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Models, Neurological

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Spiking Neural P Systems With Enzymes.

IEEE transactions on nanobioscience
The neurotransmitter is a chemical substance that transmits information between neurons. Its metabolic process includes four links: synthesis, storage, release and inactivation. As one of the important chemical components of neurotransmitters, acetyl...

How to incorporate biological insights into network models and why it matters.

The Journal of physiology
Due to the staggering complexity of the brain and its neural circuitry, neuroscientists rely on the analysis of mathematical models to elucidate its function. From Hodgkin and Huxley's detailed description of the action potential in 1952 to today, ne...

Multilevel development of cognitive abilities in an artificial neural network.

Proceedings of the National Academy of Sciences of the United States of America
Several neuronal mechanisms have been proposed to account for the formation of cognitive abilities through postnatal interactions with the physical and sociocultural environment. Here, we introduce a three-level computational model of information pro...

Geometry of neural computation unifies working memory and planning.

Proceedings of the National Academy of Sciences of the United States of America
Real-world tasks require coordination of working memory, decision-making, and planning, yet these cognitive functions have disproportionately been studied as independent modular processes in the brain. Here, we propose that contingency representation...

CerebelluMorphic: Large-Scale Neuromorphic Model and Architecture for Supervised Motor Learning.

IEEE transactions on neural networks and learning systems
The cerebellum plays a vital role in motor learning and control with supervised learning capability, while neuromorphic engineering devises diverse approaches to high-performance computation inspired by biological neural systems. This article present...

Perturbation of Spike Timing Benefits Neural Network Performance on Similarity Search.

IEEE transactions on neural networks and learning systems
Perturbation has a positive effect, as it contributes to the stability of neural systems through adaptation and robustness. For example, deep reinforcement learning generally engages in exploratory behavior by injecting noise into the action space an...

Dynamic branching in a neural network model for probabilistic prediction of sequences.

Journal of computational neuroscience
An important function of the brain is to predict which stimulus is likely to occur based on the perceived cues. The present research studied the branching behavior of a computational network model of populations of excitatory and inhibitory neurons, ...

An integrated deep learning-based model of spatial cells that combines self-motion with sensory information.

Hippocampus
A special class of neurons in the hippocampal formation broadly known as the spatial cells, whose subcategories include place cells, grid cells, and head direction cells, are considered to be the building blocks of the brain's map of the spatial worl...

Digital computing through randomness and order in neural networks.

Proceedings of the National Academy of Sciences of the United States of America
We propose that coding and decoding in the brain are achieved through digital computation using three principles: relative ordinal coding of inputs, random connections between neurons, and belief voting. Due to randomization and despite the coarsenes...

The impact of sparsity in low-rank recurrent neural networks.

PLoS computational biology
Neural population dynamics are often highly coordinated, allowing task-related computations to be understood as neural trajectories through low-dimensional subspaces. How the network connectivity and input structure give rise to such activity can be ...