Abstract
Spiking neural networks (SNNs) are considered to be more biologically realistic compared to typical rate-coded networks as they can model closely different types of neurons and their temporal dynamics. Typical spiking models use a number of fixed parameters such as the ratio between excitatory and inhibitory neurons. However, the parameters that are used in these models focus almost exclusively on our understanding of the neocortex with, for example, 80% of neurons chosen as excitatory and 20% inhibitory. In this paper we will evaluate how varying the ratio of excitatory versus inhibitory neurons, axonal conduction delays and the number of synaptic connections affect a SNN model by observing the change in mean firing rate and polychronization. Our main focus is to examine the effect on the emergence of spatiotemporal time-locked patterns, known as polychronous groups (PNGs). We show that the number of PNGs varies dramatically with a changing proportion of inhibitory neurons, that they increase exponentially as the number of synaptic connections is increased and that they decrease as the maximum axonal delays in the network increases. Our findings show that if we are to use SNNs and PNGs to model cognitive functions we must take into account these critical parameters.
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References
Shepherd, G.M.: The Organization of the Brain. Oxford University Press (2004)
Izhikevich, E.M.: Polychronization: Computation with spikes. Neural Computation 18(2), 245–282 (2006)
Szatmáry, B., Izhikevich, E.M.: Spike-timing theory of working memory. PLoS Comput. Biol. 6(8), e1000879 (2010)
Vertes, P.E., Duke, T.: Effect of network topology on neuronal encoding based on spatiotemporal patterns of spikes. HFSP Journal 4(3-4), 153–163 (2010)
Maier, W.L., Miller, B.N.: A minimal model for the study of polychronous groups. Time 2, 8 (2008)
Notley, S., Grüning, A.: Improved spike-timed mappings using a tri-phasic spike timing-dependent plasticity rule. In: Proceedings of the International Joint Conference on Neural Networks (accepted, 2012)
Chrol-Cannon, J., Grüning, A., Jin, Y.: The emergence of polychronous groups under varying input patterns, plasticity rules and network connectivities. In: Proceedings of the International Joint Conference on Neural Networks (accepted, 2012)
Cowan, N.: An Embedded-Processes Model of Working Memory. Cambridge University Press (1999)
Izhikevich, E.M.: Simple model of spiking neurons. IEEE Transactions on Neural Networks 14(6), 1569–1572 (2003)
Bi, G.Q., Poo, M.M.: Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. The Journal of Neuroscience 18(24), 10464–10472 (1998)
Song, S., Miller, K.D., Abbott, L.F.: Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3(9), 919–926 (2000), doi:10.1038/78829
Megías, M., Emri, Z., Freund, T.F., Gulyás, A.I.: Total number and distribution of inhibitory and excitatory synapses on hippocampal ca1 pyramidal cells. Neuroscience 102(3), 527–540 (2001)
Swadlow, H.A.: Physiological properties of individual cerebral axons studied in vivo for as long as one year. Journal of Neurophysiology 54(5), 1346–1362 (1985)
Paugam-Moisy, H., Martinez, R., Bengio, S.: Delay learning and polychronization for reservoir computing. Neurocomput. 71(7-9), 1143–1158 (2008)
Miller, G.A.: The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review 101(2), 343–352 (1994)
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Ioannou, P., Casey, M., Grüning, A. (2012). Evaluating the Effect of Spiking Network Parameters on Polychronization. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_33
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DOI: https://doi.org/10.1007/978-3-642-33269-2_33
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