AIMC Topic: Models, Neurological

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A neural surveyor to map touch on the body.

Proceedings of the National Academy of Sciences of the United States of America
Perhaps the most recognizable sensory map in all of neuroscience is the somatosensory homunculus. Although it seems straightforward, this simple representation belies the complex link between an activation in a somatotopic map and the associated touc...

The neural architecture of language: Integrative modeling converges on predictive processing.

Proceedings of the National Academy of Sciences of the United States of America
The neuroscience of perception has recently been revolutionized with an integrative modeling approach in which computation, brain function, and behavior are linked across many datasets and many computational models. By revealing trends across models,...

Dynamic Spatiotemporal Pattern Recognition With Recurrent Spiking Neural Network.

Neural computation
Our real-time actions in everyday life reflect a range of spatiotemporal dynamic brain activity patterns, the consequence of neuronal computation with spikes in the brain. Most existing models with spiking neurons aim at solving static pattern recogn...

Neuromorphic learning with Mott insulator NiO.

Proceedings of the National Academy of Sciences of the United States of America
Habituation and sensitization (nonassociative learning) are among the most fundamental forms of learning and memory behavior present in organisms that enable adaptation and learning in dynamic environments. Emulating such features of intelligence fou...

Hidden coexisting firings in fractional-order hyperchaotic memristor-coupled HR neural network with two heterogeneous neurons and its applications.

Chaos (Woodbury, N.Y.)
The firing patterns of each bursting neuron are different because of the heterogeneity, which may be derived from the different parameters or external drives of the same kind of neurons, or even neurons with different functions. In this paper, the di...

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 ...