Performance Optimization Study of the Neuromorphic Radiation Anomaly Detector

Abstract:

This work reports on new results and insights from the optimization of spiking neural networks developed for gamma-ray radiation anomaly detection. Our previous paper introduced the first known neuromorphic algorithm for this application, demonstrating promising results and insights into optimal hyperparameter selection - particularly in the choice of data input encodings. Since the first paper, we have tested the algorithms on new datasets to investigate transferability from one background radiation environment to another. We have also performed a new hyperparameter optimization experiment with this new dataset to investigate the impact of new radiation data formatting techniques, the inclusion or neuronal temporality, and neuron charge leakage. This paper provides an overview and discussion of the results from this study. Of note, we report that the inclusion of neuronal temporality, or the process of maintaining synaptic state between sequences of input, improves recall by over 50\% at an operationally-relevant false alarm rate of 1 per hr.

This paper is in press.

Recommended citation: James M. Ghawaly Jr., Aaron Young, Andrew Nicholson, Brett Witherspoon, Nick Prins, Matthew Swinney, Cihangir Celik, Catherine D. Schuman, and Karan Pankaj Kumar Patel. 2023. Performance Optimization Study of the Neuromorphic Radiation Anomaly Detector. In International Conference on Neuromorphic Systems (ICONS ’23), August 1–3, 2023, Santa Fe, NM, USA. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3589737.3605980