AIMC Topic: Neurons

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Synaptic Learning With Augmented Spikes.

IEEE transactions on neural networks and learning systems
Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones. With a more brain-like processing paradigm, spiking neurons are more promising for improvements ...

Versatile memristor for memory and neuromorphic computing.

Nanoscale horizons
The memristor is a promising candidate to implement high-density memory and neuromorphic computing. Based on the characteristic retention time, memristors are classified into volatile and non-volatile types. However, a single memristor generally prov...

Broad Echo State Network with Reservoir Pruning for Nonstationary Time Series Prediction.

Computational intelligence and neuroscience
The nonstationary time series is generated in various natural and man-made systems, of which the prediction is vital for advanced control and management. The neural networks have been explored in the time series prediction, but the problem remains in...

A new automatic forecasting method based on a new input significancy test of a single multiplicative neuron model artificial neural network.

Network (Bristol, England)
The model adequacy and input significance tests have not been proposed as features for the specification of a single multiplicative neuron model artificial neural networks in the literature. Moreover, there is no systematic approach based on hypothes...

Organic electrochemical neurons and synapses with ion mediated spiking.

Nature communications
Future brain-machine interfaces, prosthetics, and intelligent soft robotics will require integrating artificial neuromorphic devices with biological systems. Due to their poor biocompatibility, circuit complexity, low energy efficiency, and operating...

Spatiotemporal dynamics in spiking recurrent neural networks using modified-full-FORCE on EEG signals.

Scientific reports
Methods on modelling the human brain as a Complex System have increased remarkably in the literature as researchers seek to understand the underlying foundations behind cognition, behaviour, and perception. Computational methods, especially Graph The...

Zero-Hopf Bifurcation of a memristive synaptic Hopfield neural network with time delay.

Neural networks : the official journal of the International Neural Network Society
This paper proposes a novel memristive synaptic Hopfield neural network (MHNN) with time delay by using a memristor synapse to simulate the electromagnetic induced current caused by the membrane potential difference between two adjacent neurons. Firs...

Reconstruction of a Fully Paralleled Auditory Spiking Neural Network and FPGA Implementation.

IEEE transactions on biomedical circuits and systems
This paper presents a field-programmable gate array (FPGA) implementation of an auditory system, which is biologically inspired and has the advantages of robustness and anti-noise ability. We propose an FPGA implementation of an eleven-channel hierar...

Binding events through the mutual synchronization of spintronic nano-neurons.

Nature communications
The brain naturally binds events from different sources in unique concepts. It is hypothesized that this process occurs through the transient mutual synchronization of neurons located in different regions of the brain when the stimulus is presented. ...

Backpropagation Neural Tree.

Neural networks : the official journal of the International Neural Network Society
We propose a novel algorithm called Backpropagation Neural Tree (BNeuralT), which is a stochastic computational dendritic tree. BNeuralT takes random repeated inputs through its leaves and imposes dendritic nonlinearities through its internal connect...