Dynamics of the Firing Probability of Noisy Integrate-and-Fire Neurons

By Nicolas Fourcaud-Trocmé, Nicolas J.-B. Brunel. Neural Computation, 2002

335 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/f8b6f8d88a62a59c01f844981672d63c12c1ce7f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Statistical and Nonlinear Physics

Citations in the corpus

Decorrelation of Neural-Network Activity by Inhibitory Feedback (2012, background): “For many real and model neurons, the linear population-rate response exhibits low-pass characteristics [35, 36, 8, 7, 34, 22, 10, 40, 19, 20, 48, 55, 50, 58].”
A Recurrent Network Mechanism of Time Integration in Perceptual Decisions (2006, methodology): “These calculations are computationally extensive, taking into account realistic synaptic dynamics (Wang, 1999; Brunel and Wang, 2001) and higher order corrections (Fourcaud and Brunel, 2002).”
Modelling event-related responses in the brain (2005, methodology): “Being able to formulate the model in terms of differential equations is important for extending the neural mass formulation used here to a true mean field approximation using the Fokker–Plank formalism (De Groff et al., 1993; Fourcaud and Brunel, 2002).”
Neuronal Reward and Decision Signals: From Theories to Data. (2015, background): “The key to explaining such brain activity may be the tendency to stochastic activity in many neurons and networks (129, 167, 180, 195).”
Computing and Stability in Cortical Networks (2004, background): “I curves—ring rate versus injected current (McCormick, Connors, Lighthall, & Prince, 1985)—that have been smoothed at low ring rates (Brunel & Sergi, 1998; Tiesinga, José, & Sejnowski, 2000; Fourcaud & Brunel, 2002; Brunel & Latham, 2003).”
A review of the integrate-and-fire neuron model: II. Inhomogeneous synaptic input and network properties (2006, methodology): “Brunel and colleagues have developed a perturbative methodbasedupontheFokker–Planckequation(Brunel and Hakim 1999; Brunel et al. 2001, 2003; Fourcaud and Brunel 2002).”
Inferring Network Dynamics and Neuron Properties from Population Recordings (2011, background): “Nevertheless, recent advances in the study of the input–output linear response of the IF neurons have proved that realistic synaptic transmission brings neurons to have fast responses to input changes (Brunel et al., 2001; Fourcaud and Brunel, 2002).”
What determines the frequency of fast network oscillations with irregular neural discharges? I. Synaptic dynamics and excitation-inhibition balance. (2003, methodology): “This subject has been analytically investigated in (Brunel et al. 2001; Fourcaud and Brunel 2002) for the LIF neuron model.”
On the Phase Reduction and Response Dynamics of Neural Oscillator Populations (2004, background): “…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, Chance, Fourcaud, and Abbott (2001), Fourcaud and Brunel (2002), and Gerstner and Kistler (2002).”
A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input (2006, methodology): “This method has also been used for studying the leaky integrate-and-fire neuron model with current synapses and oscillatory input current (Fourcaud and Brunel 2002).”