A simple white noise analysis of neuronal light responses

By Systems Neurobiology. Network, 2001

985 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/468b007840cdac23ee0e3f19359544d33d312fbf

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Citations in the corpus

Spike-triggered neural characterization. (2006, methodology): “For an LNP model with a single linear filter, the STA provides an unbiased estimate of this filter, provided that the input stimuli are spherically symmetric (Bussgang, 1952; Chichilnisky, 2001; Paninski, 2003), and the nonlinearity of the model is such that it leads to a shift in the mean of the sp”
Prediction and Decoding of Retinal Ganglion Cell Responses with a Probabilistic Spiking Model (2005, methodology): “This model is widely used because of its computational simplicity and the ease with which its parameters can be estimated (Chichilnisky , 2001).”
Spatio-temporal correlations and visual signalling in a complete neuronal population (2008, background): “Previous studies have examined visual coding by pairs of neurons [7], and the statistics of simultaneous firing patterns in larger neural populations [10, 11].”
The Statistical Computation Underlying Contrast Gain Control (2006, positive): “As expected from studies in retina (Chander and Chichilnisky, 2001; Zaghloul et al., 2005) and in LGN (Bonin et al., 2005), these changes in gain were more pronounced in ON cells than in OFF cells.”
Segregation of object and background motion in the retina (2003, methodology): “The waveform of the temporal filter was measured by reverse correlation of ganglion cell spikes to a flicker stimulu”
Maximum likelihood estimation of cascade point-process neural encoding models (2004, methodology): “Results are shown in ﬁgure 3; the main conclusion is that the IF–GLM permits quite accurate prediction of the observed spike-train; moreover, the predictions of the IF–GLM are much more precise than are those of the simple LNP model, in large part because the refractory effects of the post-spike cur”
Contrast Gain Control in Auditory Cortex (2011, neutral): “, 2001) and linear-nonlinear (LN) (Chichilnisky, 2001; Simoncelli et al., 2004; Dahmen et al., 2010) models.”
Analyzing Neural Responses to Natural Signals: Maximally Informative Dimensions (2002, methodology): “In this simple model, the reverse correlation method (de Boer & Kuyper, 1968; Rieke et al., 1997; Chichilnisky, 2001) can be used to recover the vector ê1 by analyzing the neuron’s responses to Gaussian white noise.”
Do We Know What the Early Visual System Does? (2005, methodology): “However, the linear model fails for ganglion cell responses because of several nonlinearities (Shapley and Victor, 1978; Victor, 1987; Chichilnisky, 2001; Kim and Rieke, 2001; Baccus and Meister, 2002; Zaghloul et al., 2003, 2005).”
Revealing neuronal function through microelectrode array recordings (2015, positive): “This allowed the identiﬁcation of cell types of populations of RGCs and the mapping of their receptive ﬁelds (Meister et al., 1994; Chichilnisky, 2001), in different regions of the retina.”
Fast and slow contrast adaptation in retinal circuitry. (2002, neutral): “nonlinear " ) model (Berry and Meister, 1998; Chichilnisky, 2001; Hunter and Korenberg, 1986; Kim and Rieke, 2001; although the”