Short Term Plasticity for Artificial Neural Networks
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Achieving efficient learning for AI systems was identified as a major challenge in the DOE's recently released, AI for Science, report. The human brain is capable of efficient and low-powered learning. It is likely that implementing brain-like principles will lead to more efficient AI systems. In this LDRD, I aim to contribute to this goal by creating a foundation for implementing and studying a brain phenomenon termed short term plasticity (STP) in spiking artificial neural networks within Sandia. First, data collected by the Allen Institute for Brain Science (AIBS) was analyzed to see if STP could be classified into types using the data collected. Although the data was inadequate at the time, AIBS has updated their database and created models that could be utilized in the future. Second, I began creating a software package to assess the ability of a Boltzmann machine utilizing STP to sample from national security data.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
- DOE Contract Number:
- NA0003525
- OSTI ID:
- 2004879
- Report Number(s):
- SAND--2021-11680; 712755
- Country of Publication:
- United States
- Language:
- English
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