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Synapses

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Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data.

Cell reports
Serial section electron microscopy (ssEM) can provide comprehensive 3D ultrastructural information of the brain with exceptional computational cost. Targeted reconstruction of subcellular structures from ssEM datasets is less computationally demandin...

Neuromorphic learning with Mott insulator NiO.

Proceedings of the National Academy of Sciences of the United States of America
Habituation and sensitization (nonassociative learning) are among the most fundamental forms of learning and memory behavior present in organisms that enable adaptation and learning in dynamic environments. Emulating such features of intelligence fou...

Training Spiking Neural Networks in the Strong Coupling Regime.

Neural computation
Recurrent neural networks trained to perform complex tasks can provide insight into the dynamic mechanism that underlies computations performed by cortical circuits. However, due to a large number of unconstrained synaptic connections, the recurrent ...

Transition to synchronization in heterogeneous inhibitory neural networks with structured synapses.

Chaos (Woodbury, N.Y.)
Inhibitory neurons form an extensive network involved in the development of different rhythms in the cerebral cortex. A transition from an incoherent state, where all inhibitory neurons fire unrelated to each other, to a synchronized or locked state,...

3D Integrable W/SiN/n-Si/p-Si 1D1R Unipolar Resistive Random Access Memory Synapse for Suppressing Reverse Leakage in Spiking Neural Network.

Journal of nanoscience and nanotechnology
In this paper, we pose reverse leakage current issue which occurs when resistive random access memory (RRAM) is used as synapse for spiking neural networks (SNNs). To prevent this problem, 1 diode-1 RRAM (1D1R) synapse is suggested and simulated to e...

Dynamics and bifurcations in multistable 3-cell neural networks.

Chaos (Woodbury, N.Y.)
We disclose the generality of the intrinsic mechanisms underlying multistability in reciprocally inhibitory 3-cell circuits composed of simplified, low-dimensional models of oscillatory neurons, as opposed to those of a detailed Hodgkin-Huxley type [...

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

Chaos (Woodbury, N.Y.)
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. ...

Automatic Adaptation of Model Neurons and Connections to Build Hybrid Circuits with Living Networks.

Neuroinformatics
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...

Dynamic behaviors of hyperbolic-type memristor-based Hopfield neural network considering synaptic crosstalk.

Chaos (Woodbury, N.Y.)
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 ...