1. Journal Publication – mark by [1]
  2. Conferences with proceedings – mark by [2]
  3. Lectures at Scholarly Conferences – link


  • Control flow in active inference systems Part II: Tensor networks as general models of control flow
    Chris Fields, Filippo Fabrocini, Karl Friston, James F Glazebrook, Hananel Hazan, Michael Levin, Antonino Marcianò
    IEEE Transactions on Molecular, Biological and Multi-Scale Communications: 2023 May
    [Part I, Part II][1]


  • Hananel Hazan, Michael Levin
    Exploring The Behavior of Bioelectric Circuits using Evolution Heuristic Search.
    Bioelectricity. Volume: 4 Issue 4: December 15, 2022.
    [Bioelectricity, bioRxiv][1]
  • Aidan Kierans, Hananel Hazan, Shiri Dori-Hacohen.
    Quantifying Misalignment Between Agents
  • Alex Henderson, Chris Yakopcic, Cory Merkel, Steven Harbour, Tarek M. Taha, Hananel Hazan.
    Circuit Optimization Techniques for Efficient Ex-Situ Training of Robust Memristor Based Liquid State Machine.
    INANOARCH ’22: Proceedings of the 17th ACM International Symposium on Nanoscale Architectures December 2022 Article No.: 10 Pages 1–6 [link] [3]
  • Daniel Haşegan, Matt Deible, Christopher Earl, David D’Onofrio, Hananel Hazan, Haroon Anwar and Samuel A. Neymotin
    Training spiking neuronal networks to perform motor control using reinforcement and evolutionary learning,
    Front. Comput. Neurosci., 30 September 2022
  • Haroon Anwar, Simon Caby, Salvador Dura-Bernal, David D’Onofrio, Daniel Hasegan, Matt Deible, Sara Grunblatt, George L Chadderdon, Cliff C Kerr, Peter Lakatos, William W Lytton, Hananel Hazan, Samuel A Neymotin.
    Training a spiking neuronal network model of visual-motor cortex to play a virtual racket-ball game using reinforcement learning.
    Plos One, [link, bioRxiv][1]
  • Hananel Hazan, Simon Caby, Cristopher Earl, Hava Siegelmann, Michael Levin.
    Memory via Temporal Delays in Weightless Spiking Neural Network.
    In press, [arXiv]


  • Daniel Haşegan, Matt Deible, Christopher Earl, David D’Onofrio, Hananel Hazan, Haroon Anwar, Samuel A Neymotin.
    Multi-timescale biological learning algorithms train spiking neuronal network motor control.


  • Hillel Ori, Hananel Hazan, Eve Marder, Shimon Marom.
    Dynamic clamp constructed phase diagram of the Hodgkin-Huxley action potential model [Link, DOI, bioRxiv][1]
  • Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi, Hava Siegelmann, Robert Kozma.
    Lattice Map Spiking Neural Networks (LM-SNNs) for Clustering and Classifying Image Data
    Annals of Mathematics and Artificial Intelligence (2019).
    [Link, DOI, Arxiv-Link] [1]


  • Sneha Aenugu, Abhishek Sharma, Sasikiran Yelamarthy, Hananel Hazan, Philip.S.Thomas, Robert Kozma
    Reinforcement learning with a network of spiking agents
    Poster NIPS 2019
  • Daniel J. Saunders, Devdhar Patel, Hananel Hazan, Hava Siegelmann, Robert Kozma.
    Locally Connected Spiking Neural Networks for Unsupervised Feature Learning
    Neural Networks, Volume 119, November 2019, Pages 332-340
    [link, DOI, Arxiv-Link] [1]
  • Devdhar Patel, Hananel Hazan, Daniel J. Saunders, Hava Siegelmann, Robert Kozma.
    Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games.
    Neural Networks, 25 August 2019 
    [Link, DOI, Arxiv-Link] [1]


  • Hananel Hazan, Daniel J. Saunders, Hassaan Khan, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma.
    BindsNET: A machine learning-oriented spiking neural networks library in Python.
    Frontiers in Neuroinformatics, 12 December 2018
    [Link, Arxiv-Link, git] [1]
  • Hananel Hazan, Daniel Saunders, T, Darpan Sanghavi, Hava Siegelmann and Kozma Robert.
    Unsupervised Learning with Self-Organizing Spiking Neural Networks.
    Conference on Neural Networks (IJCNN), 2018, Rio de Janeiro, 2018, pp. 1-6. Nominated for best paper.
    [Link, Arxiv-Link] [2]


  • Hananel Hazan and Noam E. Ziv:
    Closed Loop Experiment Manager (CLEM)—An Open and Inexpensive Solution for Multichannel Electrophysiological Recordings and Closed Loop Experiments
    Frontiers in Neuroscience, 18 October 2017.
    [Link, PDF, git] [1]


  • T Bitan, A Frid, H Hazan, LM Manevitz, H Shalelashvili, Y Weiss.
    Classification from generation: Recognizing deep grammatical information during reading from rapid event-related fMRI.
    Conference on Neural Networks (IJCNN), 2016. International Joint Conference on, 4637-4642.
    [Link, PDF] [2]
  • A Frid, H Hazan, E Koilis, LM Manevitz, M Merhav, G Star.
    The Existence of Two Variant Processes in Human Declarative Memory: Evidence Using Machine Learning Classification.
    Techniques in Retrieval Tasks Transactions on Computational Collective Intelligence XXIV, 117-133.
    [Link, PDF] [2]


