Estimating a State-Space Model from Point Process Observations

By Anne C. Smith, Emery N. Brown. Neural Computation, 2003

420 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Endocrine and Autonomic Systems

View Paper: https://www.semanticscholar.org/paper/9c01ebdf379be03f76b82a0ae96d9dea56cd4fd9

How researchers see this paper

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

In Cognitive Neuroscience

1. John P. Cunningham
2. Krishna V. Shenoy
3. Loren M. Frank
4. Emery N. Brown
5. Byron M. Yu
6. Uri T. Eden
7. Saurabh Saxena
8. Shigeru Shinomoto

In Endocrine and Autonomic Systems

1. John P. Cunningham
2. Loren M. Frank
3. Shigeru Shinomoto
4. Hideaki Shimazaki
5. Riccardo Barbieri
6. Emery N. Brown
7. Ann M. Graybiel

Citations in the corpus

• Towards the neural population doctrine. (2019, background): “Classic versions of this have included linear dynamical systems [61,62].”
• Dimensionality reduction for large-scale neural recordings (2014, background): “It can be used in settings in which the explanatory variables are observed 64,66 or unobserved (that is, latent) 71,72,74–77 .”
• Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis (2017, background): “In particular, single-trial neural activity has beenmodeled with linear dynamics (Smith and Brown, 2003; Macke et al., 2011; Buesing et al., 2012; Kao et al., 2015), switched linear dynamics (Petreska et al.”
• Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering (2004, methodology): “Additionally, if the form of the model can be specie ed but the model parameters are unknown, then the parameters can be estimated using an expectation-maximization algorithm ( Smith & Brown, 2003; Roweis & Ghahramani, 2000).”
• Kernel bandwidth optimization in spike rate estimation (2009, result): “We refer to Cunningham et al. (2008) for a result on the superior fitting performance of a fixed kernel to small samples in comparison with the Locfit and BARS, as well as the Gaussian process smoother (Cunningham et al. 2008; Smith and Brown 2003; Koyama and Shinomoto 2005).”
• Statistical models for neural encoding, decoding, and optimal stimulus design. (2007, neutral): “via the expectation maximization algorithm [29, 22, 11].”
• Functional, but Not Anatomical, Separation of "what" and "when" in Prefrontal Cortex Gaussian-process Factor Analysis for Low-dimensional Single-trial Analysis of Neural Population Activity (positive): “A possible solution is to replace the Gaussian observation noise model of PPCA and FA with a point-process (Smith and Brown 2003; Truccolo et al. 2005; Yu et al. 2006) likelihood model.”
• Dynamic Analysis of Learning in Behavioral Experiments (2004, methodology): “The derivation of this algorithm for arbitrary point process observation model and linear state–space model is given by Smith and Brown (2003).”