Academic Journal
Bayesian entropy estimators for spike trains
| العنوان: | Bayesian entropy estimators for spike trains |
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| المؤلفون: | Park, Il Memming, Archer, Evan, Pilow, Jonathan |
| بيانات النشر: | BMC Neuroscience |
| سنة النشر: | 2013 |
| المجموعة: | The University of Texas at Austin: Texas ScholarWorks |
| مصطلحات موضوعية: | Bayesian entropy, spike trains, nueral codes |
| الوصف: | Il Memming Park and Jonathan Pillow are with the Institute for Neuroscience and Department of Psychology, The University of Texas at Austin, TX 78712, USA -- Evan Archer is with the Institute for Computational and Engineering Sciences, The University of Texas at Austin, TX 78712, USA -- Jonathan Pillow is with the Division of Statistics and Scientific Computation, The University of Texas at Austin, Austin, TX 78712, USA ; Poster presentation: Information theoretic quantities have played a central role in neuroscience for quantifying neural codes [1]. Entropy and mutual information can be used to measure the maximum encoding capacity of a neuron, quantify the amount of noise, spatial and temporal functional dependence, learning process, and provide a fundamental limit for neural coding. Unfortunately, estimating entropy or mutual information is notoriously difficult--especially when the number of observations N is less than the number of possible symbols K [2]. For the neural spike trains, this is often the case due to the combinatorial nature of the symbols: for n simultaneously recorded neurons on m time bins, the number of possible symbols is K = 2n+m. Therefore, the question is how to extrapolate when you may have a severely under-sampled distribution. Here we describe a couple of recent advances in Bayesian entropy estimation for spike trains. Our approach follows that of Nemenman et al. [2], who formulated a Bayesian entropy estimator using a mixture-of-Dirichlet prior over the space of discrete distributions on K bins. We extend this approach to formulate two Bayesian estimators with different strategies to deal with severe under-sampling. For the first estimator, we design a novel mixture prior over countable distributions using the Pitman-Yor (PY) process [3]. The PY process is useful when the number of parameters is unknown a priori, and as a result finds many applications in Bayesian nonparametrics. PY process can model the heavy, power-law distributed tails which often occur in neural data. To reduce ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| ردمك: | 978-1-4712-2021-0 1-4712-2021-4 |
| Relation: | Park, Il M., Evan Archer, and Jonathan Pillow. “Bayesian Entropy Estimators for Spike Trains.” BMC Neuroscience 14, no. Suppl 1 (July 8, 2013): P316. doi:10.1186/1471-2202-14-S1-P316.; http://hdl.handle.net/2152/27957; 1471-2202-14-S1-P316.pdf |
| DOI: | 10.1186/1471-2202-14-S1-P316 |
| الاتاحة: | http://hdl.handle.net/2152/27957 https://doi.org/10.1186/1471-2202-14-S1-P316 |
| Rights: | Administrative deposit of works to UT Digital Repository: This works author(s) is or was a University faculty member, student or staff member; this article is already available through open access at http://www.biomedcentral.com. The public license is specified as CC-BY: http://creativecommons.org/licenses/by/4.0/. The library makes the deposit as a matter of fair use (for scholarly, educational, and research purposes), and to preserve the work and further secure public access to the works of the University. |
| رقم الانضمام: | edsbas.352A1106 |
| قاعدة البيانات: | BASE |
| ردمك: | 9781471220210 1471220214 |
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| DOI: | 10.1186/1471-2202-14-S1-P316 |