Two trailblazing computer scientists have won the 2024 Turing Award for their work in reinforcement learning, a discipline in ...

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Abstract: In this paper, by taking a locally active memristor (LAM) as the neuronal synapse to link two identical/disparate ReLU-type Hopfield neural networks (RHNNs), the memristive homogeneous and ...

However, most existing neural networks capable of generating complex dynamics are continuous-time models with high dimensionality, resulting in significant resource overhead for digital implementation ...

Associate Professor, Physics, CWRU; Associate Director, Center for AI in Disease Biology, CWRU ...

Unlike traditional electron microscopy, which only visualizes synapses, this method also measures connection strength, providing deeper insight into brain network function. The chip mimics patch-clamp ...

Unlike traditional electron microscopy, which only visualizes synapses, this method also measures connection strength, providing deeper insight into brain network function. The chip mimics patch-clamp ...

“This is a leap forward in understanding how the brain does such a good job of detecting novelty,” said Yuste, noting that the model that Shymkiv created builds on the ideas of John Hopfield, who won ...

Living organisms persist by virtue of complex interactions among many components organized into dynamic, environment-responsive networks that span multiple scales and dimensions. Biological networks ...