0.5V 4.8 pJ/SOP 0.93\mu \mathrm{W}$ Leakage/core Neuromorphic Processor with Asynchronous NoC and Reconfigurable LIF Neuron

Author(s):  
V. P. Nambiar ◽  
J. Pu ◽  
Y. K. Lee ◽  
A. Mani ◽  
T. Luo ◽  
...  
Keyword(s):  
2018 ◽  
Vol 65 (11) ◽  
pp. 5137-5144 ◽  
Author(s):  
Sangya Dutta ◽  
Tinish Bhattacharya ◽  
Nihar R. Mohapatra ◽  
Manan Suri ◽  
Udayan Ganguly

2003 ◽  
Vol 15 (2) ◽  
pp. 253-278 ◽  
Author(s):  
Maurice J. Chacron ◽  
Khashayar Pakdaman ◽  
André Longtin

Neuronal adaptation as well as interdischarge interval correlations have been shown to be functionally important properties of physiological neurons. We explore the dynamics of a modified leaky integrate-and-fire (LIF) neuron, referred to as the LIF with threshold fatigue, and show that it reproduces these properties. In this model, the postdischarge threshold reset depends on the preceding sequence of discharge times. We show that in response to various classes of stimuli, namely, constant currents, step currents, white gaussian noise, and sinusoidal currents, the model exhibits new behavior compared with the standard LIF neuron. More precisely, (1) step currents lead to adaptation, that is, a progressive decrease of the discharge rate following the stimulus onset, while in the standard LIF, no such patterns are possible; (2) a saturation in the firing rate occurs in certain regimes, a behavior not seen in the LIF neuron; (3) interspike intervals of the noise-driven modified LIF under constant current are correlated in a way reminiscent of experimental observations, while those of the standard LIF are independent of one another; (4) the magnitude of the correlation coefficients decreases as a function of noise intensity; and (5) the dynamics of the sinusoidally forced modified LIF are described by iterates of an annulus map, an extension to the circle map dynamics displayed by the LIF model. Under certain conditions, this map can give rise to sensitivity to initial conditions and thus chaotic behavior.


2008 ◽  
Vol 20 (6) ◽  
pp. 1411-1426 ◽  
Author(s):  
Kukjin Kang ◽  
Shun-ichi Amari

We study the discrimination capability of spike time sequences using the Chernoff distance as a metric. We assume that spike sequences are generated by renewal processes and study how the Chernoff distance depends on the shape of interspike interval (ISI) distribution. First, we consider a lower bound to the Chernoff distance because it has a simple closed form. Then we consider specific models of ISI distributions such as the gamma, inverse gaussian (IG), exponential with refractory period (ER), and that of the leaky integrate-and-fire (LIF) neuron. We found that the discrimination capability of spike times strongly depends on high-order moments of ISI and that it is higher when the spike time sequence has a larger skewness and a smaller kurtosis. High variability in terms of coefficient of variation (CV) does not necessarily mean that the spike times have less discrimination capability. Spike sequences generated by the gamma distribution have the minimum discrimination capability for a given mean and variance of ISI. We used series expansions to calculate the mean and variance of ISIs for LIF neurons as a function of the mean input level and the input noise variance. Spike sequences from an LIF neuron are more capable of discrimination than those of IG and gamma distributions when the stationary voltage level is close to the neuron's threshold value of the neuron.


Author(s):  
Syed Ahmed Aamir ◽  
Paul Muller ◽  
Andreas Hartel ◽  
Johannes Schemmel ◽  
Karlheinz Meier

2009 ◽  
Vol 21 (11) ◽  
pp. 3079-3105 ◽  
Author(s):  
Xuejuan Zhang ◽  
Gongqiang You ◽  
Tianping Chen ◽  
Jianfeng Feng

An expression for the probability distribution of the interspike interval of a leaky integrate-and-fire (LIF) model neuron is rigorously derived, based on recent theoretical developments in the theory of stochastic processes. This enables us to find for the first time a way of developing maximum likelihood estimates (MLE) of the input information (e.g., afferent rate and variance) for an LIF neuron from a set of recorded spike trains. Dynamic inputs to pools of LIF neurons both with and without interactions are efficiently and reliably decoded by applying the MLE, even within time windows as short as 25 msec.


2020 ◽  
Vol 14 (2) ◽  
pp. 148-160
Author(s):  
Saket K. Choudhary ◽  
Vijender K. Solanki

Background: Distributed Delay Framework (DDF) has suggested a mechanism to incorporate the delay factor in the evolution of the membrane potential of a neuron model in terms of distributed delay kernel functions. Incorporation of delay in neural networks provide comparatively more efficient output. Depending on the parameter of investigation, there exist a number of choices of delay kernel function for a neuron model. Objective: We investigate the Leaky integrate-and-fire (LIF) neuron model in DDF with hypoexponential delay kernel. LIF neuron with hypo-exponential distributed delay (LIFH) model is capable to regenerate almost all possible empirically observed spiking patterns. Methods: In this article, we perform the detailed analytical and simulation based study of the LIFH model. We compute the explicit expressions for the membrane potential and its first two moment viz. mean and variance, in analytical study. Temporal information processing functionality of the LIFH model is investigated during simulation based study. Results: We find that the LIFH model is capable to reproduce unimodal, bimodal and multimodal inter-spike- interval distributions which are qualitatively similar with the experimentally observed ISI distributions. Conclusion: We also notice the neurotransmitter imbalance situation, where a noisy neuron exhibits long tail behavior in aforementioned ISI distributions which can be characterized by power law behavior.


2018 ◽  
Vol 91 (12) ◽  
Author(s):  
Nefeli-Dimitra Tsigkri-DeSmedt ◽  
Ioannis Koulierakis ◽  
Georgios Karakos ◽  
Astero Provata
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