The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Maximum likelihood estimation of cascade point-process neural encoding models
Abstract
Recent work has examined the estimation of models of stimulus-driven neural activity in which some linear filtering process is followed by a nonlinear, probabilistic spiking stage. We analyze the estimation of one such model for which this nonlinear step is implemented by a known parametric function; the assumption that this function is known speeds the estimation process considerably. We investigate the shape of the likelihood function for this type of model, give a simple condition on the nonlinearity ensuring that no non-global local maxima exist in the likelihood-leading, in turn, to efficient algorithms for the computation of the maximum likelihood estimator-and discuss the implications for the form of the allowed nonlinearities. Finally, we note some interesting connections between the likelihood-based estimators and the classical spike-triggered average estimator, discuss some useful extensions of the basic model structure, and provide two novel applications to physiological data.
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Cited by
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Self-citationProbabilistic population codes for Bayesian decision making
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Inferring single-trial neural population dynamics using sequential auto-encoders
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How MT cells analyze the motion of visual patterns
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Functional connectivity in the retina at the resolution of photoreceptors
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Metadata
- Authors
- Paninski, L.
- Venue
- Network: Computation in Neural Systems
- Publisher
- Informa UK Limited
- Published
- 2004-11-01
- Volume
- 15
- Issue
- 4
- Pages
- 243-262
- ISSN
- 0954-898X (ISSN-L)
- 1361-6536
- OpenAlex
- W2112246597
- Cited by
- 481
- References
- 54
- FWCI
- 4.48
- Type
- journal article
- Language
- en
- Open access
- closed
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