250 citations · 352 across the 16 of their papers we have counts for
16 papers
Training Ecosystems: A Computational Approach to Uncovering Learning Behavior in Unconventional Contexts
Adrita Samanta, Hananel Hazan, Michael Levin
Recent progress in diverse intelligence has shown simple learning capacities below the organism level - single cells and even molecular networks. However, there are still many know…
A Little Rank Goes a Long Way: Random Scaffolds with LoRA Adapters Are All You Need
Hananel Hazan, Yanbo Zhang, Benedikt Hartl +1
How many of a neural network's parameters actually encode task-specific information? We investigate this question with LottaLoRA, a training paradigm in which every backbone weight…
Reservoir Computation with Networks of Differentiating Neuron Ring Oscillators
Alexander Yeung, Peter DelMastro, Arjun Karuvally +3
Reservoir Computing is a machine learning approach that uses the rich repertoire of complex system dynamics for function approximation. Current approaches to reservoir computing us…
Transient Dynamics in Lattices of Differentiating Ring Oscillators
Peter DelMastro, Arjun Karuvally, Hananel Hazan +2
Recurrent neural networks (RNNs) are machine learning models widely used for learning temporal relationships. Current state-of-the-art RNNs use integrating or spiking neurons -- tw…
Stop treating `AGI' as the north-star goal of AI research
Borhane Blili-Hamelin, Christopher Graziul, Leif Hancox-Li +13
The AI research community plays a vital role in shaping the scientific, engineering, and societal goals of AI research. In this position paper, we argue that focusing on the highly…
Heuristically Adaptive Diffusion-Model Evolutionary Strategy
Benedikt Hartl, Yanbo Zhang, Hananel Hazan +1
Diffusion Models represent a significant advancement in generative modeling, employing a dual-phase process that first degrades domain-specific information via Gaussian noise and r…
Diffusion Models are Evolutionary Algorithms
Yanbo Zhang, Benedikt Hartl, Hananel Hazan +1
In a convergence of machine learning and biology, we reveal that diffusion models are evolutionary algorithms. By considering evolution as a denoising process and reversed evolutio…
Quantifying Misalignment Between Agents: Towards a Sociotechnical Understanding of Alignment
Aidan Kierans, Avijit Ghosh, Hananel Hazan +1
Existing work on the alignment problem has focused mainly on (1) qualitative descriptions of the alignment problem; (2) attempting to align AI actions with human interests by focus…
Control flow in active inference systems
Chris Fields, Filippo Fabrocini, Karl Friston +4
Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deplo…
Memory via Temporal Delays in weightless Spiking Neural Network
Hananel Hazan, Simon Caby, Christopher Earl +2
A common view in the neuroscience community is that memory is encoded in the connection strength between neurons. This perception led artificial neural network models to focus on c…
Reinforcement learning with a network of spiking agents
Sneha Aenugu, Abhishek Sharma, Sasikiran Yelamarthi +3
Neuroscientific theory suggests that dopaminergic neurons broadcast global reward prediction errors to large areas of the brain influencing the synaptic plasticity of the neurons i…
Lattice Map Spiking Neural Networks (LM-SNNs) for Clustering and Classifying Image Data
Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi +2
Spiking neural networks (SNNs) with a lattice architecture are introduced in this work, combining several desirable properties of SNNs and self-organized maps (SOMs). Networks are…
Locally Connected Spiking Neural Networks for Unsupervised Feature Learning
Daniel J. Saunders, Devdhar Patel, Hananel Hazan +2
In recent years, Spiking Neural Networks (SNNs) have demonstrated great successes in completing various Machine Learning tasks. We introduce a method for learning image features by…
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games
Devdhar Patel, Hananel Hazan, Daniel J. Saunders +2
Deep Reinforcement Learning (RL) demonstrates excellent performance on tasks that can be solved by trained policy. It plays a dominant role among cutting-edge machine learning appr…
Unsupervised Learning with Self-Organizing Spiking Neural Networks
Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi +2
We present a system comprising a hybridization of self-organized map (SOM) properties with spiking neural networks (SNNs) that retain many of the features of SOMs. Networks are tra…
BindsNET: A machine learning-oriented spiking neural networks library in Python
Hananel Hazan, Daniel J. Saunders, Hassaan Khan +3
The development of spiking neural network simulation software is a critical component enabling the modeling of neural systems and the development of biologically inspired algorithm…