Definition
Bayesian analysis of electrophysiological data refers to the statistical processing of data obtained in electrophysiological experiments (i.e., recordings of action potentials or voltage measurements with electrodes or imaging devices) which utilize methods from Bayesian statistics. Bayesian statistics is a framework for describing and modelling empirical data using the mathematical language of probability to model uncertainty. Bayesian statistics provides a principled and flexible framework for combining empirical observations with prior knowledge and for quantifying uncertainty. These features are especially useful for analysis questions in which the dataset sizes are small in comparison to the complexity of the model, which is often the case in neurophysiological data analysis.
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The Bayesian approach to statistics has become an established framework for analysis of...
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References
Archer E, Park IM, Pillow J (2012) Bayesian estimation of discrete entropy with mixtures of stick-breaking priors. Adv Neural Inf Process Syst 25:2024–2032
Barber D (2012) Bayesian reasoning and machine learning. Cambridge University Press, Cambridge
Bishop CM (2006) Pattern recognition and machine learning. Springer, New York
Brown EN, Kass RE, Mitra PP (2004) Multiple neural spike train data analysis: state-of-the-art and future challenges. Nat Neurosci 7(5):456–461
Chen Z (2013) An overview of Bayesian methods for neural spike train analysis. Comput Intell Neurosci 2013(251905), p 17. doi:10.1155/2013/251905
Cronin B, Stevenson IH, Sur M, Körding KP (2010) Hierarchical Bayesian modeling and Markov chain Monte Carlo sampling for tuning-curve analysis. J Neurophysiol 103(1):591–602. doi:10.1152/jn.00379.2009
Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB (2013) Bayesian data analysis, 3rd edn. Chapman and Hall/CRC
Gerwinn S, Macke J, Bethge M (2009) Bayesian population decoding of spiking neurons. Front Comput Neurosci 3:21
Gerwinn S, Macke JH, Bethge M (2010) Bayesian inference for generalized linear models for spiking neurons. Front Comput Neurosci 4:12. doi:10.3389/fn-com.2010.00012, ISSN 1662–5188 (Electronic); 1662–5188 (Linking)
Kass RE, Carlin BP, Gelman A, Neal RM (1998) Markov chain Monte Carlo in practice: a roundtable discussion. Am Stat 52(2):93–100
Kass RE, Ventura V, Brown EN (2005) Statistical issues in the analysis of neuronal data. J Neurophysiol 94(1):8–25, ISSN 0022-3077 (Print)
Marreiros AC, Stephan KE, Friston KJ (2010) Dynamic causal modeling. Scholarpedia 5(7):9568
Nemenman I, Bialek W, van Steveninck R d R (2004) Entropy and information in neural spike trains: progress on the sampling problem. Phys Rev E Stat Nonlin Soft Matter Phys 69(5 Pt 2):056111, ISSN 1539-3755 (Print)
Paninski L, Pillow J, Lewi J (2007) Statistical models for neural encoding, decoding, and optimal stimulus design. Prog Brain Res 165:493–507. doi:10.1016/S0079-6123(06)65031-0, ISSN 0079-6123 (Print)
Park M, Pillow JW (2011) Receptive field inference with localized priors. PLoS Comput Biol 7(10):e1002219. doi:10.1371/journal.pcbi.1002219
Sahani M, Linden JF (2003) How linear are auditory cortical responses?. In: Becker S, Thrun S, Obermayer K (eds) Advances in neural information processing systems. The MIT Press, Cambridge, Massachusetts, vol 15, p 317
Spiegelhalter D, Rice K (2009) Bayesian statistics. Scholarpedia 4(8):5230
Vogelstein JT, Watson BO, Packer AM, Yuste R, Jedynak B, Paninski L (2009) Spike inference from calcium imaging using sequential Monte Carlo methods. Biophys J 97(2):636–655. doi:10.1016/j.bpj.2008.08.005, ISSN 1542–0086 (Electronic)
Wood F, Fellows M, Donoghue JP, Black MJ (2004) Automatic spike sorting for neural decoding. In: Proceedings of the 27th IEEE conference on engineering in medicine and biological systems, pp 4126–4129
Wu W, Gao Y, Bienenstock E, Donoghue JP, Black MJ (2006) Bayesian population decoding of motor cortical activity using a kalman filter. Neural Comput 18(1):80–118
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Macke, J.H. (2015). Electrophysiology Analysis, Bayesian. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6675-8_448
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DOI: https://doi.org/10.1007/978-1-4614-6675-8_448
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