Abstract: By integrating memristors into a Hopfield neural network (HNN), a diverse range of dynamical behavior can be generated, which has significant implications for modeling and biomimetic ...

Previous work has shown that the dynamical regime of Recurrent Neural Networks (RNNs)—ranging from oscillatory to chaotic and fixed point behavior—can be controlled by the global distribution of ...

The final, formatted version of the article will be published soon. Previous work has shown that the dynamical regime of Recurrent Neural Networks (RNNs)—ranging from oscillatory to chaotic and fixed ...

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...

Calling the model on the input returns a 2-dimensional tensor with dim=0 corresponding to each output of 10 raw predicted values for each class, and dim=1 corresponding to the individual values of ...

Not revised: This Reviewed Preprint includes the authors’ original preprint (without revision), an eLife assessment, public reviews, and a provisional response from the authors. The manuscript by Ito ...

Astrocytes are the brain’s star-shaped support cells, and they might be doing more than just backing up neurons. While we usually credit neurons for storing memories and processing thoughts, ...

Listen to the first notes of an old, beloved song. Can you name that tune? If you can, congratulations — it’s a triumph of your associative memory, in which one piece of information (the first few ...

Summary: A new memory model called Input-Driven Plasticity (IDP) offers a more human-like explanation for how external stimuli help us retrieve memories, building on the foundations of the classic ...