AIMC Topic: Neuronal Plasticity

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Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier.

Sensors (Basel, Switzerland)
One of the modern trends in the design of human-machine interfaces (HMI) is to involve the so called spiking neuron networks (SNNs) in signal processing. The SNNs can be trained by simple and efficient biologically inspired algorithms. In particular,...

Modeling of Brain-Like Concept Coding with Adulthood Neurogenesis in the Dentate Gyrus.

Computational intelligence and neuroscience
Mammalian brains respond to new concepts via a type of neural coding termed "concept coding." During concept coding, the dentate gyrus (DG) plays a vital role in pattern separation and pattern integration of concepts because it is a brain region with...

Discrimination of the behavioural dynamics of visually impaired infants via deep learning.

Nature biomedical engineering
Sensory loss is associated with behavioural changes, but how behavioural dynamics change when a sensory modality is impaired remains unclear. Here, by recording under a designed standardized scenario, the behavioural phenotypes of 4,196 infants who e...

Reinforcement Learning in Spiking Neural Networks with Stochastic and Deterministic Synapses.

Neural computation
Though succeeding in solving various learning tasks, most existing reinforcement learning (RL) models have failed to take into account the complexity of synaptic plasticity in the neural system. Models implementing reinforcement learning with spiking...

A review of learning in biologically plausible spiking neural networks.

Neural networks : the official journal of the International Neural Network Society
Artificial neural networks have been used as a powerful processing tool in various areas such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has encouraged researchers to improve artificial neural networks by ...

Adaptive Neural Sliding-Mode Controller for Alternative Control Strategies in Lower Limb Rehabilitation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Research on control strategies for rehabilitation robots has gradually shifted from providing therapies with fixed, relatively stiff assistance to compelling alternatives with assistance or challenge strategies to maximize subject participation. Thes...

Stable memory with unstable synapses.

Nature communications
What is the physiological basis of long-term memory? The prevailing view in Neuroscience attributes changes in synaptic efficacy to memory acquisition, implying that stable memories correspond to stable connectivity patterns. However, an increasing b...

A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule.

Neural networks : the official journal of the International Neural Network Society
Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are non-differentiable. Previous SNNs training methods can be generally cat...

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics.

Advanced materials (Deerfield Beach, Fla.)
Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next-generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behavior...

A comprehensive knowledge base of synaptic electrophysiology in the rodent hippocampal formation.

Hippocampus
The cellular and synaptic architecture of the rodent hippocampus has been described in thousands of peer-reviewed publications. However, no human- or machine-readable public catalog of synaptic electrophysiology data exists for this or any other neur...