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 language | English |
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| Title of host publication | 2024 IEEE Neuro Inspired Computational Elements Conference, NICE 2024 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798350390582 |
| DOIs | |
| State | Published - 2024 |
| Event | 2024 IEEE Neuro Inspired Computational Elements Conference, NICE 2024 - La Jolla, United States Duration: Apr 23 2024 → Apr 26 2024 |
Publication series
| Name | 2024 IEEE Neuro Inspired Computational Elements Conference, NICE 2024 - Proceedings |
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Conference
| Conference | 2024 IEEE Neuro Inspired Computational Elements Conference, NICE 2024 |
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| Country/Territory | United States |
| City | La Jolla |
| Period | 04/23/24 → 04/26/24 |
Funding
This manuscript has been authored in part by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-publicaccess-plan). This material is also based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under award number DESC0022566. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC (NTESS), a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration (DOE/NNSA) under contract DE-NA0003525. This written work is co-Authored by an employee of NTESS. The employee, not NTESS, owns the right, title and interest in and to the written work and is responsible for its contents. Any subjective views or opinions that might be expressed in the written work do not necessarily represent the views of the U.S. Government.
Keywords
- Machine Learning
- Neural Algorithms
- Neuromorphic simulations
- Spiking Neural Networks