AI Medical Compendium Journal:
Trends in neurosciences

Showing 1 to 10 of 10 articles

Continual task learning in natural and artificial agents.

Trends in neurosciences
How do humans and other animals learn new tasks? A wave of brain recording studies has investigated how neural representations change during task learning, with a focus on how tasks can be acquired and coded in ways that minimise mutual interference....

Using artificial neural networks to ask 'why' questions of minds and brains.

Trends in neurosciences
Neuroscientists have long characterized the properties and functions of the nervous system, and are increasingly succeeding in answering how brains perform the tasks they do. But the question 'why' brains work the way they do is asked less often. The...

Contributions by metaplasticity to solving the Catastrophic Forgetting Problem.

Trends in neurosciences
Catastrophic forgetting (CF) refers to the sudden and severe loss of prior information in learning systems when acquiring new information. CF has been an Achilles heel of standard artificial neural networks (ANNs) when learning multiple tasks sequent...

Informing deep neural networks by multiscale principles of neuromodulatory systems.

Trends in neurosciences
Our brains have evolved the ability to configure and adapt their processing states to match the unique challenges of acting and learning in diverse environments and behavioral contexts. In biological nervous systems, such state specification and adap...

Learning offline: memory replay in biological and artificial reinforcement learning.

Trends in neurosciences
Learning to act in an environment to maximise rewards is among the brain's key functions. This process has often been conceptualised within the framework of reinforcement learning, which has also gained prominence in machine learning and artificial i...

Deep learning and the Global Workspace Theory.

Trends in neurosciences
Recent advances in deep learning have allowed artificial intelligence (AI) to reach near human-level performance in many sensory, perceptual, linguistic, and cognitive tasks. There is a growing need, however, for novel, brain-inspired cognitive archi...

Network Dynamics Governed by Lyapunov Functions: From Memory to Classification.

Trends in neurosciences
In 1982, John Hopfield published a neural network model for memory retrieval, a model that became a cornerstone in theoretical neuroscience. In a recent paper, Krotov and Hopfield built on these early studies and showed how a network that incorporate...

Social Cognition in the Age of Human-Robot Interaction.

Trends in neurosciences
Artificial intelligence advances have led to robots endowed with increasingly sophisticated social abilities. These machines speak to our innate desire to perceive social cues in the environment, as well as the promise of robots enhancing our daily l...

Machine Learning and Brain Imaging: Opportunities and Challenges.

Trends in neurosciences
Machine learning approaches may provide ways to link brain activation patterns to behavior at an individual-subject level. Using a comparative performance analysis, Jollans and colleagues (Neuroimage, 2019) highlight in a recent paper key considerati...

Towards the automatic classification of neurons.

Trends in neurosciences
The classification of neurons into types has been much debated since the inception of modern neuroscience. Recent experimental advances are accelerating the pace of data collection. The resulting growth of information about morphological, physiologic...