Neuroscientists want to understand how individual neurons encode information that allows us to distinguish objects, like ...

In recent years, with the public availability of AI tools, more people have become aware of how closely the inner workings of ...

Predicting how molecular changes affect the brain's overall activity is a major challenge in neuroscience. Many deep ...

This important study demonstrates the significance of incorporating biological constraints in training neural networks to develop models that make accurate predictions under novel conditions. By ...

CPUs are far too mathematical and logical. The neural processing unit (NPU), on the other hand, takes an entirely different approach: simulating the structure of the human brain in its very circuitry.

In other words, the brain learns, in large part, by adjusting the connections between its neurons. In the perceptron, this meant assigning to each connection a “weight” — a number that determined the ...

And running logic-gate networks is cheap, fast, and easy: in his talk at the Neural Information Processing Systems (NeurIPS) conference, Petersen said that they consume less energy than perceptron ...

The basic building block of many of today’s successful networks is known as a multilayer perceptron, or MLP. But despite a string of successes, humans just can’t understand how networks built on these ...

This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed ...