Abstract
This defensive publication details the innovative design and implementation of a single-layer 256 neuron Spike Neural Network (Spike NN) employing shapelets and a standing wave method for neural activation. Spike Neural Networks, inspired by biological neural systems, utilize discrete spikes for information processing, enhancing energy and computational efficiency. In this approach, shapelets—small, local patterns within time series data—are integrated to provide a new dimension of pattern recognition and neural activation. The standing wave method introduces a novel mechanism where interference patterns between outward and inward waves create areas of sustained neural activation. This model not only mimics the complex dynamics of biological neurons but also highlights focal points of computational significance. The publication outlines the mathematical models, algorithms, and practical steps for implementing this network, emphasizing its potential applications in neuromorphic computing, real-time systems, and energy-efficient AI solutions. Through detailed simulations and validation, this work demonstrates the viability and advantages of combining shapelets with standing wave activation in Spike NNs, contributing valuable insights to the field of advanced neural network design.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Gagnon, Renee, "Defensive Publication: Single-Layer 256 Neuron Spike Neural Network with Standing Wave Activation", Technical Disclosure Commons, (July 14, 2024)
https://www.tdcommons.org/dpubs_series/7174