AIMC Topic: Neuronal Plasticity

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Cocaine-Induced Preference Conditioning: a Machine Vision Perspective.

Neuroinformatics
Existing work on drug-induced synaptic changes has shown that the expression of perineuronal nets (PNNs) at the cerebellar cortex can be regulated by cocaine-related memory. However, these studies on animals have mostly relied on limited manually-dri...

Recent Progress on Neuromorphic Synapse Electronics: From Emerging Materials, Devices, to Neural Networks.

Journal of nanoscience and nanotechnology
To realize intelligent functions in electronic devices like a human brain, it is important to develop the electronic devices that can imitate biological neurons and synapses (synaptic electronics). In this paper, we review the critical learning mecha...

Attractor dynamics of a Boolean model of a brain circuit controlled by multiple parameters.

Chaos (Woodbury, N.Y.)
Studies of Boolean recurrent neural networks are briefly introduced with an emphasis on the attractor dynamics determined by the sequence of distinct attractors observed in the limit cycles. We apply this framework to a simplified model of the basal ...

Propagation delays determine neuronal activity and synaptic connectivity patterns emerging in plastic neuronal networks.

Chaos (Woodbury, N.Y.)
In plastic neuronal networks, the synaptic strengths are adapted to the neuronal activity. Specifically, spike-timing-dependent plasticity (STDP) is a fundamental mechanism that modifies the synaptic strengths based on the relative timing of pre- and...

Spiking Neural Networks with Unsupervised Learning Based on STDP Using Resistive Synaptic Devices and Analog CMOS Neuron Circuit.

Journal of nanoscience and nanotechnology
We designed the CMOS analog integrate and fire (I&F) neuron circuit can drive resistive synaptic device. The neuron circuit consists of a current mirror for spatial integration, a capacitor for temporal integration, asymmetric negative and positive p...

Computational Principles of Supervised Learning in the Cerebellum.

Annual review of neuroscience
Supervised learning plays a key role in the operation of many biological and artificial neural networks. Analysis of the computations underlying supervised learning is facilitated by the relatively simple and uniform architecture of the cerebellum, a...

γ-Aminobutyric Acid Type A Receptor Potentiation Inhibits Learning in a Computational Network Model.

Anesthesiology
BACKGROUND: Propofol produces memory impairment at concentrations well below those abolishing consciousness. Episodic memory, mediated by the hippocampus, is most sensitive. Two potentially overlapping scenarios may explain how γ-aminobutyric acid re...

[Review of the research of spiking neuron network based on memristor].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
The rapid development of artificial intelligence put forward higher requirements for the computational speed, resource consumption and the biological interpretation of computational neuroscience. Spiking neuron networks can carry a large amount of in...

Robotic Assistance for Training Finger Movement Using a Hebbian Model: A Randomized Controlled Trial.

Neurorehabilitation and neural repair
BACKGROUND: Robots that physically assist movement are increasingly used in rehabilitation therapy after stroke, yet some studies suggest robotic assistance discourages effort and reduces motor learning.

Biological modelling of a computational spiking neural network with neuronal avalanches.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed ne...