Paninski L. (2003). Convergence properties of three spike-triggered analysis techniques. Network (Bristol, England). 14 [PubMed]

See more from authors: Paninski L

References and models cited by this paper
References and models that cite this paper

Huys QJ, Zemel RS, Natarajan R, Dayan P. (2007). Fast population coding. Neural computation. 19 [PubMed]

McFarland JM, Cui Y, Butts DA. (2013). Inferring nonlinear neuronal computation based on physiologically plausible inputs. PLoS computational biology. 9 [PubMed]

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

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

Sharpee T, Rust NC, Bialek W. (2004). Analyzing neural responses to natural signals: maximally informative dimensions. Neural computation. 16 [PubMed]

Shlens J, Kennel MB, Abarbanel HD, Chichilnisky EJ. (2007). Estimating information rates with confidence intervals in neural spike trains. Neural computation. 19 [PubMed]

This website requires cookies and limited processing of your personal data in order to function. By continuing to browse or otherwise use this site, you are agreeing to this use. See our Privacy policy and how to cite and terms of use.