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Found 4 papers, 2 papers with code
A Sparse Quantized Hopfield Network for Online-Continual Memory
An important difference between brains and deep neural networks is the way they learn.
Understanding and Improving Optimization in Predictive Coding Networks
Backpropagation (BP), the standard learning algorithm for artificial neural networks, is often considered biologically implausible.
A Theoretical Framework for Inference Learning
Our novel implementation considerably improves the stability of IL across learning rates, which is consistent with our theory, as a key property of implicit SGD is its stability.
A Framework to Explore Workload-Specific Performance and Lifetime Trade-offs in Neuromorphic Computing
Our framework first extracts the precise times at which a charge pump in the hardware is activated to support neural computations within a workload.
