Hananel Hazan

I'm a Research Scientist in Machine Learning and Computational Biology focusing on bio-inspired computing and neural networks. I'm currently working on developing diffusion models and evolutionary algorithms at Allen Discovery Center, having previously done research at University of Haifa and University of Massachusetts Amherst.

My work bridges multiple domains including spiking neural networks, reinforcement learning, and bioelectric circuits. I've developed several key frameworks including BindsNET, a Python library for simulating spiking neural networks, and pioneered new approaches combining diffusion models with evolutionary computation. I'm particularly interested in how biological principles can inform better machine learning architectures.

A major focus of my research has been on making neural networks more robust and biologically plausible through techniques like liquid state machines, tensor networks, and bio-inspired learning rules. I collaborate extensively across disciplines, working with neuroscientists, biologists and computer scientists to develop novel computational approaches inspired by natural systems.

Publications

Heuristically Adaptive Diffusion-Model Evolutionary Strategy

Benedikt Hartl, Yanbo Zhang, Hananel Hazan, Michael Levin

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Diffusion Models are Evolutionary Algorithms

Yanbo Zhang, Benedikt Hartl, Hananel Hazan, Michael Levin

arXiv.org 2024

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Quantifying Misalignment Between Agents: Towards a Sociotechnical Understanding of Alignment

Aidan Kierans, Avijit Ghosh, Hananel Hazan, Shiri Dori-Hacohen

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A Systems Biology Analysis of Chronic Lymphocytic Leukemia

Giulia Pozzati, Jinrui Zhou, Hananel Hazan, G. L. Klement, H. Siegelmann, E. Rietman, J. Tuszynski

bioRxiv 2024

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Control Flow in Active Inference Systems—Part II: Tensor Networks as General Models of Control Flow

C. Fields, Filippo Fabrocini, K. Friston, J. Glazebrook, Hananel Hazan, M. Levin, A. Marcianò

IEEE Transactions on Molecular Biological and Multi-Scale Communications 2023

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Control Flow in Active Inference Systems—Part I: Classical and Quantum Formulations of Active Inference

C. Fields, Filippo Fabrocini, K. Friston, J. Glazebrook, Hananel Hazan, Michael Levin, A. Marcianò

IEEE Transactions on Molecular Biological and Multi-Scale Communications 2023

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Circuit Optimization Techniques for Efficient Ex-Situ Training of Robust Memristor Based Liquid State Machine

Alex Henderson, C. Yakopcic, Steven Harbour, Tarek Taha, Cory E. Merkel, Hananel Hazan

IEEE/ACM International Symposium on Nanoscale Architectures 2022

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Exploring The Behavior of Bioelectric Circuits using Evolution Heuristic Search

Hananel Hazan, M. Levin

bioRxiv 2022

Hananel Hazan

Publications

Heuristically Adaptive Diffusion-Model Evolutionary Strategy

Diffusion Models are Evolutionary Algorithms

Quantifying Misalignment Between Agents: Towards a Sociotechnical Understanding of Alignment

A Systems Biology Analysis of Chronic Lymphocytic Leukemia

Control Flow in Active Inference Systems—Part II: Tensor Networks as General Models of Control Flow

Control Flow in Active Inference Systems—Part I: Classical and Quantum Formulations of Active Inference

Circuit Optimization Techniques for Efficient Ex-Situ Training of Robust Memristor Based Liquid State Machine

Exploring The Behavior of Bioelectric Circuits using Evolution Heuristic Search

Training spiking neuronal networks to perform motor control using reinforcement and evolutionary learning

Memory via Temporal Delays in weightless Spiking Neural Network

Evolutionary and spike-timing-dependent reinforcement learning train spiking neuronal network motor control

Training a spiking neuronal network model of visual-motor cortex to play a virtual racket-ball game using reinforcement learning

Dynamic clamp constructed phase diagram for the Hodgkin and Huxley model of excitability

Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to Atari Breakout game

Reinforcement learning with spiking coagents

Dynamic clamp constructed phase diagram of the Hodgkin-Huxley action potential model

Reinforcement learning with a network of spiking agents

Lattice map spiking neural networks (LM-SNNs) for clustering and classifying image data

Locally Connected Spiking Neural Networks for Unsupervised Feature Learning

Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games

Biometric data vulnerabilities : privacy implications

Unsupervised Learning with Self-Organizing Spiking Neural Networks

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

Closed Loop Experiment Manager (CLEM)—An Open and Inexpensive Solution for Multichannel Electrophysiological Recordings and Closed Loop Experiments

ROBOTS WITH BIOLOGICAL BRAINS: AUTONOMY AND LIABILITY OF A SEMI-ARTIFICIAL LIFE FORM

The Liquid State Machine is not Robust to Problems in Its Components but Topological Constraints Can Restore Robustness

Interactions between Hemispheres When Disambiguating Ambiguous Homograph Words during Silent Reading

Classification from generation: Recognizing deep grammatical information during reading from rapid event-related fMRI

The Existence of Two Variant Processes in Human Declarative Memory: Evidence Using Machine Learning Classification Techniques in Retrieval Tasks

Machine learning techniques and the existence of variant processes in humans declarative memory

Non-parametric temporal modeling of the hemodynamic response function via a liquid state machine

Decoding the Formation of New Semantics: MVPA Investigation of Rapid Neocortical Plasticity during Associative Encoding through Fast Mapping

Autonomous Decisions of Hybrid IT Systems with Biological Brain

Recognizing deep grammatical information during reading from event related fMRI

Computational Diagnosis of Parkinson's Disease Directly from Natural Speech Using Machine Learning Techniques

Towards Classifying Human Phonemes without Encodings via Spatiotemporal Liquid State Machines: Extended Abstract

Creating Free Will in Artificial Intelligence

Temporal pattern recognition via temporal networks of temporal neurons

Early diagnosis of Parkinson's disease via machine learning on speech data

Topological constraints and robustness in liquid state machines

Learning BOLD Response in fMRI by Reservoir Computing

Stability and Topology in Reservoir Computing

Two hemispheres—two networks: a computational model explaining hemispheric asymmetries while reading ambiguous words

Differences and Interactions Between Cerebral Hemispheres When Processing Ambiguous Words

Using Neural Network Models to Model Cerebral Hemispheric Differences in Processing Ambiguous Words

Quantifying Misalignment Between Agents

Memristor Based Liquid State Machine With Method for In-Situ Training

Multi-timescale biological learning algorithms train spiking neuronal network motor control

Temporal classification and computation tools inspired by biological neurons

Control ﬂow in active inference systems Part I: Classical and quantum formulations of active inference