Computing loss of efficiency in optimal Bayesian decoders given noisy or incomplete spike trains

No abstract is available for this paper.

• Publication year

  2013

• Venue

  Network

• Publication date

  2013-06-01

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.3109/0954898X.2013.789568PMID 23742213
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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  2012influential reference
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  2008cited by this paper
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  2008influential reference
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  2008cited by this paper
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  2005influential reference
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• Statistical issues in the analysis of neuronal data.

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• Monte Carlo Statistical Methods (Springer Texts in Statistics)

  2005influential reference
• Monte Carlo Statistical Methods

  2004influential reference
• Attractor dynamics of network UP states in the neocortex

  2003cited by this paper
• Latent variable models for neural data analysis

  1999cited by this paper
• A review of methods for spike sorting: the detection and classification of neural action potentials.

  1998cited by this paper
• Topics in Matrix Analysis

  1991cited by this paper
• A tutorial on hidden Markov models and selected applications in speech recognition

  1989influential reference

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  2023cites this paper
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