4 papers
Self-Organized Learning in Oscillatory Neural Networks with Memristive Signed Couplings
Riley Acker, Aman Desai, Garrett Kenyon +1
Oscillatory neural networks (ONNs) have emerged as a promising neuromorphic architecture, leveraging coupled dynamical systems to perform computation and represent information thro…
How to Train Your Resistive Network: Generalized Equilibrium Propagation and Analytical Learning
Jonathan Lin, Aman Desai, Frank Barrows +1
Machine learning is a powerful method of extracting meaning from data; unfortunately, current digital hardware is extremely energy-intensive. There is interest in an alternative an…
Autonomous Learning of Attractors for Neuromorphic Computing with Wien Bridge Oscillator Networks
Riley Acker, Aman Desai, Garrett Kenyon +1
We present an oscillatory neuromorphic primitive implemented with networks of coupled Wien bridge oscillators and tunable resistive couplings. Phase relationships between oscillato…
A unifying approach to self-organizing systems interacting via conservation laws
Frank Barrows, Guanming Zhang, Satyam Anand +5
We present a unified framework for embedding and analyzing dynamical systems using generalized projection operators rooted in local conservation laws. By representing physical, bio…