Abstract
Bayesian statistics is has been very successful in describing behavioural data on decision making and cue integration under noisy circumstances. However, it is still an open question how the human brain actually incorporates this functionality. Here we compare three ways in which Bayes rule can be implemented using neural fields. The result is a truly dynamic framework that can easily be extended by non-Bayesian mechanisms such as learning and memory.
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References
Gold, J.I., Shadlen, M.N.: The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007)
Hillis, J.M., Watt, S.J., Landy, M.S., Banks, M.S.: Slant from texture and disparity cues: optimal cue combination. J Vis 4, 967–992 (2004)
Kuss, M., Jäkel, F., Wichmann, F.A.: Bayesian inference for psychometric functions. J. Vis. 5, 478–492 (2005)
Chen, Y., Geisler, W.S., Seidemann, E.: Optimal decoding of correlated neural population responses in the primate visual cortex. Nat. Neurosci. 9, 1412–1420 (2006)
Ma, W.J., Beck, J.M., Latham, P.E., Pouget, A.: Bayesian inference with probabilistic population codes. Nat. Neurosci. 9, 1432–1438 (2006)
Zemel, R.S., Dayan, P., Pouget, A.: Probabilistic interpretation of population codes. Neural Comput. 10, 403–430 (1998)
Rao, R.P.N.: Bayesian computation in recurrent neural circuits. Neural Comput. 16, 1–38 (2004)
Cisek, P.: Integrated neural processes for defining potential actions and deciding between them: a computational model. J. Neurosci. 26, 9761–9770 (2006)
Wilimzig, C., Schneider, S., Schner, G.: The time course of saccadic decision making: dynamic field theory. Neural Netw. 19, 1059–1074 (2006)
Erlhagen, W., Schöner, G.: Dynamic field theory of movement preparation. Psychol. Rev. 109, 545–572 (2002)
Erlhagen, W., Mukovskiy, A., Bicho, E.: A dynamic model for action understanding and goal-directed imitation. Brain Res. 1083, 174–188 (2006)
Guo, Y., Chow, C.C.: Existence and stability of standing pulses in neural networks: I. existence. SIAM J. Appl. Dyn. Sys. 4, 217–248 (2005)
Taylor, J.G.: Neural bubble dynamics in two dimensions: foundations. Bioligal Cybernetics 80, 393–409 (1999)
Erlhagen, W., Bicho, E.: The dynamic neural field approach to cognitive robotics. J. Neural. Eng. 3, R36–R54 (2006)
Trappenberg, T.P.: Fundamentals of computational neuroscience. Oxford University Press, New York (2002)
Amari, S.: Dynamics of pattern formation in lateral-inhibition type neural fields. Biol. Cybern. 27, 77–87 (1977)
Cuijpers, R.H., van Schie, H.T., Koppen, M., Erlhagen, W., Bekkering, H.: Goals and means in action observation: a computational approach. Neural Netw. 19, 311–322 (2006)
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Cuijpers, R.H., Erlhagen, W. (2008). Implementing Bayes’ Rule with Neural Fields. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_24
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DOI: https://doi.org/10.1007/978-3-540-87559-8_24
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