AI Medical Compendium Journal:
Current opinion in neurobiology

Showing 21 to 30 of 34 articles

What does the mind learn? A comparison of human and machine learning representations.

Current opinion in neurobiology
We present a brief review of modern machine learning techniques and their use in models of human mental representations, detailing three notable branches: spatial methods, logical methods and artificial neural networks. Each of these branches contain...

An integrative computational architecture for object-driven cortex.

Current opinion in neurobiology
Computational architecture for object-driven cortex Objects in motion activate multiple cortical regions in every lobe of the human brain. Do these regions represent a collection of independent systems, or is there an overarching functional architect...

Analyzing biological and artificial neural networks: challenges with opportunities for synergy?

Current opinion in neurobiology
Deep neural networks (DNNs) transform stimuli across multiple processing stages to produce representations that can be used to solve complex tasks, such as object recognition in images. However, a full understanding of how they achieve this remains e...

Normalization and pooling in hierarchical models of natural images.

Current opinion in neurobiology
Divisive normalization and subunit pooling are two canonical classes of computation that have become widely used in descriptive (what) models of visual cortical processing. Normative (why) models from natural image statistics can help constrain the f...

Harnessing networks and machine learning in neuropsychiatric care.

Current opinion in neurobiology
The development of next-generation therapies for neuropsychiatric illness will likely rely on a precise and accurate understanding of human brain dynamics. Toward this end, researchers have focused on collecting large quantities of neuroimaging data....

Texture and art with deep neural networks.

Current opinion in neurobiology
Although the study of biological vision and computer vision attempt to understand powerful visual information processing from different angles, they have a long history of informing each other. Recent advances in texture synthesis that were motivated...

Parsing learning in networks using brain-machine interfaces.

Current opinion in neurobiology
Brain-machine interfaces (BMIs) define new ways to interact with our environment and hold great promise for clinical therapies. Motor BMIs, for instance, re-route neural activity to control movements of a new effector and could restore movement to pe...

New insights into olivo-cerebellar circuits for learning from a small training sample.

Current opinion in neurobiology
Artificial intelligence such as deep neural networks exhibited remarkable performance in simulated video games and 'Go'. In contrast, most humanoid robots in the DARPA Robotics Challenge fell down to ground. The dramatic contrast in performance is ma...

Recurrent neural networks as versatile tools of neuroscience research.

Current opinion in neurobiology
Recurrent neural networks (RNNs) are a class of computational models that are often used as a tool to explain neurobiological phenomena, considering anatomical, electrophysiological and computational constraints. RNNs can either be designed to implem...

Editorial overview: Neurobiology of cognitive behavior: Complexity of neural computation and cognition.

Current opinion in neurobiology
We live in an age when our phones incorporate real-time updates on traffic and subway delays to help us navigate complex urban environments, vacuum cleaning robots map the layout of our apartments to optimize their cleaning strategies, and cars are b...