Statistical models for neural encoding, decoding, and optimal stimulus design.

By Liam Paninski, Jonathan W. Pillow, Jeremy Lewi. Progress in Brain Research, 2007

298 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/b51884cc52c07c178e607951f202947ff9e77430

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Citations in the corpus

Spatio-temporal correlations and visual signalling in a complete neuronal population (2008, methodology): “using cubic splines • a stochastic, leaky integrate-and-fire model with post-spike current ([7], and compared in [8]) • a point process model with multiple stimulus filters (excitatory and suppressive), giving each cell sensitivity to multiple dimensions of the stimulus space (cf.”
How advances in neural recording affect data analysis (2011, background): “In most cases, encoding and decoding models are tightly linked; leading decoding models are usually based on explicit models of encodin”
The Science of Neural Interface Systems (2009, unknown): “Similar approaches for decoding sensory stimuli as well as motor behaviors have been used by a number of groups (Barbieri et al. 2004a,b; Paninski et al. 2007; Sanger 2003; Wiener & Richmond 2002).”
Bridging the brain to the world: a perspective on neural interface systems. (2008, unknown): “Virtually every readily identifiable method of decoding information has been attempted in both FP and spike signals (for further discussion see Paninski et al., 2007; Schwartz et al., 2006; Serruya et al., 2003; Srinivasan et al., 2007; Truccolo et al., 2005; Wu et al., 2006).”
Inferring Network Dynamics and Neuron Properties from Population Recordings (2011, methodology): “This is a novelty with respect to other modeling approaches that rely on input linear filtering and non-linear input–output relationships with feedback like generalized linear models (Paninski et al., 2007) and linear–non-linear cascade models (Sakai, 1992; Marmarelis, 1993).”