Neural representation of probabilities for Bayesian inference

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• 作者：Dylan Rich ; Fanny Cazettes ; Yunyan Wang…
• 关键词：Bayesian inference ; Neural coding ; Sound localization ; Barn owl ; Population code
• 刊名：Journal of Computational Neuroscience
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：38
• 期：2
• 页码：315-323
• 全文大小：827 KB
• 参考文献：1. Albeck, Y, Konishi, M (1995) Responses of neurons in the auditory pathway of the barn owl to partially correlated binaural signals. Journal of Neurophysiology 74: pp. 1689-1700
  2. Anderson, C.H., and Van Essen, D.C. (1994). Neurobiological computational systems. / Computational Intelligence Imitating Life, 213-22.
  3. Arthur, BJ (2004) Sensitivity to spectral interaural intensity difference cues in space-specific neurons of the barn owl. Journal of Comparative Physiology A 190: pp. 91-104 CrossRef
  4. Bala, ADS, Spitzer, MW, Takahashi, TT (2003) Prediction of auditory spatial acuity from neural images on the owl’s auditory space map. Nature 424: pp. 771-774 CrossRef
  5. Barber, MJ, Clark, JW, Anderson, CH (2003) Neural representation of probabilistic information. Neural Computation 15: pp. 1843-1864 CrossRef
  6. Beck, JM, Ma, WJ, Kiani, R, Hanks, T, Churchland, AK, Roitman, J, Shadlen, MN, Latham, PE, Pouget, A (2008) Probabilistic population codes for Bayesian decision making. Neuron 60: pp. 1142-1152 CrossRef
  7. Berkes, P, Orbán, G, Lengyel, M, Fiser, J (2011) Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science 331: pp. 83-87 CrossRef
  8. Deneve, S (2008) Bayesian spiking neurons I: inference. Neural Computation 20: pp. 91-117 CrossRef
  9. Edut, S, Eilam, D (2004) Protean behavior under barn-owl attack: voles alternate between freezing and fleeing and spiny mice flee in alternating patterns. Behavioural Brain Research 155: pp. 207-216 CrossRef
  10. Eliasmith, C, Anderson, CCH (2004) Neural engineering: Computation, representation, and dynamics in neurobiological systems. MIT Press, Cambridge
  11. Fetsch, CR, Pouget, A, DeAngelis, GC, Angelaki, DE (2011) Neural correlates of reliability-based cue weighting during multisensory integration. Nature Neuroscience 15: pp. 146-154 CrossRef
  12. Fischer, BJ, Pe?a, JL (2011) Owl’s behavior and neural representation predicted by Bayesian inference. Nature Neuroscience 14: pp. 1061-1066 CrossRef
  13. Fiser, J, Berkes, P, Orbán, G, Lengyel, M (2010) Statistically optimal perception and learning: from behavior to neural representations. Trends in Cognitive Science 14: pp. 119-130 CrossRef
  14. Foldiak, P.?(1993). The “Ideal Homunculus- statistical inference from neural population responses. In Eeckman, F. & Bower, J. (eds), / Computation and Neural Systems (pp. 55-0). Kluwer Academic?Publishers.
  15. Ganguli, D., & Simoncelli, E. P. (2014). Efficient sensory encoding and Bayesian inference with heterogeneous neural populations. / Neural Comput, 26, 2103-134.
  16. Girshick, AR, Landy, MS, Simoncelli, EP (2011) Cardinal rules: visual orientation perception reflects knowledge of environmental statistics. Nature Neuroscience 14: pp. 926-932 CrossRef
  17. Gold, JI, Shadlen, MN (2000) Representation of a perceptual decision in developing oculomotor commands. Nature 404: pp. 390-394 CrossRef
  18. Hausmann, L, Campenhausen, M, Endler, F, Singheiser, M, Wagner, H (2009) Improvements of sound localization abilities by the facial ruff of the barn owl (Tyto alba) as demonstrated by virtual ruff removal. PLoS One 4: pp. e7721 CrossRef
  19. Kita, H, Armstrong, W (1991) A biotin-containing compound N-(2-aminoethyl)biotinamide for intracellular labeling and neuronal tracing studies: comparison with biocytin. Journal of Neuroscience Metho
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Biomedicine
  Neurosciences
  Neurology
  Human Genetics
  Theory of Computation
  
• 出版者：Springer Netherlands
• ISSN：1573-6873

文摘

Bayesian models are often successful in describing perception and behavior, but the neural representation of probabilities remains in question. There are several distinct proposals for the neural representation of probabilities, but they have not been directly compared in an example system. Here we consider three models: a non-uniform population code where the stimulus-driven activity and distribution of preferred stimuli in the population represent a likelihood function and a prior, respectively; the sampling hypothesis which proposes that the stimulus-driven activity over time represents a posterior probability and that the spontaneous activity represents a prior; and the class of models which propose that a population of neurons represents a posterior probability in a distributed code. It has been shown that the non-uniform population code model matches the representation of auditory space generated in the owl’s external nucleus of the inferior colliculus (ICx). However, the alternative models have not been tested, nor have the three models been directly compared in any system. Here we tested the three models in the owl’s ICx. We found that spontaneous firing rate and the average stimulus-driven response of these neurons were not consistent with predictions of the sampling hypothesis. We also found that neural activity in ICx under varying levels of sensory noise did not reflect a posterior probability. On the other hand, the responses of ICx neurons were consistent with the non-uniform population code model. We further show that Bayesian inference can be implemented in the non-uniform population code model using one spike per neuron when the population is large and is thus able to support the rapid inference that is necessary for sound localization.