Abstract
Izhikevich network is a relatively new neuronal network, which consists of cortical spiking model neurons with axonal conduction delays and spike-timing-dependent plasticity (STDP). In this network polychrony is identified which is neither synchrony nor asynchrony, but a phenomenon of occurence and transmission of a sequence of firing patterns with specific inter-firing intervals. In this work we use van Rossum’s distance to measure the correlation between spike trains issued by neurons in a testing polychromous group and analyse the characterisation of information flow in the group of the network.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Izhikevich, E.M.: Polychronization: Computation with Spikes. Neural Computation 18, 245–282 (2006)
Izhikevich, E.M.: Large-Scale Model of Mammalian Thalamocortical Systems. PNAS 105, 3593–3598 (2008)
Slaughter, M.: Basic Concepts in Neuroscience, International Edition, vol. 1, pp. 2–14 (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)
Izhikevich, E.M.: Spike-timing Dynamics of Neuronal Groups. Cerebral Cortex 14, 933–944 (2004)
Izhikevich, E.M.: Polychronous Wavefront Computations. International Journal of Bifurcation and Chaos 19, 1733–1739 (2009)
Wilson, H.R.: Spikes, Decisions, and Actions–the dynamical foundations of neuroscience, vol. 2, pp. 13–27. Oxford University Press, New York (1999)
Song, S., Miller, K.D., Abbott, L.F.: Competition Hebbian Learning through Spike-timing-dependent Synaptic Plasticity. Nature Neuroscience 3, 919–926 (2000)
Izhikevich, E.M.: Solving the Distal Reward Problem through Linkage of STDP and Dopamine Signaling. Cerebral Cortex 17, 2443–2452 (2007)
Markram, H., Gerstne, W.R., Sjostrom, P.J.: A History of Spike-timing-dependent Plasticity. Frontiers in Synaptic Neuroscience 3, 1–24 (2011)
Muller, L., Brette, R., Gutkin, B.: Spike-timing-dependent and feed-forward input oscillations produce precise and invariant spike phase-looking. Frontiers in Computational Neuroscience 5, 1–8 (2011)
Dayan, P., Abbott, L.F.: Theoretical Neuroscience-Computational and Mathematical Modeling of Neural System, vol. 8, pp. 293–313. The MIT Press, Cambridge (2001)
Paiva, A.R.C., Park, I., Principe, J.C.: A Comparison of Binless Spike Train Measures. Natural Computing and Applications 19, 405–419 (2010)
van Rossum, M.C.W.: A Novel Spike Distance. Neural Computation 13, 751–764 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guo, L., Yang, Z., Graham, B., Zhang, D. (2012). Characterisation of Information Flow in an Izhikevich Network. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34475-6_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-34475-6_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34474-9
Online ISBN: 978-3-642-34475-6
eBook Packages: Computer ScienceComputer Science (R0)