AIMC Topic: Models, Neurological

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Roman domination-based spiking neural network for optimized EEG signal classification of four class motor imagery.

Computers in biology and medicine
The Spiking Neural Network (SNN) is a third-generation neural network recognized for its energy efficiency and ability to process spatiotemporal information, closely imitating the behavioral mechanisms of biological neurons in the brain. SNN exhibit ...

Spatial reasoning via recurrent neural dynamics in mouse retrosplenial cortex.

Nature neuroscience
From visual perception to language, sensory stimuli change their meaning depending on previous experience. Recurrent neural dynamics can interpret stimuli based on externally cued context, but it is unknown whether they can compute and employ interna...

Fusing multisensory signals across channels and time.

PLoS computational biology
Animals continuously combine information across sensory modalities and time, and use these combined signals to guide their behaviour. Picture a predator watching their prey sprint and screech through a field. To date, a range of multisensory algorith...

Decreased spinal inhibition leads to undiversified locomotor patterns.

Biological cybernetics
During walking and running, animals display rich and coordinated motor patterns that are generated and controlled within the central nervous system. Previous computational and experimental results suggest that the balance between excitation and inhib...

Concept transfer of synaptic diversity from biological to artificial neural networks.

Nature communications
Recent developments in artificial neural networks have drawn inspiration from biological neural networks, leveraging the concept of the artificial neuron to model the learning abilities of biological nerve cells. However, while neuroscience has provi...

Modeling Normal and Abnormal Circuit Development with Recurrent Neural Networks.

Cold Spring Harbor perspectives in biology
Neural development must construct neural circuits that can perform the computations necessary for survival. However, many theoretical models of development do not explicitly address the computational goals of the resulting networks, or computations t...

Egocentric value maps of the near-body environment.

Nature neuroscience
Body-part-centered response fields are pervasive in single neurons, functional magnetic resonance imaging, electroencephalography and behavior, but there is no unifying formal explanation of their origins and role. In the present study, we used reinf...

Neuron-astrocyte associative memory.

Proceedings of the National Academy of Sciences of the United States of America
Astrocytes, the most abundant type of glial cell, play a fundamental role in memory. Despite most hippocampal synapses being contacted by an astrocyte, there are no current theories that explain how neurons, synapses, and astrocytes might collectivel...

Automated inference of disease mechanisms in patient-hiPSC-derived neuronal networks.

Communications biology
Human induced pluripotent stem cells (hiPSCs)-derived neuronal networks on multi-electrode arrays (MEAs) are a powerful tool for studying neurological disorders. The electric activity patterns of these networks differ between healthy and patient-deri...

Parvalbumin neurons and cortical coding of dynamic stimuli: a network model.

Journal of neurophysiology
Cortical circuits feature both excitatory and inhibitory cells that underlie the encoding of dynamic sensory stimuli, e.g., speech, music, odors, and natural scenes. Although previous studies have shown that inhibition plays an important role in shap...