AIMC Topic: Neural Networks, Computer

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Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses.

Science advances
The human brain is a complex integrated spatiotemporal system, where space (which neuron fires) and time (when a neuron fires) both carry information to be processed by cognitive functions. To parallel the energy efficiency and computing functionalit...

LCD: A Fast Contrastive Divergence Based Algorithm for Restricted Boltzmann Machine.

Neural networks : the official journal of the International Neural Network Society
Restricted Boltzmann Machine (RBM) is the building block of Deep Belief Nets and other deep learning tools. Fast learning and prediction are both essential for practical usage of RBM-based machine learning techniques. This paper proposes Lean Contras...

The impact of encoding-decoding schemes and weight normalization in spiking neural networks.

Neural networks : the official journal of the International Neural Network Society
Spike-timing Dependent Plasticity (STDP) is a learning mechanism that can capture causal relationships between events. STDP is considered a foundational element of memory and learning in biological neural networks. Previous research efforts endeavore...

MoS Memristors Exhibiting Variable Switching Characteristics toward Biorealistic Synaptic Emulation.

ACS nano
Memristors based on 2D layered materials could provide biorealistic ionic interactions and potentially enable construction of energy-efficient artificial neural networks capable of faithfully emulating neuronal interconnections in human brains. To bu...

CNN-Based Multimodal Human Recognition in Surveillance Environments.

Sensors (Basel, Switzerland)
In the current field of human recognition, most of the research being performed currently is focused on re-identification of different body images taken by several cameras in an outdoor environment. On the other hand, there is almost no research bein...

Resilience of and recovery strategies for weighted networks.

PloS one
The robustness and resilience of complex networks have been widely studied and discussed in both research and industry because today, the diversity of system components and the complexity of the connection between units are increasingly influencing t...

Protocol-based state estimation for delayed Markovian jumping neural networks.

Neural networks : the official journal of the International Neural Network Society
This paper is concerned with the state estimation problem for a class of Markovian jumping neural networks (MJNNs) with sensor nonlinearities, mode-dependent time delays and stochastic disturbances subject to the Round-Robin (RR) scheduling mechanism...

Digital Multiplierless Realization of Coupled Wilson Neuron Model.

IEEE transactions on biomedical circuits and systems
The human brain is composed of 10 neurons with a switching speed of about 1 ms. Studying spiking neural networks, including the modeling, simulation, and implementation of the biological neuron models, helps us to learn about the brain and the relate...

Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT.

Medical physics
BACKGROUND: Accurate prediction of radiation toxicity of healthy organs-at-risks (OARs) critically determines the radiation therapy (RT) success. The existing dose-volume histogram-based metric may grossly under/overestimate the therapeutic toxicity ...

A novel training method to preserve generalization of RBPNN classifiers applied to ECG signals diagnosis.

Neural networks : the official journal of the International Neural Network Society
In this paper a novel training technique is proposed to offer an efficient solution for neural network training in non-trivial and critical applications such as the diagnosis of health threatening illness. The presented technique aims to enhance the ...