Abstract
This study is an attempt to answer the question – what kind of spiking neural networks could efficiently transform rate-coded input signal into temporally coded form – specific activity of neuronal groups with strictly fixed temporal delays between spikes emitted by different neurons in every group. Since theoretical approach to the solution of this problem appears to be very hard or impossible the network configurations performing this task efficiently were found by means of genetic algorithm. Exploration of their structure showed that while excitatory neurons form the groups with stereotypical firing patterns, the inhibitory neurons of the network make these patterns specific for different rate-coded stimuli and, thus, play the key role in conversion of rate-coded input signal to temporal code.
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Notes
- 1.
2 NVIDIA GTX TITAN-X cards were used in this work.
References
Izhikevich, E.: Polychronization: computation with spikes. Neural Comput. 18, 245–282 (2006)
Ikegaya, Y., Aaron, G., Cossart, R., Aronov, D., Lampl, I., Ferster, D., et al.: Synfire chains and cortical songs: temporal modules of cortical activity. Science 304, 559–564 (2004)
Butts, D.A., Weng, C., Jin, J., Yeh, C.I., Lesica, N.A., Alonso, J.M., Stanley, G.B.: Temporal precision in the neural code and the timescales of natural vision. Nature 449, 92–95 (2007)
Szatmary, B., Izhikevich, E.: Spike-timing theory of working memory. PLoS Comput. Biol. 6, e1000879 (2010)
Gardner, B., Sporea, I., Grüning, A.: Learning spatiotemporally encoded pattern; transformations in structured spiking neural networks. Neural Comput. 27, 2548–2586 (2015)
Guise, M., Knott, A., Benuskova, L.: Bayesian model of polychronicity. Neural Comput. 26, 1–22 (2014)
Mehta, M.R., Lee, A.K., Wilson, M.A.: Role of experience and oscillations in transforming a rate code into a temporal code. Nature 417, 741–746 (2002)
Kiselev, M.: Homogenous chaotic network serving as a rate/population code to temporal code converter. Comput. Intell. Neurosci. 2014, 476580 (2014). doi:10.1155/2014/476580
Kiselev, M.: Self-organization process in large spiking neural networks leading to formation of working memory mechanism. In: Rojas, I., Joya, G., Gabestany, J. (eds.) IWANN 2013, Part I. LNCS, vol. 7902, pp. 510–517. Springer, Heidelberg (2013)
Martinez, R., Paugam-Moisy, H.: Algorithms for structural and dynamical polychronous groups detection. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009, Part II. LNCS, vol. 5769, pp. 75–84. Springer, Heidelberg (2009)
Kiselev, M.: Rate coding vs. temporal coding – is optimum between? Accepted for Publication in Proceedings of IJCNN-2016 (2016)
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Kiselev, M.V. (2016). Conversion from Rate Code to Temporal Code – Crucial Role of Inhibition. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_76
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DOI: https://doi.org/10.1007/978-3-319-40663-3_76
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