AIMC Topic: Neuronal Plasticity

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Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for Neuroplasticity.

Neural plasticity
The effects of acupuncture facilitating neural plasticity for treating diseases have been identified by clinical and experimental studies. In the last two decades, the application of neuroimaging techniques in acupuncture research provided visualized...

General Distributed Neural Control and Sensory Adaptation for Self-Organized Locomotion and Fast Adaptation to Damage of Walking Robots.

Frontiers in neural circuits
Walking animals such as invertebrates can effectively perform self-organized and robust locomotion. They can also quickly adapt their gait to deal with injury or damage. Such a complex achievement is mainly performed via coordination between the legs...

Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics.

Nature communications
Sensory information processing in robot skins currently rely on a centralized approach where signal transduction (on the body) is separated from centralized computation and decision-making, requiring the transfer of large amounts of data from periphe...

Sparse coding with a somato-dendritic rule.

Neural networks : the official journal of the International Neural Network Society
Cortical neurons are silent most of the time: sparse activity enables low-energy computation in the brain, and promises to do the same in neuromorphic hardware. Beyond power efficiency, sparse codes have favourable properties for associative learning...

Single-Trial EEG Responses Classified Using Latency Features.

International journal of neural systems
Covert attention has been repeatedly shown to impact on EEG responses after single and repeated practice sessions. Machine learning techniques are increasingly adopted to classify single-trial EEG responses thereby primarily relying on amplitude-base...

A Mathematical Analysis of Memory Lifetime in a Simple Network Model of Memory.

Neural computation
We study the learning of an external signal by a neural network and the time to forget it when this network is submitted to noise. The presentation of an external stimulus to the recurrent network of binary neurons may change the state of the synapse...

Population coupling predicts the plasticity of stimulus responses in cortical circuits.

eLife
Some neurons have stimulus responses that are stable over days, whereas other neurons have highly plastic stimulus responses. Using a recurrent network model, we explore whether this could be due to an underlying diversity in their synaptic plasticit...

Neural memory plasticity for medical anomaly detection.

Neural networks : the official journal of the International Neural Network Society
In the domain of machine learning, Neural Memory Networks (NMNs) have recently achieved impressive results in a variety of application areas including visual question answering, trajectory prediction, object tracking, and language modelling. However,...

Gate-Tunable Synaptic Dynamics of Ferroelectric-Coupled Carbon-Nanotube Transistors.

ACS applied materials & interfaces
Artificial neural networks (ANNs) based on synaptic devices, which can simultaneously perform processing and storage of data, have superior computing performance compared to conventional von Neumann architectures. Here, we present a ferroelectric cou...