Population Dynamics of Spiking Neurons: Fast Transients, Asynchronous States, and Locking

By Wulfram Gerstner. Neural Computation, 2000

425 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/32305c38382f2d6511919fd6034397e5b3430fcf

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Citations in the corpus

On the Phase Reduction and Response Dynamics of Neural Oscillator Populations (2004, background): “…involving distributed conductances, are developed and applied in, among others, Stein (1965), Wilson and Cowan (1972), Fetz and Gustaffson (1983), Gerstner (2000), Nykamp and Tranchina (2000), Omurtag, Knight, and Sirovich (2000), Herrmann and Gerstner (2001), Casti et al. (2001), Brunel,…”
The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields (2008, background): “This is determined by the proportion of active neurons by counting the number of spikes nspikes(t,t+dt) in a small time interval dt and dividing by N and by dt [18]; i.”
Dynamics of the Firing Probability of Noisy Integrate-and-Fire Neurons (2002, methodology): “The noise model we have studied provides an interpolation between two previously studied noise models: at large ts, the rapid changes in response to sharp input transient are similar to what was found in slow noise models (Knight, 1972; Gerstner, 2000), and, at small ts, the response is fast only at”
A neurodynamical cortical model of visual attention and invariant object recognition. (2004, methodology): “charge rate) for a spiking neuron with deterministic input, membrane time constant s, and absolute refrac- tory time Tr. Eq. (A.1) was derived by Gerstner (2000)”
Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning (2001, background): “If the interspike interval distribution is broad, the second term on the right is small and may be neglected (Gerstner, 2000).”
Decorrelation of Neural-Network Activity by Inhibitory Feedback (2012, background): “This seems to be at odds with the finding that neighboring neurons receive a considerable fraction of inputs from identical presynaptic sources constituting an inevitable source of correlation.”
Spontaneous Dynamics of Asymmetric Random Recurrent Spiking Neural Networks (2004, methodology): “This technique can be traced back to Gerstner and van Hemmen (1992). In that case, it is used within a symmetric weight matrix and a discrete formalism. It leads to similar recursive equations. The independence of the charge hypothesis leads us to consider the local field of a particular neuron as a”
Balancing Feed-Forward Excitation and Inhibition via Hebbian Inhibitory Synaptic Plasticity (2012, background): “Similarly, in this section, we assume that the synaptic coupling is sufficiently weak such that we can approximate the postsynaptic firing rate by a linear function of the presynaptic input (see also [43,60,61]), yielding”
A model of interval timing by neural integration (2011, background): “A transient burst of firing, like those seen in the rat data in Figure 2, c and d, at the cue onset, naturally occurs in more biophysically detailed spiking models of neural populations when a quiescent population receives a step voltage input (e.g., Gerstner, 1999).”
A review of the integrate-and-fire neuron model: II. Inhomogeneous synaptic input and network properties (2006, background): “In the low-noise regime, it was found that transitions between the asynchronous and the synchronized state occur almost immediately, which indicates that a population of neurons can transmit information fast and reliably (Gerstner 2000).”