AI Medical Compendium Journal:
Neuron

Showing 31 to 40 of 49 articles

Machine learning identification of enhancers in the rhesus macaque genome.

Neuron
Nonhuman primate (NHP) neuroanatomy and cognitive complexity make NHPs ideal models to study human neurobiology and disease. However, NHP circuit-function investigations are limited by the availability of molecular reagents that are effective in NHPs...

Toward biologically realistic models of the motor system.

Neuron
In this issue of Neuron, Chiappa et al. describe how neural networks can be trained to perform complex hand motor skills. A key to their approach is curriculum learning, breaking learning into stages, leading to good control.

Internal world models in humans, animals, and AI.

Neuron
How do brains-biological or artificial-respond and adapt to an ever-changing environment? In a recent meeting, experts from various fields of neuroscience and artificial intelligence met to discuss internal world models in brains and machines, arguin...

Hanan Salam.

Neuron
An AI and robotics researcher, and an entrepreneur, Hanan Salam tells Neuron about her work on Artificial Social Intelligence and experience as the co-founder of Women in AI. Her passion for science, equity, and education nurtures her advocacy of tec...

Yulong Li.

Neuron
In an interview with Neuron, Yulong Li discusses optical tool development and next steps to interrogate the whole brain. He further shares the importance of interdisciplinarity; how new tools for neural imaging, perturbation, and artificial intellige...

Perception and memory in the medial temporal lobe: Deep learning offers a new lens on an old debate.

Neuron
In this issue of Neuron, Bonnen et al. (2021) use artificial neural networks to resolve a long-standing controversy surrounding the neurocognitive dichotomy between memory and perception. They show that the perirhinal cortex supports performance on t...

Visualizing a joint future of neuroscience and neuromorphic engineering.

Neuron
Recent research resolves the challenging problem of building biophysically plausible spiking neural models that are also capable of complex information processing. This advance creates new opportunities in neuroscience and neuromorphic engineering, w...

A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives.

Neuron
Extracting behavioral measurements non-invasively from video is stymied by the fact that it is a hard computational problem. Recent advances in deep learning have tremendously advanced our ability to predict posture directly from videos, which has qu...

Artificial Neural Networks for Neuroscientists: A Primer.

Neuron
Artificial neural networks (ANNs) are essential tools in machine learning that have drawn increasing attention in neuroscience. Besides offering powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models fo...

Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks.

Neuron
Evolution is a blind fitting process by which organisms become adapted to their environment. Does the brain use similar brute-force fitting processes to learn how to perceive and act upon the world? Recent advances in artificial neural networks have ...