scholarly journals A BDNF-Mediated Push-Pull Plasticity Mechanism for Synaptic Clustering

Cell Reports ◽  
2018 ◽  
Vol 24 (8) ◽  
pp. 2063-2074 ◽  
Author(s):  
Dragos Niculescu ◽  
Kristin Michaelsen-Preusse ◽  
Ülkü Güner ◽  
René van Dorland ◽  
Corette J. Wierenga ◽  
...  
Keyword(s):  
Plasticity Mechanism ◽  
Synaptic Clustering

Chaotic Liquid State Machine

2015 ◽  
Vol 9 (4) ◽  
pp. 1-20
Author(s):  
Mario Antoine Aoun ◽  
Mounir Boukadoum
Keyword(s):  
Liquid State ◽  
State Machine ◽  
Spiking Neurons ◽  
Signature Verification ◽  
Time Varying ◽  
Plasticity Mechanism ◽  
Online Signature ◽  
Online Signature Verification ◽  
One Class Classifier ◽  
Classification Capability

The authors implement a Liquid State Machine composed from a pool of chaotic spiking neurons. Furthermore, a synaptic plasticity mechanism operates on the connection weights between the neurons inside the pool. A special feature of the system's classification capability is that it can learn the class of a set of time varying inputs when trained from positive examples only, thus, it is a one class classifier. To demonstrate the applicability of this novel neurocomputing architecture, the authors apply it for Online Signature Verification.

Enumeration of the hydrogen-enhanced localized plasticity mechanism for hydrogen embrittlement in structural materials

2019 ◽  
Vol 165 ◽  
pp. 734-750 ◽  
Author(s):  
May L. Martin ◽  
Mohsen Dadfarnia ◽  
Akihide Nagao ◽  
Shuai Wang ◽  
Petros Sofronis
Keyword(s):  
Hydrogen Embrittlement ◽  
Structural Materials ◽  
Plasticity Mechanism

scholarly journals Rapid Bayesian learning in the mammalian olfactory system

2019 ◽  
Author(s):  
Naoki Hiratani ◽  
Peter E. Latham
Keyword(s):  
Olfactory System ◽  
Bayesian Learning ◽  
Experimental Studies ◽  
Bayesian Optimization ◽  
Mammalian Brain ◽  
Theoretical Understanding ◽  
Plasticity Mechanism ◽  
Neural Architecture ◽  
Learning Rules ◽  
Efficient Learning

AbstractMany experimental studies suggest that animals can rapidly learn to identify odors and predict the rewards associated with them. However, the underlying plasticity mechanism remains elusive. In particular, it is not clear how olfactory circuits achieve rapid, data efficient learning with local synaptic plasticity. Here, we formulate olfactory learning as a Bayesian optimization process, then map the learning rules into a computational model of the mammalian olfactory circuit. The model is capable of odor identification from a small number of observations, while reproducing cellular plasticity commonly observed during development. We extend the framework to reward-based learning, and show that the circuit is able to rapidly learn odor-reward association with a plausible neural architecture. These results deepen our theoretical understanding of unsupervised learning in the mammalian brain.

scholarly journals Synaptic clustering differences due to different GABRB3 mutations cause variable epilepsy syndromes

Brain ◽  
2019 ◽  
Vol 142 (10) ◽  
pp. 3028-3044 ◽  
Author(s):  
Yi-Wu Shi ◽  
Qi Zhang ◽  
Kefu Cai ◽  
Sarah Poliquin ◽  
Wangzhen Shen ◽  
...  
Keyword(s):  
Intellectual Disability ◽  
Gabaa Receptors ◽  
Disease Heterogeneity ◽  
Synaptic Clustering ◽  
Do So

Mutations in GABRB3, which encodes the β3 subunit of GABAA receptors, cause variable epilepsy syndromes with autism and intellectual disability. Shi et al. report that mutant β3 subunits reduce expression of wildtype γ2 subunits, which are critical for receptor synaptic clustering. However, they do so to different degrees, contributing to disease heterogeneity.

Classification of Correlated Patterns with a Configurable Analog VLSI Neural Network of Spiking Neurons and Self-Regulating Plastic Synapses

2009 ◽  
Vol 21 (11) ◽  
pp. 3106-3129 ◽  
Author(s):  
Massimilian Giulioni ◽  
Mario Pannunzi ◽  
Davide Badoni ◽  
Vittorio Dante ◽  
Paolo Del Giudice
Keyword(s):  
Marked Improvement ◽  
Analog Vlsi ◽  
Spiking Neurons ◽  
Learning Performance ◽  
Event Representation ◽  
Plasticity Mechanism ◽  
Integrate And Fire ◽  
Correlated Patterns ◽  
Synaptic Changes

We describe the implementation and illustrate the learning performance of an analog VLSI network of 32 integrate-and-fire neurons with spike-frequency adaptation and 2016 Hebbian bistable spike-driven stochastic synapses, endowed with a self-regulating plasticity mechanism, which avoids unnecessary synaptic changes. The synaptic matrix can be flexibly configured and provides both recurrent and external connectivity with address-event representation compliant devices. We demonstrate a marked improvement in the efficiency of the network in classifying correlated patterns, owing to the self-regulating mechanism.

Cyclic plasticity mechanism of the M330-35A steel

2016 ◽  
Vol 82 ◽  
pp. 530-539 ◽  
Author(s):  
Claire Schayes ◽  
Jean-Bernard Vogt ◽  
Jérémie Bouquerel ◽  
Frédéric Palleschi ◽  
Stefan Zaefferer
Keyword(s):  
Cyclic Plasticity ◽  
Plasticity Mechanism
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