Results 191 to 200 of about 40,549 (223)
Exfoliated‐MoS2 Gradual Resistive Switching Devices as Artificial Synapses
Advanced Electronic Materials, EarlyView.A vertical memristor based on untreated, exfoliated MoS2 is presented, revealing gradual resistive switching governed by Schottky barrier modulation at the MoS2/metal interface from the trapping/detrapping of charges. Furthermore, the device emulates synaptic‐like plasticity functions, including: potentiation, depression, and spike‐amplitude‐dependent ...
Deianira Fejzaj +3 more
wiley +1 more source
Advanced Electronic Materials, EarlyView.A lead‐free perovskite memristive solar cell structure that call emulate both synaptic and neuronal functions controlled by light and electric fields depending on top electrode type. ABSTRACT Memristive devices based on halide perovskites hold strong promise to provide energy‐efficient systems for the Internet of Things (IoT); however, lead (Pb ...
Michalis Loizos +4 more
wiley +1 more source
Synchronization of Analog Neuron Circuits With Digital Memristive Synapses: An Hybrid Approach
Advanced Electronic Materials, EarlyView.An hybrid circuit mimicking neural units coupled using memristive synapses is introduced. The analog neurons provide flexibility and robustness, and the digital memristive coupling guarantees the full reconfigurability of the interconnection. The onset of a synchronized spiking behavior in two circuits mimicking the Izhikevich neuron is discussed from ...
Lamberto Carnazza +3 more
wiley +1 more source
Advanced Electronic Materials, EarlyView.A fully coupled FEM–HH model shows that ideally capacitive microelectrodes can achieve lower charge‐density thresholds than Faradaic contacts under current‐controlled stimulation. The advantage stems from the dynamics of surface current density on capacitive interfaces, which redirects current beneath adherent neurons.
Aleksandar Opančar +2 more
wiley +1 more source
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Physical Review E, 2001
We propose two control strategies for achieving desired firing patterns in a physiologically realistic model neuron. The techniques are powerful, efficient, and robust, and we have applied them successfully to obtain a range of targeted spiking behaviors.
S, Sinha, W L, Ditto
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We propose two control strategies for achieving desired firing patterns in a physiologically realistic model neuron. The techniques are powerful, efficient, and robust, and we have applied them successfully to obtain a range of targeted spiking behaviors.
S, Sinha, W L, Ditto
openaire +2 more sources
Decoding spikes in a spiking neuronal network
Journal of Physics A: Mathematical and General, 2004Summary: We investigate how to reliably decode the input information from the output of a spiking neuronal network. A maximum likelihood estimator of the input signal, together with its Fisher information, is rigorously calculated. The advantage of the maximum likelihood estimation over the 'brute-force rate coding' estimate is clearly demonstrated. It
Feng, Jianfeng, Ding, Mingzhou
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Spiking neuron models for regular-spiking, intrinsically bursting, and fast-spiking neurons
ICONIP'99. ANZIIS'99 & ANNES'99 & ACNN'99. 6th International Conference on Neural Information Processing. Proceedings (Cat. No.99EX378), 2003Simplified models are proposed as variants of the integrate-and-fire-model for an intrinsically bursting (IB) neuron and for a fast-firing (FS) neuron taking refractory periods into consideration. A model of a regular-spiking neuron is also described in the conventional manner for the sake of comparison.
S. Inawashiro, S. Miyake, M. Ito
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2009 IEEE International Symposium on Information Theory, 2009
The information transfer through a single neuron is a fundamental information processing in the brain. This paper studies the information-theoretic capacity of a single neuron by treating the neuron as a communication channel. Two different models are considered.
Shiro Ikeda, Jonathan H. Manton
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The information transfer through a single neuron is a fundamental information processing in the brain. This paper studies the information-theoretic capacity of a single neuron by treating the neuron as a communication channel. Two different models are considered.
Shiro Ikeda, Jonathan H. Manton
openaire +1 more source
SpikeCell: a deterministic spiking neuron
Neural Networks, 2002We present a model of spiking neuron that emulates the output of the usual static neurons with sigmoidal activation functions. It allows for hardware implementations of standard feedforward networks, trained off-line with any classical learning algorithm (i.e. back-propagation and its variants). The model is validated on hand-written digits recognition,
C, Godin, M B, Gordon, J D, Muller
openaire +2 more sources