Abstract
Deterministic nonlinearity has been observed in experimental electrophysiological recordings performed in several areas of the brain. However, little is known about the ability to transmit a complex temporally organized activity through different types of spiking neurons. This study investigates the response of a spiking neuron model representing five archetypical types to input spike trains including deterministic information generated by a chaotic attractor. The comparison between input and output spike trains is carried out by the pattern grouping algorithm (PGA) as a function of the intensity of the background activity for each neuronal type. The results show that the thalamo-cortical, regular spiking and intrinsically busting model neurons can be good candidate in transmitting temporal information with different characteristics in a spatially organized neural network.
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References
Mpitsos, G.J.: Chaos in brain function and the problem of nonstationarity: a commentary. In: Basar, E., Bullock, T.H. (eds.) Dynamics of sensory and cognitive processing by the brain, pp. 521–535. Springer, Heidelberg (1989)
Celletti, A., Villa, A.E.P.: Low dimensional chaotic attractors in the rat brain. Biological Cybernetics 74, 387–394 (1996)
Villa, A.E.P., Tetko, I.V., Celletti, A., Riehle, A.: Chaotic dynamics in the primate motor cortex depend on motor preparation in a reaction-time task. Current Psychology of Cognition 17, 763–780 (1998)
Tetko, I.V., Villa, A.E.: A comparative study of pattern detection algorithm and dynamical system approach using simulated spike trains. In: ICANN 1997. LNCS, vol. 1327, pp. 37–42. Springer, Heidelberg (1997)
Asai, Y., Yokoi, T., Villa, A.E.P.: Detection of a dynamical system attractor from spike train analysis. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4131, pp. 623–631. Springer, Heidelberg (2006)
Tetko, I.V., Villa, A.E.P.: A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 1. detection of repeated patterns. J. Neurosci. Meth. 105, 1–14 (2001)
Abeles, M., Gat, I.: Detecting precise firing sequences in experimental data. Journal of Neuroscience Methods 107, 141–154 (2001)
Izhikevich, E.M.: Simple model of spiking neurons. IEEE Transactions on Neural Networks 14, 1569–1572 (2003)
Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE Transactions on Neural Networks 15, 1063–1070 (2004)
Zaslavskii, G.M.: The simplest case of a strange attractor. Phys. Let. 69A, 145–147 (1978)
Villa, A.E.P., Tetko, I.V.: Spatiotemporal activity patterns detected from single cell measurements from behaving animals. Proceedings SPIE 3728, 20–34 (1999)
Villa, A.E.P.: Cortical modulation of auditory processing in the thalamus. In: Lomber, S.G., Galuske, R.A.W. (eds.) Virtual lesions: Examining Cortical Function with reversible Deactivation, pp. 83–119. Oxford University Press, Oxford (2002)
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Asai, Y., Villa, A.E.P. (2008). Effect of the Background Activity on the Reconstruction of Spike Train by Spike Pattern Detection. 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_63
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DOI: https://doi.org/10.1007/978-3-540-87559-8_63
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