Abstract
Testing the accuracy of theoretical models requires a priori knowledge of the structural and functional levels of biological systems organization. This task involves a computational complexity, where a certain level of abstraction is required. Herein we propose a simple framework to test predictive properties of probabilistic models adapted to maximize statistical independence. The proposed framework is motivated by the idea that biological systems are largely biased to the statistics of the signal to which they are exposed. To take these statistical properties into account, we use synthetic signals modulated by a bank of linear filters. To show that is possible to measure the variations between expected (ground truth) and estimate responses, we use a standard independent component algorithm as sparse code network. Our simple, but tractable framework suggests that theoretical models are likely to have predictive dispersions with interquartile (range) error of 4.78% and range varying from 3.26% to 23.89%.
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References
Atick, J.J., Redlich, A.N.: Towards a theory of early visual processing. Neural Comput. 2, 308–320 (1999)
Attneave, F.: Some informational aspects of visual perception. Psychol. Rev. 61(3), 183–193 (1954)
Barlow, H.B.: Possible principles underlying the transformation of sensory messages. In: Rosemblum, M.G. (ed.) Sensory Communication, pp. 217–234. MIT Press, Cambridge, MA (1961)
Bell, A., Sejnowski, T.J.: The ‘independent components’ of natural scenes are edge filters. Vision Research 37, 3327–3338 (1997)
Hyvärinen, A., Oja, E.: A fast fixed-point algorithm for independent component analysis. Neural Comput. 9(7), 1483–1492 (1997), http://dx.doi.org/10.1162/neco.1997.9.7.1483
Laurent, G.: A systems perspective on early olfactory coding. Science 286(5440), 723–728 (1999)
Lewicki, M.S.: Efficient coding of natural sounds. Nat. Neurosci. 5(4), 356–363 (2002)
Linsker, R.: Perceptual neural organization: some approches based on network models and information-theory. Annual Review of Neuroscience 13, 257–281 (1990)
Lucena, F., Barros, A.K., Ohnishi, N.: Emergence of autonomic transfer properties by learning efficient codes from heartbeat intervals. In: IEICE Tech. Rep. NC2010-153, vol. 110, pp. 153–158. Tokyo (March 2011)
Lucena, F., Barros, A.K., Principe, J.C., Ohnishi, N.: Statistical coding and decoding of hearteat intervals. PLoS One 6(6), e20227 (2011)
Machens, C.K., Gollisch, T., Kolesnikova, O., Herz, A.V.M.: Testing the efficiency of sensory coding with optimal stimulus ensembles. Neuron. 47(3), 447–456 (2005)
Olshausen, B.A., Field, D.J.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583), 607–609 (1996)
Rieke, F., Bodnar, D.A., Bialek, W.: Naturalistic stimuli increase the rate and efficiency of information transmission by primary auditory afferents. Proc. Biol. Sci. 262(1365), 259–265 (1995)
Schwartz, O., Simoncelli, E.P.: Natural signal statistics and sensory gain control. Nature Neuroscience 4, 819–825 (2001)
Simoncelli, E.P., Olshausen, B.A.: Natural image statistics and neural representation. Annu. Rev. Neurosci. 24, 1193–1216 (2001)
Vinje, W.E., Gallant, J.L.: Sparse coding and decorrelation in primary visual cortex during natural vision. Science 287(5456), 1273–1276 (2000)
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Lucena, F., Kugler, M., Barros, A.K., Ohnishi, N. (2011). Testing Predictive Properties of Efficient Coding Models with Synthetic Signals Modulated in Frequency. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24958-7_63
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DOI: https://doi.org/10.1007/978-3-642-24958-7_63
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