12 citations · 14 across the 9 of their papers we have counts for
9 papers
Mean-Field Oscillator Ising Machines: Gradient Flows and Classification of Limit Solutions
Arvind R. Venkatakrishnan, Max Emerick, Bassam Bamieh +1
Oscillator Ising Machines (OIMs) have emerged as promising computational architectures for approximating solutions to combinatorial optimization problems. We derive and analyze the…
Timescale Limits of Linear-Threshold Networks
William Retnaraj, Simone Betteti, Alexander Davydov +2
Linear-threshold networks (LTNs) capture the mesoscale behavior of interacting populations of neurons and are of particular interest to control theorists due to their dynamical ric…
Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization
Arthur N. Montanari, Francesco Bullo, Dmitry Krotov +1
Recent advances at the intersection of control theory, neuroscience, and machine learning have revealed novel mechanisms by which dynamical systems perform computation. These advan…
Oscillator-Based Associative Memory with Exponential Capacity: Theory, Algorithms, and Hardware Implementation
Arie Ogranovich, Taosha Guo, Arvind R. Venkatakrishnan +3
Associative memory systems enable content-addressable storage and retrieval of patterns, a capability central to biological neural computation and artificial intelligence. Classica…
Competition, stability, and functionality in excitatory-inhibitory neural circuits
Simone Betteti, William Retnaraj, Alexander Davydov +2
Energy-based models have become a central paradigm for understanding computation and stability in both theoretical neuroscience and machine learning. However, the energetic framewo…
Contraction and concentration of measures with applications to theoretical neuroscience
Simone Betteti, Francesco Bullo
We investigate the asymptotic behavior of probability measures associated with stochastic dynamical systems featuring either globally contracting or -contracting drift terms…
Oscillatory Associative Memory with Exponential Capacity
Taosha Guo, Arie Ogranovich, Arvind R. Venkatakrishnan +3
The slowing of Moore's law and the increasing energy demands of machine learning present critical challenges for both the hardware and machine learning communities, and drive the d…
Firing Rate Models as Associative Memory: Excitatory-Inhibitory Balance for Robust Retrieval
Simone Betteti, Giacomo Baggio, Francesco Bullo +1
Firing rate models are dynamical systems widely used in applied and theoretical neuroscience to describe local cortical dynamics in neuronal populations. By providing a macroscopic…
Input-Driven Dynamics for Robust Memory Retrieval in Hopfield Networks
Simone Betteti, Giacomo Baggio, Francesco Bullo +1
The Hopfield model provides a mathematically idealized yet insightful framework for understanding the mechanisms of memory storage and retrieval in the human brain. This model has…