In resistor networks, physics computes voltages at selected output nodes automatically and rapidly by exploiting Kirchhoff’s laws when voltages are applied at input nodes. Such networks have been ...

This manuscript reports a valuable modeling study on sequence generation in the hippocampus in a variety of behavioral contexts. The authors model context-depending decision making, and suggest that ...

A Python library for deterministic associative memory in AI agents. Status: toy / research memory. Deterministic associative recall for small agent fact stores and learning Modern Hopfield networks.

This study presents a useful approach for revealing large-scale brain attractor dynamics during resting states, task processing, and disease conditions using insights from Hopfield neural networks.

This course describes how neural networks are used in machine learning. Neural networks are distributed computational models inspired by the structure of the human brain, consisting of many simple ...

Cellular neural networks, inspired in part by the biological retina, offer a potential route to massively parallel analogue computing. However, the hardware implementation of such systems remains ...

It’s 3 AM. You’re buried in spreadsheets, your brain feels like it’s been put through a blender, and your coffee has lost its magic. Sound familiar? Now, imagine this: the same mountain of work could ...

The PyCX project aims to develop an online repository of simple, crude, yet easy-to-understand Python sample codes for dynamic complex systems modeling and simulation, including iterative maps, ...

On Thursday, Google and the Computer History Museum (CHM) jointly released the source code for AlexNet, the convolutional neural network (CNN) that many credit with transforming the AI field in 2012 ...

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Which learning goals must individual computational elements pursue to contribute to a network-level task solution? This local understanding is missing in both biological, but also artificial neural ...