When is it better to use agent-based (AB) models, and when should differential equation (DE) models be used? Whereas DE models assume homogeneity and perfect mixing within compartments, AB models can ...

Two new approaches allow deep neural networks to solve entire families of partial differential equations, making it easier to model complicated systems and to do so orders of magnitude faster.

We also examine the usefulness of a class of novel prediction models called "artificial neural networks" and investigate the issue of appropriate window sizes for rolling-window-based prediction ...

Secondly, traditional ANNs have arbitrary structures that do not reflect how real brain networks are organized. By integrating brain connectomics into the construction of ANN architectures, ...

This new technical paper titled “Machine-Learning-Based Compact Modeling for Sub-3-nm-Node Emerging Transistors” was published by researchers at SungKyunKwan University, Korea. Abstract: “In this ...

Reversible synaptic plasticity between sensory neurons and interneurons, switching between inhibitory and excitatory, underpins the neural basis of salt concentration memory-dependent preference ...

In 2020, the team solved this by using liquid neural networks with 19 nodes, so 19 neurons plus a small perception module could drive a car. A differential equation describes each node of that system.

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