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Paninski L (2006) The spike-triggered average of the integrate-and-fire cell driven by gaussian white noise. Neural Comput 18:2592-616[PubMed]

References and models cited by this paper

References and models that cite this paper

Aguera y Arcas B, Fairhall AL (2003) What causes a neuron to spike? Neural Comput 15:1789-807 [PubMed]

Badel L, Richardson M, Gerstner W (2006) Dependence of the spike-triggered average voltage on membrane response properties Neurocomputing 69:1062-1065

Berry MJ, Meister M (1998) Refractoriness and neural precision. J Neurosci 18:2200-11 [PubMed]

Brunel N, Hakim V (1999) Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. Neural Comput 11:1621-71 [Journal] [PubMed]

   Fast global oscillations in networks of I&F neurons with low firing rates (Brunel and Hakim 1999) [Model]

Brunel N, Latham PE (2003) Firing rate of the noisy quadratic integrate-and-fire neuron. Neural Comput 15:2281-306 [PubMed]

Bryant HL, Segundo JP (1976) Spike initiation by transmembrane current: a white-noise analysis. J Physiol 260:279-314 [PubMed]

Burkitt AN, Clark GM (1999) Analysis of integrate-and-fire neurons: synchronization of synaptic input and spike output. Neural Comput 11:871-901 [PubMed]

Bussgang JJ (1952) Crosscorrelation functions of amplitude distorted gaussian signals Mit Res Lab Elec Tech Rep 216:1-14

Chichilnisky EJ (2001) A simple white noise analysis of neuronal light responses. Network 12:199-213 [PubMed]

Daniels H (1982) Sequential tests constructed from images Ann Stat 10:394-400

Dayan P, Abbott LF (2001) Theoretical Neuroscience. Computational and Mathematical Modeling of Neural Systems

de Boer R, Kuyper P (1968) Triggered correlation. IEEE Trans Biomed Eng 15:169-79

de Ruyter van Steveninck RR, Bialek W (1988) Real-time performance of a movement sensitive in the blowfly visual system: Information transfer in short spike sequences Proc Roy Soc Lond 234:379-414

Freidlin M, Wentzell A (1998) Random Perturbations of Dynamical Systems

Gerstner W (2001) Coding properties of spiking neurons: Reverse and cross correlations Neural Netw 14:599-610

Gerstner W, Kistler WM (2002) Spiking neuron models

Harvey A (1991) Forecasting: Structural time series models and the Kalman filter

Haskell E, Nykamp DQ, Tranchina D (2001) Population density methods for large-scale modelling of neuronal networks with realistic synaptic kinetics: cutting the dimension down to size. Network 12:141-74

Hida T (1980) Brownian Motion

Kanev J, Wenning G, Obermayer K (2004) Approximating the response stimulus correlation for the integrate-and-fire neuron Neurocomputing 58:47-52

Karatzas I, Shreve S (1997) Brownian Motion and Stochastic Calculus

Karlin S, Taylor HM (1982) Asecond course in stochasticprocesses.

Kautz R (1988) Thermally induced escape: The principle of minimum available noise energy Physical Rev A 38:2066-2080

Koch C (1999) Biophysics Of Computation: Information Processing in Single Neurons

Paninski L (2003) Convergence properties of three spike-triggered analysis techniques. Network 14:437-64

Paninski L (2004) Maximum likelihood estimation of cascade point-process neural encoding models. Network 15:243-62 [PubMed]

Paninski L (2006) The most likely voltage path and large deviations approximations for integrate-and-fire neurons J Comput Neurosci 21:71-87 [Journal]

Paninski L, Haith A, Pillow J, Williams C (2005) Improved numerical methods for computing likelihoods in the stochastic integrate-and-fire model Comp Sys Neur

Paninski L, Lau B, Reyes A (2003) Noise-driven adaptation: in vitro and mathematical analysis Neurocomputing 52:877-883

Paninski L, Pillow JW, Simoncelli EP (2004) Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model. Neural Comput 16:2533-61 [PubMed]

Pillow J, Simoncelli E (2003) Biases in white noise analysis due to non-Poisson spike generation Neurocomputing 52:109-155

Pillow JW, Paninski L, Uzzell VJ, Simoncelli EP, Chichilnisky EJ (2005) Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. J Neurosci 25:11003-13 [PubMed]

Plesser HE, Tanaka S (1997) Stochastic resonance in a model neuron with reset. Phys Lett A 225:228-234

Press WH, Teukolsky SA, Vellerling WT, Flannery BP (1992) Numerical Recipes In C: The Art Of Scientific Computing

Rabiner L (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77:257-286

Rieke F, Warland D, de Ruyter van Steveninck, R, Bialek B (1997) Spikes: Exploring The Neural Code

Risken H (1996) The Fokker Planck Equation: Methods of Solution and Applications

Rudd ME, Brown LG (1997) Noise adaptation in integrate-and fire neurons. Neural Comput 9:1047-69 [PubMed]

Seshadri V (1993) The inverse gaussian distribution

Simoncelli EP, Paninski L, Pillow J, Schwartz O (2004) Characterization of neural responses with stochastic stimuli The New Cognitive Neurosciences (3rd ed), Gazzaniga M, ed.

Tuckwell HC (1988) Introduction to Theoretical Neurobiology. Volume 2: Nonlinear and Stochastic Theories

Yu Y, Lee TS (2003) Dynamical mechanisms underlying contrast gain control in single neurons. Phys Rev E Stat Nonlin Soft Matter Phys 68:011901-89

Koendgen H, Geisler C, Fusi S, Wang XJ, Luscher HR, Giugliano M (2008) The dynamical response properties of neocortical neurons to temporally modulated noisy inputs in vitro. Cereb Cortex 18:2086-97 [Journal] [PubMed]

   Response properties of neocort. neurons to temporally modulated noisy inputs (Koendgen et al. 2008) [Model]

Paninski L (2006) The most likely voltage path and large deviations approximations for integrate-and-fire neurons J Comput Neurosci 21:71-87 [Journal]

Pospischil M, Piwkowska Z, Rudolph M, Bal T, Destexhe A (2007) Calculating event-triggered average synaptic conductances from the membrane potential. J Neurophysiol 97:2544-52 [PubMed]

   Code to calc. spike-trig. ave (STA) conduct. from Vm (Pospischil et al. 2007, Rudolph et al. 2007) [Model]

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