
Spiking Analog VLSI Neuron Assemblies as Constraint Satisfaction Problem Solvers
Solving constraint satisfaction problems (CSPs) is a notoriously expensi...
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Autonomous learning of nonlocal stochastic neuron dynamics
Neuronal dynamics is driven by externally imposed or internally generate...
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Proposal for a LeakyIntegrateFire Spiking Neuron based on MagnetoElectric Switching of Ferromagnets
The efficiency of the human brain in performing classification tasks has...
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Counting to Ten with Two Fingers: Compressed Counting with Spiking Neurons
We consider the task of measuring time with probabilistic threshold gate...
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A Numerical Study of the Time of Extinction in a Class of Systems of Spiking Neurons
In this paper we present a numerical study of a mathematical model of sp...
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Alloptical neuromorphic binary convolution with a spiking VCSEL neuron for image gradient magnitudes
Alloptical binary convolution with a photonic spiking verticalcavity s...
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Estimating the interaction graph of stochastic neuronal dynamics by observing only pairs of neurons
We address the questions of identifying pairs of interacting neurons fro...
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Stochastic IMT (insulatormetaltransition) neurons: An interplay of thermal and threshold noise at bifurcation
A stochastic neuron, a key hardware kernel for implementing stochastic neural networks, is constructed using an insulatormetaltransition (IMT) device based on electrically induced phasetransition in series with a tunable resistance. We show that such an IMT neuron has dynamics similar to a piecewise linear FitzHughNagumo (FHN) neuron. Spiking statistics of such neurons are demonstrated experimentally using Vanadium Dioxide (VO_2) based IMT neurons, and modeled as an OrnsteinUhlenbeck (OU) process with a fluctuating boundary. The stochastic spiking is explained by thermal noise and threshold fluctuations acting as precursors of bifurcation which result in a sigmoidlike transfer function. Moments of interspike intervals are calculated analytically by extending the firstpassagetime (FPT) models for OrnsteinUhlenbeck (OU) process to include a fluctuating boundary. We find that the coefficient of variation of interspike intervals depend on the relative proportion of thermal and threshold noise. In the current experimental demonstrations where both kinds of noise are present, the coefficient of variation is about an order of magnitude higher compared to the case where only thermal noise were present.
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