no code implementations • 30 May 2023 • Devdhar Patel, Terrence Sejnowski, Hava Siegelmann
We present a temporally layered architecture (TLA) for temporally adaptive control with minimal energy expenditure.
no code implementations • 25 Dec 2022 • Devdhar Patel, Hava Siegelmann
However, early exits increase the training time of the neural networks.
no code implementations • 25 Dec 2022 • Devdhar Patel, Joshua Russell, Francesca Walsh, Tauhidur Rahman, Terrence Sejnowski, Hava Siegelmann
Our design is biologically inspired and draws on the architecture of the human brain which executes actions at different timescales depending on the environment's demands.
no code implementations • 30 Sep 2020 • Weihao Tan, Devdhar Patel, Robert Kozma
The present work focuses on using SNNs in combination with deep reinforcement learning in ATARI games, which involves additional complexity as compared to image classification.
no code implementations • 12 Apr 2019 • Daniel J. Saunders, Devdhar Patel, Hananel Hazan, Hava T. Siegelmann, Robert Kozma
In recent years, Spiking Neural Networks (SNNs) have demonstrated great successes in completing various Machine Learning tasks.
3 code implementations • 26 Mar 2019 • Devdhar Patel, Hananel Hazan, Daniel J. Saunders, Hava Siegelmann, Robert Kozma
Previous studies in image classification domain demonstrated that standard NNs (with ReLU nonlinearity) trained using supervised learning can be converted to SNNs with negligible deterioration in performance.