AIMC Topic: Synapses

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Invertible generalized synchronization: A putative mechanism for implicit learning in neural systems.

Chaos (Woodbury, N.Y.) Jun 1, 2020
Regardless of the marked differences between biological and artificial neural systems, one fundamental similarity is that they are essentially dynamical systems that can learn to imitate other dynamical systems whose governing equations are unknown. ...
Synapses Nerve Net Humans Neural Networks, Computer Learning Models, Neurological
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Automatic Adaptation of Model Neurons and Connections to Build Hybrid Circuits with Living Networks.

Neuroinformatics Jun 1, 2020
Hybrid circuits built by creating mono- or bi-directional interactions among living cells and model neurons and synapses are an effective way to study neuron, synaptic and neural network dynamics. However, hybrid circuit technology has been largely u...
Neural Networks, Computer Synapses Models, Neurological Central Pattern Generators Animals Brachyura
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Dynamic behaviors of hyperbolic-type memristor-based Hopfield neural network considering synaptic crosstalk.

Chaos (Woodbury, N.Y.) Mar 1, 2020
Crosstalk phenomena taking place between synapses can influence signal transmission and, in some cases, brain functions. It is thus important to discover the dynamic behaviors of the neural network infected by synaptic crosstalk. To achieve this, in ...
Humans Nonlinear Dynamics Neural Networks, Computer Synapses Models, Neurological
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Memristive synapses with high reproducibility for flexible neuromorphic networks based on biological nanocomposites.

Nanoscale Jan 2, 2020
Memristive synapses from biomaterials are promising for building flexible and implantable artificial neuromorphic systems due to their remarkable mechanical and biological properties. However, these biological devices have relatively poor memristive ...
Biocompatible Materials Synapses Silver Nanocomposites Electric Conductivity Biomimetic Materials Carrageenan Neural Networks, Computer Machine Learning Reproducibility of Results
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Analysis of the Memristor-Based Crossbar Synapse for Neuromorphic Systems.

Journal of nanoscience and nanotechnology Oct 1, 2019
In this study, we analyzed the memristor device typically used as a synapse in neuromorphic architecture and confirmed that the synaptic memristor device can be adopted to perform the machine learning algorithm. The nonlinear characteristics of the m...
Synapses Neural Networks, Computer
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Automatic Classification for the Type of Multiple Synapse Based on Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
Recent studies have shown that the synaptic plasticity induced by development and learning can promote the formation of multiple synapse. With the rapid development of electron microscopy (EM) technology, we can closely observe the multiple synapse s...
Deep Learning Neuronal Plasticity Synapses Microscopy, Electron
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[Artificial Intelligence and Cerebellar Motor Learning].

Brain and nerve = Shinkei kenkyu no shinpo Jul 1, 2019
Half a century ago, cerebellar learning models based on a simple perceptron were proposed independently by Marr and Albus. Soon, these models were combined with Ito's flocculus hypothesis that the cerebellar flocculus controls the vestibulo-ocular re...
Reflex, Vestibulo-Ocular Artificial Intelligence Humans Synapses Neuronal Plasticity Models, Neurological Neural Networks, Computer Cerebellum
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Gradient and Hamiltonian coupled systems on undirected networks.

Mathematical biosciences and engineering : MBE May 23, 2019
Many real world applications are modelled by coupled systems on undirected networks. Two striking classes of such systems are the gradient and the Hamiltonian systems. In fact, within these two classes, coupled systems are admissible only by the undi...
Humans Synapses Neurons Brain Nonlinear Dynamics Neural Networks, Computer Models, Biological Algorithms
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A role for optics in AI hardware.

Nature May 1, 2019
Synapses Optics and Photonics Artificial Intelligence
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Computing of temporal information in spiking neural networks with ReRAM synapses.

Faraday discussions Feb 18, 2019
Resistive switching random-access memory (ReRAM) is a two-terminal device based on ion migration to induce resistance switching between a high resistance state (HRS) and a low resistance state (LRS). ReRAM is considered one of the most promising tech...
Nerve Net Computer Simulation Information Storage and Retrieval Neural Networks, Computer Learning Pattern Recognition, Automated Animals Algorithms Synapses Humans
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