Abstract
This is a follow up paper that integrates our recent published work discussing the implementation of brain-inspired information processing system by means of finite-state machines. Using a formerly presented implementation of the liquid-state machines framework using a novel synaptic model, this study shows that such a network represents and processes input information internally using transitions among a set of discrete and finite neural temporal states. The introduced framework is coined the temporal finite-state machine (tFSM). The proposed work involves a new definition for a ”neural state” within a dynamic network and it discusses the computational capacity of the tFSM. This paper presents novel perspectives and open new avenues in importing the behaviour of spiking neural networks into the classical computational model of finite-state machines.
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References
van der Velde, F., de Kamps, M.: Neural blackboard architectures of combinatorial structures in cognition. Behavioral and Brain Sciences 29, 37–70 (2006)
Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14, 2531–2560 (2002)
Maass, W., Markram, H.: On the computational power of circuits of spiking neurons. J. Comput. Syst. Sci. 69, 593–616 (2004)
Markram, H., Wang, Y., Tsodyks, M.: Differential signaling via the same axon of neocortical pyramidal neurons. Proc. of the Nat. Academy of Sciences of the USA 95, 5323–5328 (1998)
Legenstein, R., Maass, W.: Edge of chaos and prediction of computational performance for neural microcircuit models. Neural Networks 20, 323–334 (2007)
El-laithy, K., Bogdan, M.: Synchrony state generation: An approach using stochastic synapses. J. of Artificial Intelligence and Soft Computing Research 1, 17–26 (2011)
El-laithy, K.: Towards a Brain-inispired information processing system: Modeling and analysis of synaptic dynamics. PhD thesis, Leipzig University (2011)
El-Laithy, K., Bogdan, M.: On the Capacity of Transient Internal States in Liquid-State Machines. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds.) ICANN 2011, Part II. LNCS, vol. 6792, pp. 56–63. Springer, Heidelberg (2011)
Maass, W., Sontag, E.D.: Neural systems as nonlinear filters. Neural Comput. 12, 1743–1772 (2000)
Boyd, S., Chua, L.: Fading memory and the problem of approximating nonlinear operators with volterra series. IEEE Transactions on Circuits and Systems 32, 1150–1161 (1985)
von der Malsburg, C.: The what and why of binding: The modelerąŕs perspective (1999)
Revonsuo, A., Newman, J.: Binding and consciousness. Consciousness and Cognition 8, 123–127 (1999)
Engel, A.K., Fries, P., König, P., Brecht, M., Singer, W.: Temporal binding, binocular rivalry, and consciousness. Consciousness and Cognition 8, 128–151 (1999)
Engel, A.K., Singer, W.: Temporal binding and the neural correlates of sensory awareness. Trends in Cognitive Sciences 5, 16–25 (2001)
Ikegaya, Y., Aaron, G., Cossart, R., Aronov, D., Lampl, I., Ferster, D., Yuste, R.: Synfire chains and cortical songs: Temporal modules of cortical activity. Science 304, 559–564 (2004)
Gilbert, C.D., Sigman, M.: Brain states: top-down infuences in sensory processing. Neuron 54, 677–696 (2007)
El-Laithy, K., Bogdan, M.: A Hypothetical Free Synaptic Energy Function and Related States of Synchrony. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds.) ICANN 2011, Part II. LNCS, vol. 6792, pp. 40–47. Springer, Heidelberg (2011)
Brunel, N., van Rossum, M.: Quantitative investigations of electrical nerve excitation treated as polarization: Louis lapicque 1907: translated. Biol. Cybern. 97, 341–349 (2007)
El-Laithy, K., Bogdan, M.: Synchrony State Generation in Artificial Neural Networks with Stochastic Synapses. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009, Part I. LNCS, vol. 5768, pp. 181–190. Springer, Heidelberg (2009)
El-Laithy, K., Bogdan, M.: Predicting spike-timing of a thalamic neuron using a stochastic synaptic model. In: ESANN Proceedings, pp. 357–362 (2010)
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El-Laithy, K., Bogdan, M. (2012). Temporal Finite-State Machines: A Novel Framework for the General Class of Dynamic Networks. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_52
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DOI: https://doi.org/10.1007/978-3-642-34481-7_52
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