AI Medical Compendium Topic:
Models, Neurological

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Controversial stimuli: Pitting neural networks against each other as models of human cognition.

Proceedings of the National Academy of Sciences of the United States of America
Distinct scientific theories can make similar predictions. To adjudicate between theories, we must design experiments for which the theories make distinct predictions. Here we consider the problem of comparing deep neural networks as models of human ...

Stability, bifurcation and phase-locking of time-delayed excitatory-inhibitory neural networks.

Mathematical biosciences and engineering : MBE
We study a model for a network of synaptically coupled, excitable neurons to identify the role of coupling delays in generating different network behaviors. The network consists of two distinct populations, each of which contains one excitatory-inhib...

Artificial Neural Networks for Neuroscientists: A Primer.

Neuron
Artificial neural networks (ANNs) are essential tools in machine learning that have drawn increasing attention in neuroscience. Besides offering powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models fo...

Rapid Recalibration of Peri-Personal Space: Psychophysical, Electrophysiological, and Neural Network Modeling Evidence.

Cerebral cortex (New York, N.Y. : 1991)
Interactions between individuals and the environment occur within the peri-personal space (PPS). The encoding of this space plastically adapts to bodily constraints and stimuli features. However, these remapping effects have not been demonstrated on ...

Dynamics and bifurcations in multistable 3-cell neural networks.

Chaos (Woodbury, N.Y.)
We disclose the generality of the intrinsic mechanisms underlying multistability in reciprocally inhibitory 3-cell circuits composed of simplified, low-dimensional models of oscillatory neurons, as opposed to those of a detailed Hodgkin-Huxley type [...

Invertible generalized synchronization: A putative mechanism for implicit learning in neural systems.

Chaos (Woodbury, N.Y.)
Regardless of the marked differences between biological and artificial neural systems, one fundamental similarity is that they are essentially dynamical systems that can learn to imitate other dynamical systems whose governing equations are unknown. ...

Automated MRI-Based Deep Learning Model for Detection of Alzheimer's Disease Process.

International journal of neural systems
In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). The advanced deep learning method, a novel brain imagi...

Automatic Adaptation of Model Neurons and Connections to Build Hybrid Circuits with Living Networks.

Neuroinformatics
Hybrid circuits built by creating mono- or bi-directional interactions among living cells and model neurons and synapses are an effective way to study neuron, synaptic and neural network dynamics. However, hybrid circuit technology has been largely u...

Extreme value theory of evolving phenomena in complex dynamical systems: Firing cascades in a model of a neural network.

Chaos (Woodbury, N.Y.)
We extend the scope of the dynamical theory of extreme values to include phenomena that do not happen instantaneously but evolve over a finite, albeit unknown at the onset, time interval. We consider complex dynamical systems composed of many individ...