AI Medical Compendium Topic:
Models, Neurological

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Phase-locking intermittency induced by dynamical heterogeneity in networks of thermosensitive neurons.

Chaos (Woodbury, N.Y.)
In this work, we study the phase synchronization of a neural network and explore how the heterogeneity in the neurons' dynamics can lead their phases to intermittently phase-lock and unlock. The neurons are connected through chemical excitatory conne...

A neuro-symbolic method for understanding free-text medical evidence.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We introduce Medical evidence Dependency (MD)-informed attention, a novel neuro-symbolic model for understanding free-text clinical trial publications with generalizability and interpretability.

Training Spiking Neural Networks in the Strong Coupling Regime.

Neural computation
Recurrent neural networks trained to perform complex tasks can provide insight into the dynamic mechanism that underlies computations performed by cortical circuits. However, due to a large number of unconstrained synaptic connections, the recurrent ...

Transition to synchronization in heterogeneous inhibitory neural networks with structured synapses.

Chaos (Woodbury, N.Y.)
Inhibitory neurons form an extensive network involved in the development of different rhythms in the cerebral cortex. A transition from an incoherent state, where all inhibitory neurons fire unrelated to each other, to a synchronized or locked state,...

An ecologically motivated image dataset for deep learning yields better models of human vision.

Proceedings of the National Academy of Sciences of the United States of America
Deep neural networks provide the current best models of visual information processing in the primate brain. Drawing on work from computer vision, the most commonly used networks are pretrained on data from the ImageNet Large Scale Visual Recognition ...

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...

Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input.

Proceedings of the National Academy of Sciences of the United States of America
Before they even speak, infants become attuned to the sounds of the language(s) they hear, processing native phonetic contrasts more easily than nonnative ones. For example, between 6 to 8 mo and 10 to 12 mo, infants learning American English get bet...

In Vivo Assay of Cortical Microcircuitry in Frontotemporal Dementia: A Platform for Experimental Medicine Studies.

Cerebral cortex (New York, N.Y. : 1991)
The analysis of neural circuits can provide crucial insights into the mechanisms of neurodegeneration and dementias, and offer potential quantitative biological tools to assess novel therapeutics. Here we use behavioral variant frontotemporal dementi...

New role for circuit expansion for learning in neural networks.

Physical review. E
Many sensory pathways in the brain include sparsely active populations of neurons downstream from the input stimuli. The biological purpose of this expanded structure is unclear, but it may be beneficial due to the increased expressive power of the n...

Predicting the fMRI Signal Fluctuation with Recurrent Neural Networks Trained on Vascular Network Dynamics.

Cerebral cortex (New York, N.Y. : 1991)
Resting-state functional MRI (rs-fMRI) studies have revealed specific low-frequency hemodynamic signal fluctuations (<0.1 Hz) in the brain, which could be related to neuronal oscillations through the neurovascular coupling mechanism. Given the vascul...