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Hananel Hazan
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2020 – today
- 2024
- [i9]Aidan Kierans, Avijit Ghosh, Hananel Hazan, Shiri Dori-Hacohen:
Quantifying Misalignment Between Agents. CoRR abs/2406.04231 (2024) - [i8]Yanbo Zhang, Benedikt Hartl, Hananel Hazan, Michael Levin:
Diffusion Models are Evolutionary Algorithms. CoRR abs/2410.02543 (2024) - 2023
- [j11]Chris Fields
, Filippo Fabrocini
Control Flow in Active Inference Systems - Part I: Classical and Quantum Formulations of Active Inference. IEEE Trans. Mol. Biol. Multi Scale Commun. 9(2): 235-245 (2023) - [j10]Chris Fields
Control Flow in Active Inference Systems - Part II: Tensor Networks as General Models of Control Flow. IEEE Trans. Mol. Biol. Multi Scale Commun. 9(2): 246-256 (2023) - 2022
- [j9]Daniel Hasegan, Matt Deible, Christopher Earl
Training spiking neuronal networks to perform motor control using reinforcement and evolutionary learning. Frontiers Comput. Neurosci. 16 (2022) - [c12]Alex Henderson
Circuit Optimization Techniques for Efficient Ex-Situ Training of Robust Memristor Based Liquid State Machine. NANOARCH 2022: 10:1-10:6 - [i7]Hananel Hazan, Simon Caby, Christopher Earl, Hava T. Siegelmann, Michael Levin:
Memory via Temporal Delays in weightless Spiking Neural Network. CoRR abs/2202.07132 (2022) - 2020
- [j8]Hananel Hazan
Lattice map spiking neural networks (LM-SNNs) for clustering and classifying image data. Ann. Math. Artif. Intell. 88(11): 1237-1260 (2020)
2010 – 2019
- 2019
- [j7]Daniel J. Saunders, Devdhar Patel
Locally connected spiking neural networks for unsupervised feature learning. Neural Networks 119: 332-340 (2019) - [j6]Devdhar Patel
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to Atari Breakout game. Neural Networks 120: 108-115 (2019) - [i6]Devdhar Patel, Hananel Hazan, Daniel J. Saunders, Hava T. Siegelmann, Robert Kozma:
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games. CoRR abs/1903.11012 (2019) - [i5]Daniel J. Saunders, Devdhar Patel, Hananel Hazan, Hava T. Siegelmann, Robert Kozma:
Locally Connected Spiking Neural Networks for Unsupervised Feature Learning. CoRR abs/1904.06269 (2019) - [i4]Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma:
Lattice Map Spiking Neural Networks (LM-SNNs) for Clustering and Classifying Image Data. CoRR abs/1906.11826 (2019) - [i3]Sneha Aenugu, Abhishek Sharma, Sasikiran Yelamarthi, Hananel Hazan, Philip S. Thomas, Robert Kozma:
Reinforcement learning with spiking coagents. CoRR abs/1910.06489 (2019) - 2018
- [j5]Hananel Hazan
BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python. Frontiers Neuroinformatics 12: 89 (2018) - [c11]Hananel Hazan
Unsupervised Learning with Self-Organizing Spiking Neural Networks. IJCNN 2018: 1-6 - [i2]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. CoRR abs/1806.01423 (2018) - [i1]Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma:
Unsupervised Learning with Self-Organizing Spiking Neural Networks. CoRR abs/1807.09374 (2018) - 2016
- [j4]Alex Frid
The Existence of Two Variant Processes in Human Declarative Memory: Evidence Using Machine Learning Classification Techniques in Retrieval Tasks. Trans. Comput. Collect. Intell. 24: 117-133 (2016) - [c10]Tali Bitan
Classification from generation: Recognizing deep grammatical information during reading from rapid event-related fMRI. IJCNN 2016: 4637-4642 - 2015
- [j3]Paolo Avesani
Non-parametric temporal modeling of the hemodynamic response function via a liquid state machine. Neural Networks 70: 61-73 (2015) - [c9]Alex Frid
Machine Learning Techniques and the Existence of Variant Processes in Humans Declarative Memory. IJCCI (NCTA) 2015: 114-121 - 2014
- [c8]Alex Frid
Computational Diagnosis of Parkinson's Disease Directly from Natural Speech Using Machine Learning Techniques. SwSTE 2014: 50-53 - [c7]Alex Frid
Towards Classifying Human Phonemes without Encodings via Spatiotemporal Liquid State Machines: Extended Abstract. SwSTE 2014: 63-64 - 2013
- [b1]Hananel Hazan:
Temporal classification and computation tools inspired by biological neurons. University of Haifa, Israel, 2013 - 2012
- [j2]Hananel Hazan
Topological constraints and robustness in liquid state machines. Expert Syst. Appl. 39(2): 1597-1606 (2012) - 2011
- [c6]Paolo Avesani
Learning BOLD Response in fMRI by Reservoir Computing. PRNI 2011: 57-60 - 2010
- [j1]Orna Peleg, Larry M. Manevitz, Hananel Hazan
Two hemispheres - two networks: a computational model explaining hemispheric asymmetries while reading ambiguous words. Ann. Math. Artif. Intell. 59(1): 125-147 (2010) - [c5]Hananel Hazan, Larry M. Manevitz:
The Liquid State Machine is not Robust to Problems in Its Components but Topological Constraints Can Restore Robustness. IJCCI (ICFC-ICNC) 2010: 258-264 - [c4]Zohar Eviatar, Hananel Hazan, Larry M. Manevitz, Orna Peleg, Rom Timor:
Interactions between Hemispheres When Disambiguating Ambiguous Homograph Words during Silent Reading. IJCCI (ICFC-ICNC) 2010: 271-278 - [c3]Larry M. Manevitz, Hananel Hazan
Stability and Topology in Reservoir Computing. MICAI (2) 2010: 245-256
2000 – 2009
- 2007
- [c2]Orna Peleg, Zohar Eviatar, Larry M. Manevitz, Hananel Hazan:
Using Neural Network Models to Model Cerebral Hemispheric Differences in Processing Ambiguous Words. NeSy 2007 - [c1]Orna Peleg, Zohar Eviatar
Differences and Interactions Between Cerebral Hemispheres When Processing Ambiguous Words. WAPCV 2007: 367-380
Coauthor Index
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