  • Paolo Avesani, Hananel Hazan, Ester Koilis, Larry M. Manevitz, Diego Sona:
    Non-parametric temporal modeling of the hemodynamic response function via a liquid state machine
    Neural Networks. Volume 70, October 2015, Pages 61–73.
    [Link, PDF] [1]
  • Tali Atir-Sharon, Asaf Gilboa, Hananel Hazan, Ester Koilis, and Larry M. Manevitz:
    Decoding the Formation of New Semantics: MVPA Investigation of Rapid Neocortical Plasticity during Associative Encoding through Fast Mapping
    Neural Plasticity, vol. 2015, Article ID 804385, 17 pages, 2015.
    [Link, PDF] [1]
  • Frid A., Hazan H., Koilis E., M. Manevitz L., Merhav M. and Star G. (2015).
    Machine Learning Techniques and the Existence of Variant Processes in Humans Declarative Memory.
    In Proceedings of the 7th International Joint Conference on Computational Intelligence, ISBN 978-989-758-157-1, pages 114-121. DOI: 10.5220/0005594501140121
    [Link, PDF] [2]


  • Haim Shalelashvili, Tali Bitan, Alex Frid, Hananel Hazan, Stav Hertz, Yael Weiss and Larry Manevitz:
    Recognizing Deep Grammatical Information during Reading from Event Related fMRI
    2014 IEEE 28h Convention of Electrical Electronics Engineers in Israel (IEEEI), 2014.
    [Link, PDF] [2]
  • Alex Frid, Hananel Hazan, Larry Manevitz,:
    Towards Classifying Human Phonemes without Encodings via Spatiotemporal Liquid State Machines
    Software Science, IEEE International Conference Software Science, Technology and Engineering (SWSTE), 2014, 63-64.
    [Link, PDF] [2]
  • Alex Frid, Hananel Hazan, Dan Hilu, Larry Manevitz, Lorraine O Ramig, Shimon Sapir:
    Computational Diagnosis of Parkinson’s Disease Directly from Natural Speech Using Machine Learning Techniques
    IEEE International Conference Software Science, Technology and Engineering (SWSTE), 2014, 50-53.
    [Link, PDF] [2]



  • Hazan, H. and Manevitz, L:
    Topological constraints and robustness in liquid state machines
    Expert Systems with Applications, Volume 39, Issue 2, Pages 1597-1606, February 2012.
    [Link, PDF, Source – Code] [1]
  • Frid, A., Hazan, H. and Manevitz, L:
    Temporal Pattern Recognition via Temporal Networks of  Temporal Neurons
    2012 IEEE 27th Convention of Electrical Electronics Engineers in Israel (IEEEI), 2012, pp. 1 –4.
    [Link, PDF] [2]
  • Hananel Hazan; Dan Hilu; Larry Manevitz; Lorraine O. Ramig; and Shimon Sapir:
    Early Diagnosis of Parkinson’s Disease via Machine Learning on Speech Data
    2012 IEEE 27th Convention of Electrical Electronics Engineers in Israel (IEEEI), 2012, pp. 1 –4.
    [Link, PDF] [2]


  • Gilboa, A., Hazan, H., Koilis, E., Manevitz, L. and Sharon, T:
    Multiple Declarative Memory Systems: Classification with Machine Learning Techniques
    Proceedings of the International Joint Conference on Neural Networks(IJCNN), 2011, San Jose, Ca. Page 54, Poster 312.
    [PDF] [2]
  • Avesani, P.; Hazan, H.; Koilis, E.; Manevitz, L.; Sona, D;
    Learning BOLD Response in fMRI by Reservoir Computing
    IEEE Workshop on Pattern Recognition in Neuroimaging (PRNI),ISBN: 978-0-7695-4399-4, pp.57-60, 16-18 May 2011.
    [Link,PDF] [2]


  • Manevitz, L. Hazan,H:
    Stability and Topology in Reservoir Computing.
    Lecture Notes in Computer Science (LNAI).2010. 6438.
    [Link,PDF] [1]
  • Peleg, O., Manevitz, L., Hazan, H., Eviatar, Z:
    Two Hemispheres – Two Networks A Computational Model Explaining Hemispheric Asymmetries While Reading Ambiguous Words.
    Annals of Mathematics and Artificial Intelligence (AMAI). 2010. Volume 59, Number 1, 125-147.
    [Link, PDF] [1]
  • Manevitz, L. Hazan,H.
    Stability and Topology in Reservoir Computing
    Proceedings of the 9th Mexican International Conference on Artificial Intelligence (MICAI), Pages 245-256.

    [Link] [2]
  • Hazan H. and Manevitz L:
    The Liquid State Machine Is Not Robust To Problems In Its Components But Topological Constraints Can Restore Robustness.
    Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation,2010, pages 258-264.
    [Link,PDF] [2]
  • Eviatar Z., Hazan H., Manevitz L., Peleg, O. and Timor, R.:
    Interactions Between Hemispheres When Disambiguating Ambiguous Homograph Words During Silent Reading.
    Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation,2010, pages 271-278.
    [Link, PDF] [2]


Hananel Hazan , חננאל חזן , Activities, Academic Publications , Open Source Programs

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