AI Medical Compendium Topic:
Models, Neurological

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Estimating Scale-Invariant Future in Continuous Time.

Neural computation
Natural learners must compute an estimate of future outcomes that follow from a stimulus in continuous time. Widely used reinforcement learning algorithms discretize continuous time and estimate either transition functions from one step to the next (...

Extracellular GABA assisting in organizing dynamic cell assemblies to shorten reaction time to sensory stimulation.

Biological cybernetics
Until recently, glia, which exceeds the number of neurons, was considered to only have supportive roles in the central nervous system, providing homeostatic controls and metabolic supports. However, recent studies suggest that glia interacts with neu...

Predicting Human Saccadic Scanpaths Based on Iterative Representation Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Visual attention is a dynamic process of scene exploration and information acquisition. However, existing research on attention modeling has concentrated on estimating static salient locations. In contrast, dynamic attributes presented by saccade hav...

Merging information in the entorhinal cortex: what can we learn from robotics experiments and modeling?

The Journal of experimental biology
Place recognition is a complex process involving idiothetic and allothetic information. In mammals, evidence suggests that visual information stemming from the temporal and parietal cortical areas ('what' and 'where' information) is merged at the lev...

Control of a Humanoid NAO Robot by an Adaptive Bioinspired Cerebellar Module in 3D Motion Tasks.

Computational intelligence and neuroscience
A bioinspired adaptive model, developed by means of a spiking neural network made of thousands of artificial neurons, has been leveraged to control a humanoid NAO robot in real time. The learning properties of the system have been challenged in a cla...

Network structure and input integration in competing firing rate models for decision-making.

Journal of computational neuroscience
Making a decision among numerous alternatives is a pervasive and central undertaking encountered by mammals in natural settings. While decision making for two-option tasks has been studied extensively both experimentally and theoretically, characteri...

An Efficient Population Density Method for Modeling Neural Networks with Synaptic Dynamics Manifesting Finite Relaxation Time and Short-Term Plasticity.

eNeuro
When incorporating more realistic synaptic dynamics, the computational efficiency of population density methods (PDMs) declines sharply due to the increase in the dimension of master equations. To avoid such a decline, we develop an efficient PDM, te...

A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease.

NeuroImage
Some forms of mild cognitive impairment (MCI) are the clinical precursors of Alzheimer's disease (AD), while other MCI types tend to remain stable over-time and do not progress to AD. To identify and choose effective and personalized strategies to pr...

Task representations in neural networks trained to perform many cognitive tasks.

Nature neuroscience
The brain has the ability to flexibly perform many tasks, but the underlying mechanism cannot be elucidated in traditional experimental and modeling studies designed for one task at a time. Here, we trained single network models to perform 20 cogniti...

Hindmarsh-Rose neuron model with memristors.

Bio Systems
We analyze single and coupled Hindmarsh-Rose neurons in the presence of a time varying electromagnetic field which results from the exchange of ions across the membrane. Memristors are used to model the relation between magnetic flux of the electroma...