دورية أكاديمية
Optimal Encoding in Stochastic Latent-Variable Models.
| العنوان: | Optimal Encoding in Stochastic Latent-Variable Models. |
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| المؤلفون: | Rule ME; Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK., Sorbaro M; Institute of Neuroinformatics, University of Zürich and ETH, 8057 Zürich, Switzerland., Hennig MH; Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK. |
| المصدر: | Entropy (Basel, Switzerland) [Entropy (Basel)] 2020 Jun 28; Vol. 22 (7). Date of Electronic Publication: 2020 Jun 28. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: MDPI Country of Publication: Switzerland NLM ID: 101243874 Publication Model: Electronic Cited Medium: Internet ISSN: 1099-4300 (Electronic) Linking ISSN: 10994300 NLM ISO Abbreviation: Entropy (Basel) Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: Basel, Switzerland : MDPI, 1999- |
| مستخلص: | In this work we explore encoding strategies learned by statistical models of sensory coding in noisy spiking networks. Early stages of sensory communication in neural systems can be viewed as encoding channels in the information-theoretic sense. However, neural populations face constraints not commonly considered in communications theory. Using restricted Boltzmann machines as a model of sensory encoding, we find that networks with sufficient capacity learn to balance precision and noise-robustness in order to adaptively communicate stimuli with varying information content. Mirroring variability suppression observed in sensory systems, informative stimuli are encoded with high precision, at the cost of more variable responses to frequent, hence less informative stimuli. Curiously, we also find that statistical criticality in the neural population code emerges at model sizes where the input statistics are well captured. These phenomena have well-defined thermodynamic interpretations, and we discuss their connection to prevailing theories of coding and statistical criticality in neural populations. |
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| معلومات مُعتمدة: | EP/L027208/1 Engineering and Physical Sciences Research Council; EP/F500385/1 Engineering and Physical Sciences Research Council; BB/F529254/1 United Kingdom BB_ Biotechnology and Biological Sciences Research Council |
| فهرسة مساهمة: | Keywords: encoding; information theory; neural networks; sensory systems |
| تواريخ الأحداث: | Date Created: 20201208 Latest Revision: 20240329 |
| رمز التحديث: | 20240329 |
| مُعرف محوري في PubMed: | PMC7517251 |
| DOI: | 10.3390/e22070714 |
| PMID: | 33286485 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1099-4300 |
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| DOI: | 10.3390/e22070714 |