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Frontiers in Neural Circuits|March 22, 2014
Biologically inspired load balancing mechanism in neocortical competitive learningAmir Tal, Noam Peled, Hava T SiegelmannIEEE Transactions on Neural Networks and Learning Systems|April 6, 2023
Signal Propagation: The Framework for Learning and Inference in a Forward PassAdam Kohan, Edward A Rietman, Hava T SiegelmannCognitive Neurodynamics|October 29, 2009
Memory reconsolidation for natural language processingKun Tu, David G Cooper, Hava T SiegelmannPlos One|May 26, 2010
Dynamic computational model suggests that cellular citizenship is fundamental for selective tumor apoptosisMegan Olsen, Nava Siegelmann-Danieli, Hava T SiegelmannNature Communications|June 5, 2026
Turing universal neural networks do not require global clocksHava T Siegelmann, Roy N Siegelmann, Stephen Chung, et al.Proceedings of the National Academy of Sciences of the United States of America|November 6, 2020
A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortexBen Tsuda, Kay M Tye, Hava T Siegelmann, et al.Scientific Reports|March 6, 2021
Unique scales preserve self-similar integrate-and-fire functionality of neuronal clustersAnar Amgalan, Patrick Taylor, Lilianne R Mujica-Parodi, et al.Nature Communications|August 15, 2020
Brain-inspired replay for continual learning with artificial neural networksGido M van de Ven, Hava T Siegelmann, Andreas S ToliasFrontiers in Neuroscience|March 15, 2017
Energetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal NetworksJavier Burroni, P Taylor, Cassian Corey, et al.Pageof 3