Simple model of spiking neurons

By Eugene M. Izhikevich. 2003

4,649 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering, Statistical and Nonlinear Physics, Neuroscience

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Citations in the corpus

Dynamic Neural Networks: A Survey (2021, background): “Spiking neural networks (SNNs) [42], [43] also perform data-dependent inference by propagating pulse signals.”
Multi-Level Cause-Effect Systems (2015, methodology): “1), we observe a population of 100 neurons which act spontaneously using dynamics defined by Izhikevich’s equations (Izhikevich, 2003).”
An efficient automated parameter tuning framework for spiking neural networks (2014, methodology): “For example, many modelers have moved away from simple “integrate and ﬁre” neuron models to models which capture a wider range of neuronal dynamics, but have more open parameters (Izhikevich, 2003; Brette and Gerstner, 2005).”
Decoupling through synchrony in neuronal circuits with propagation delays. (2008, positive): “To illustrate how the opposing forces of Hebbian STDP interact within a circuit, we consider a randomly driven recurrent network of excitatory regular spiking (RS) Izhikevich neurons with axonal delays (Figure 4) (Izhikevich, 2003, 2006).”
Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting (2010, methodology): “In the experiment, we compare results of learning for the LIF, Hodgkin-Huxley (HH), and Izhikevich (IM) neuron models (Gerstner & Kistler, 2002; Hodgkin & Huxley, 1952; Izhikevich, 2003).”
Compact silicon neuron circuit with spiking and bursting behaviour (2008, methodology): “Many parameters, such as spike frequency, interspike-interval histogram, spike width, intra-burst frequency, adaptation index etc. can be used to classify the neurons.”
Minitaur, an Event-Driven FPGA-Based Spiking Network Accelerator (2014, background): “Event-driven, sparser-computation hardware implementations, such as in [17]– [19] are rare, typically focusing on using biologically descriptive neuron models, such as the Izhikevich model [20] and biological network topologies.”
STDP rule endowed with the BCM sliding threshold accounts for hippocampal heterosynaptic plasticity (2007, methodology): “For the neuron model we employed the simple model of a spiking neuron introduced by Izhikevich (2003).”
Firing patterns in the adaptive exponential integrate-and-fire model (2008, background): “An additional recovery variable is important to capture adaptation and resonance properties (Richardson et al. 2003; Izhikevich 2003).”
Conditional modulation of spike-timing-dependent plasticity for olfactory learning (2012, neutral): “Simulations of networks of ‘Izhikevich units&rsquo”
Deep Learning in Spiking Neural Networks (2018, methodology): “After the Hodgkin and Huxley model with extensive biological details and high computational cost [16], [64], [69], diverse neuron models have been proposed such as the spike response model (SRM) [70], the Izhikevich neuron model [71], and the leaky integrated-and-fire (LIF) neuron [72].”
Learning in spiking neural networks (2013, methodology): “a, b, c and d are the four parameters of the Izhikevich neuron model (Izhikevich, 2003): • The parameter a describes the time-scale of the recovery variable u.”
Dynamical principles for neuroscience and intelligent biomimetic devices (2006, neutral): “The sWTA circuits exhibit population coding, gain modulation, focal attention (signal selection), and spatiotemporal pattern generation, all of which are characteristics of neocortical computation [3, 1, 11, 10], and they can be fabricated in custom Very Large Scale Integrated electronic circuits co”
Simulation of networks of spiking neurons: A review of tools and strategies (2006, neutral): “While users have added, for example, the Izhikevich (2003) simpliﬁed spiking neuron model (now built in to GENESIS), and they could also add IF or other forms of abstract neuron models, these forms of neurons are not realistic enough for the interests of most GENESIS modelers.”
Which model to use for cortical spiking neurons? (2004, methodology): “1 were obtained using a simple model of spiking neurons proposed recently by Izhikevich [15] with the auxiliary after-spike resetting Here variable v represents the membrane potential of the neuron and u represents a membrane recovery variable, which accounts for the activation of K + ionic currents”
A review of learning in biologically plausible spiking neural networks (2019, methodology): “The biological plausibility and the implementation cost of various spiking neuron models are compared in (Izhikevich, 2004).”