TerritoryUnited States
CityLa Jolla
Period04/23/2404/26/24

Keywords

  • Machine Learning
  • Neural Algorithms
  • Neuromorphic simulations
  • Spiking Neural Networks

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