AI Medical Compendium Journal:
Journal of neurophysiology

Showing 11 to 20 of 43 articles

Forms of explanation and understanding for neuroscience and artificial intelligence.

Journal of neurophysiology
Much of the controversy evoked by the use of deep neural networks as models of biological neural systems amount to debates over what constitutes scientific progress in neuroscience. To discuss what constitutes scientific progress, one must have a goa...

Classic Hebbian learning endows feed-forward networks with sufficient adaptability in challenging reinforcement learning tasks.

Journal of neurophysiology
A common pitfall of current reinforcement learning agents implemented in computational models is in their inadaptability postoptimization. Najarro and Risi [Najarro E, Risi S. . 2020: 20719-20731, 2020] demonstrate how such adaptability may be salvag...

Resting-state functional network models for posttraumatic stress disorder.

Journal of neurophysiology
Four recent articles were examined for their use of resting-state functional magnetic resonance imaging on participants with posttraumatic symptoms. Theory-driven computations were complemented by the novel use of network metrics, which revealed redu...

Machine learning approaches reveal subtle differences in breathing and sleep fragmentation in -derived astrocytes ablated mice.

Journal of neurophysiology
Modern neurophysiology research requires the interrogation of high-dimensionality data sets. Machine learning and artificial intelligence (ML/AI) workflows have permeated into nearly all aspects of daily life in the developed world but have not been ...

Reliability of robotic transcranial magnetic stimulation motor mapping.

Journal of neurophysiology
Robotic transcranial magnetic stimulation (TMS) is a noninvasive and safe tool that produces cortical motor maps using neuronavigational and neuroanatomical images. Motor maps are individualized representations of the primary motor cortex (M1) topogr...

Population coding in the cerebellum: a machine learning perspective.

Journal of neurophysiology
The cere resembles a feedforward, three-layer network of neurons in which the "hidden layer" consists of Purkinje cells (P-cells) and the output layer consists of deep cerebellar nucleus (DCN) neurons. In this analogy, the output of each DCN neuron i...

Predicting memory from study-related brain activity.

Journal of neurophysiology
To isolate brain activity that may reflect effective cognitive processes during the study phase of a memory task, cognitive neuroscientists commonly contrast brain activity during study of later-remembered versus later-forgotten items. This "subseque...

Using deep neural networks to detect complex spikes of cerebellar Purkinje cells.

Journal of neurophysiology
One of the most powerful excitatory synapses in the brain is formed by cerebellar climbing fibers, originating from neurons in the inferior olive, that wrap around the proximal dendrites of cerebellar Purkinje cells. The activation of a single olivar...

Neurotransmitter networks in mouse prefrontal cortex are reconfigured by isoflurane anesthesia.

Journal of neurophysiology
This study quantified eight small-molecule neurotransmitters collected simultaneously from prefrontal cortex of C57BL/6J mice ( = 23) during wakefulness and during isoflurane anesthesia (1.3%). Using isoflurane anesthesia as an independent variable e...

Before and beyond the Wilson-Cowan equations.

Journal of neurophysiology
The Wilson-Cowan equations represent a landmark in the history of computational neuroscience. Along with the insights Wilson and Cowan offered for neuroscience, they crystallized an approach to modeling neural dynamics and brain function. Although th...