World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
11108
World Ranking
10984
National Ranking
4567

Overview

Hava T. Siegelmann is affiliated with the University of Massachusetts Amherst in the United States. Their research spans several key areas in computer science and neuroscience, reflecting an interdisciplinary approach to understanding neural dynamics and brain function through computational models.

The scientist has contributed extensively to the fields of computer science and neuroscience, with a particular focus on cognitive neuroscience, artificial intelligence, molecular biology, electrical and electronic engineering, and computer vision and pattern recognition. Their work integrates concepts from neural networks, advanced memory systems, and reinforcement learning to explore the mechanisms underlying brain function and learning processes.

Major topics in their research include:

  • Neural dynamics and brain function
  • Advanced Memory and Neural Computing
  • Neural Networks and Applications
  • Reinforcement Learning in Robotics
  • Bioinformatics and Genomic Networks
  • Memory and Neural Mechanisms
  • Functional Brain Connectivity Studies

Siegelmann has published in diverse venues, with frequent contributions to arXiv (Cornell University), bioRxiv (Cold Spring Harbor Laboratory), Nature Machine Intelligence, Neural Computation, and the International Journal of Molecular Sciences.

Among their recent papers are:

  • "Brain-inspired replay for continual learning with artificial neural networks" (2020) published in Nature Communications
  • "Biological underpinnings for lifelong learning machines" (2022) published in Nature Machine Intelligence
  • "Replay in Deep Learning: Current Approaches and Missing Biological Elements" (2021) published in Neural Computation
  • "A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex" (2020) published in Proceedings of the National Academy of Sciences
  • "A collective AI via lifelong learning and sharing at the edge" (2024) published in Nature Machine Intelligence

Siegelmann frequently collaborates with several researchers, including Edward A. Rietman, Terrence J. Sejnowski, Devdhar Patel, Jack A. Tuszyński, and Arjun Karuvally. The frequency of their collaborations ranges from 5 to 12 joint publications, indicating ongoing partnerships within their research network.

Best Publications

  • Support vector clustering

    Asa Ben-Hur;David Horn;Hava T. Siegelmann;Vladimir Vapnik

  • On the Computational Power of Neural Nets

    H.T. Siegelmann;E.D. Sontag

  • Neural networks and analog computation: beyond the Turing limit

    Hava T. Siegelmann

  • Computational capabilities of recurrent NARX neural networks

    H.T. Siegelmann;B.G. Horne;C.L. Giles

  • Analog computation via neural networks

    Hava T. Siegelmann;Eduardo D. Sontag

  • Turing computability with neural nets

    Hava T. Siegelmann;Eduardo D. Sontag

  • Computation beyond the turing limit.

    Hava T. Siegelmann

  • Brain-inspired replay for continual learning with artificial neural networks.

    Gido M. van de Ven;Gido M. van de Ven;Hava T. Siegelmann;Andreas S. Tolias;Andreas S. Tolias

  • BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python.

    Hananel Hazan;Daniel J. Saunders;Hassaan Khan;Devdhar Patel

  • BindsNET: A machine learning-oriented spiking neural networks library in Python

    Hananel Hazan;Daniel J. Saunders;Hassaan Khan;Darpan T. Sanghavi

  • A support vector clustering method

    A. Ben-Hur;D. Horn;H.T. Siegelmann;V. Vapnik

  • The Dynamic Universality of Sigmoidal Neural Networks

    Joe Kilian;Hava T. Siegelmann

  • The global landscape of cognition: hierarchical aggregation as an organizational principle of human cortical networks and functions.

    P. Taylor;J. N. Hobbs;J. Burroni;H. T. Siegelmann

  • Symbolic dynamics and computation in model gene networks.

    R. Edwards;Hava Siegelmann;K. Aziza;L. Glass

  • A Support Vector Method for Clustering

    Asa Ben-Hur;David Horn;Hava T. Siegelmann;Vladimir Vapnik

  • Replay in Deep Learning: Current Approaches and Missing Biological Elements

    Tyler L. Hayes;Giri P. Krishnan;Maxim Bazhenov;Hava T. Siegelmann

  • Analog computation with dynamical systems

    Hava T. Siegelmann;Shmuel Fishman

  • Neural and Super-Turing Computing

    Hava T. Siegelmann

  • Computational power of neural networks: a characterization in terms of Kolmogorov complexity

    J.L. Balcazar;R. Gavalda;H.T. Siegelmann

  • Neural networks and analog computation

    Hava T. Siegelmann

  • Computational power of neural networks

    Hava T. Siegelmann;Eduardo Sontag

Frequent Co-Authors

Robert Kozma
Robert Kozma University of Memphis
Eduardo D. Sontag
Eduardo D. Sontag Northeastern University
Asa Ben-Hur
Asa Ben-Hur Colorado State University
Terrence J. Sejnowski
Terrence J. Sejnowski Salk Institute for Biological Studies
Bhaskar DasGupta
Bhaskar DasGupta University of Illinois at Chicago
Hillel Pratt
Hillel Pratt Technion – Israel Institute of Technology
Ophir Frieder
Ophir Frieder Georgetown University
Lilianne R. Mujica-Parodi
Lilianne R. Mujica-Parodi Stony Brook University
Kay M. Tye
Kay M. Tye Salk Institute for Biological Studies
Leon Glass
Leon Glass McGill University

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