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Wang, Felix

Publications and source records attributed to Wang, Felix.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Spiking Markov Reward Process v.0.1

SAND2024-11150O The Spiking Markov Reward Process software is a spiking neural network that streams binary arithmetic and computes the state value function of a Markov reward process. The software will be released to the SpiNNcloud group for development of neuromorphic acceleration. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Wang, Felix↗
DOE OSTI2024

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

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.

Kulkarni, Shruti↗
DOE OSTI2024

STACS Tutorial

Explore the source record for details and available documents.

Wang, Felix↗
DOE OSTI2024

Terrain-Relative Navigation with Neuro-Inspired Elevation Encoding

Terrain-relative navigation (TRN) encompasses a wide variety of algorithms that perform localization with respect to the terrain below a flying vehicle. In traditional approaches, measurements of the terrain are matched to a map carried onboard. This work presents a terrain-relative navigation filter with a position measurement inspired by neural activity associated with positioning in nature. Here, the filter is shown to produce accurate position measurements that outperform popular optimization and template matching methods given poor prior knowledge of the position. The proposed method is also better-suited to distributed implementation than optimization-based methods.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Combining Spike Time Dependent Plasticity (STDP) and Backpropagation (BP) for Robust and Data Efficient Spiking Neural Networks (SNN)

National security applications require artificial neural networks (ANNs) that consume less power, are fast and dynamic online learners, are fault tolerant, and can learn from unlabeled and imbalanced data. We explore whether two fundamentally different, traditional learning algorithms from artificial intelligence and the biological brain can be merged. We tackle this problem from two directions. First, we start from a theoretical point of view and show that the spike time dependent plasticity (STDP) learning curve observed in biological networks can be derived using the mathematical framework of backpropagation through time. Second, we show that transmission delays, as observed in biological networks, improve the ability of spiking networks to perform classification when trained using a backpropagation of error (BP) method. These results provide evidence that STDP could be compatible with a BP learning rule. Combining these learning algorithms will likely lead to networks more capable of meeting our national security missions.

97 MATHEMATICS AND COMPUTING↗