AI Medical Compendium Topic:
Models, Neurological

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The Complex Behaviour of a Simple Neural Oscillator Model in the Human Cortex.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The brain is a complex organ responsible for memory storage and reasoning; however, the mechanisms underlying these processes remain unknown. This paper forms a contribution to a lot of theoretical studies devoted to regular or chaotic oscillations o...

Synchronization-induced spike termination in networks of bistable neurons.

Neural networks : the official journal of the International Neural Network Society
We observe and study a self-organized phenomenon whereby the activity in a network of spiking neurons spontaneously terminates. We consider different types of populations, consisting of bistable model neurons connected electrically by gap junctions, ...

Individual differences in rate of acquiring stable neural representations of tasks in fMRI.

PloS one
Task-related functional magnetic resonance imaging (fMRI) is a widely-used tool for studying the neural processing correlates of human behavior in both healthy and clinical populations. There is growing interest in mapping individual differences in f...

Brain CT and MRI medical image fusion using convolutional neural networks and a dual-channel spiking cortical model.

Medical & biological engineering & computing
The aim of medical image fusion is to improve the clinical diagnosis accuracy, so the fused image is generated by preserving salient features and details of the source images. This paper designs a novel fusion scheme for CT and MRI medical images bas...

Fixed Points of Competitive Threshold-Linear Networks.

Neural computation
Threshold-linear networks (TLNs) are models of neural networks that consist of simple, perceptron-like neurons and exhibit nonlinear dynamics determined by the network's connectivity. The fixed points of a TLN, including both stable and unstable equi...

In vitro neural networks minimise variational free energy.

Scientific reports
In this work, we address the neuronal encoding problem from a Bayesian perspective. Specifically, we ask whether neuronal responses in an in vitro neuronal network are consistent with ideal Bayesian observer responses under the free energy principle....

A neural microcircuit model for a scalable scale-invariant representation of time.

Hippocampus
Scale-invariant timing has been observed in a wide range of behavioral experiments. The firing properties of recently described time cells provide a possible neural substrate for scale-invariant behavior. Earlier neural circuit models do not produce ...

Neuroevolution of a Modular Memory-Augmented Neural Network for Deep Memory Problems.

Evolutionary computation
We present Modular Memory Units (MMUs), a new class of memory-augmented neural network. MMU builds on the gated neural architecture of Gated Recurrent Units (GRUs) and Long Short Term Memory (LSTMs), to incorporate an external memory block, similar t...

Machine learning provides novel neurophysiological features that predict performance to inhibit automated responses.

Scientific reports
Neurophysiological features like event-related potentials (ERPs) have long been used to identify different cognitive sub-processes that may contribute to task performance. It has however remained unclear whether "classical" ERPs are truly the best re...

On String Languages Generated by Spiking Neural P Systems With Structural Plasticity.

IEEE transactions on nanobioscience
Spiking neural P systems (SNP systems) are parallel and non-deterministic models of computation, inspired by the neural system of the brain. A variant of SNP systems known as SNP systems with structural plasticity (SNPSP systems) includes the feature...