Synchrony state generation : an approach using stochastic synapses

• Autorzy: El-Laithy K. , Bogdan M.
• Języki publikacji: EN

Abstrakty

EN

In this study, the generation of temporal synchrony within an artificial neural network is examined considering a stochastic synaptic model. A network is introduced and driven by Poisson distributed trains of spikes along with white-Gaussian noise that is added to the internal synaptic activity representing the background activity (neuronal noise). A Hebbian-based learning rule for the update of synaptic parameters is introduced. Only arbitrarily selected synapses are allowed to learn, i.e. update parameter values. Results show that a network using such a framework is able to achieve different states of synchrony via learning. Thus, the plausibility of using stochastic-based models in modeling the neural process is supported. It is also consistent with arguments claiming that synchrony is a part of the memory-recall process and copes with the accepted framework in biological neural systems.

Słowa kluczowe

EN

temporal synchrony; artificial neural network; stochastic synapses; synchrony state generation; Hebbian-based learning

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2011
• Tom: Vol. 1, No. 1
• Strony: 17--25
• Opis fizyczny: Bibliogr. 42 poz., rys.

Twórcy

autor

El-Laithy K.

• Faculty of Mathematics and Computer Science, University of Leipzig Johannisgasse 26, 04103 Leipzig, Germany

autor

Bogdan M.

• Faculty of Mathematics and Computer Science, University of Leipzig Johannisgasse 26, 04103 Leipzig, Germany

Bibliografia

• [1] B. Averbeck, P. Latham, and A. Pouget. Neural correlations, population coding and computation. Nature Reviews Neuroscience, 7:358–366, 2006.
• [2] P. Bressloff and P. Coombes. Dynamics of strongly coupled spiking neurons. Neural Comput., 12(1):91–129, 2000.
• [3] C.. Brody. Correlations without synchrony. Neural Comput., 11(7):1537–1551, 1999.
• [4] S. Campbell, D. Wang, and C. Jayaprakash. Synchrony and desynchrony in integrate-and-fire oscillators. Neural Computation, 11(7):1595–1619, 1999.
• [5] R. DeVille and C. Peskin. Synchrony and Asynchrony in a Fully Stochastic Neural Network. Bulletin of Mathematical Biology, 70(6):1608–1633, 2008.
• [6] R. Dodla and C. Wilson. Synchrony-asynchrony transitions in neuronal networks. BMC Neuroscience, 9(Suppl 1):P9, 2008.
• [7] D. Durstewitz. Self-Organizing Neural Integrator Predicts Interval Times through Climbing Activity. J. Neuroscience, 23(12):5342–5353, 2003.
• [8] K. El-Laithy and M. Bogdan. Synchrony state generation in artificial neural networks with stochastic synapses. In Artificial Neural Networks–ICANN 2009, volume 5768 of LNCS, pages 181–190. Springer, 2009.
• [9] K. El-Laithy and M. Bogdan. Predicting spiketiming of a thalamic neuron using a stochastic synaptic model. In ESANN Proceedings, pages 357–362, 2010.
• [10] J. Fellous, A. Rudolph, B. Destexhe, and T.Sejnowski. Synaptic background noise controls the input/output characteristics of single cells in an in vitro model of in vivo activity. Neuroscience, 122:811–829, 2003.
• [11] C. Gilbert and M. Sigman. Brain states: Topdown influences in sensory processing. Neuron, 54(5):677–696, 2007.
• [12] C. Gray. The temporal correlation hypothesis of visual feature integration: Still alive and well. Neuron, 24(1):31–47, September 1999.
• [13] D. Hansel, G. Mato, and C. Meunier. Synchrony in excitatory neural networks. Neural Comput., 7(2):307–337, 1995.
• [14] M. Herzog, M. Esfeld, and W. Gerstner. Consciousness & the small network argument. Neural Networks, 20(9):1054–1056, 2007.
• [15] D. Holcman and M. Tsodyks. The Emergence of Up and Down States in Cortical Networks. Science, 2(3):174–181, 2006.
• [16] R. Jolivet, A. Rauch, H. Loscher, and W. Gerstner. Predicting spike timing of neocortical pyramidal neurons by simple threshold models. J. Computational Neuroscience, 21:35–49, 2006.
• [17] R. Jolivet, F. Sch‥urmann, T. Berger, R. Naud, W. Gerstner, and A. Roth. The quantitative singleneuron modeling competition. Biological Cybernetics, 99(4):417–426, November 2008.
• [18] P. K‥onig, A. Engel, P. Roelfsema, and W. Singer. How precise is neuronal synchronization? Neural Comput., 7(3):469–485, 1995.
• [19] T. Kreuz, J. Haas, A. Morelli, H. Abarbanel, and A. Politi. Measuring spike train synchrony and reliability. BMC Neuroscience, 8(Suppl 2):P79, 2007.
• [20] H. Kr‥oger. Why are probabilistic laws governing quantum mechanics and neurobiology? Solutions and Fractals, 25:815, 2005.
• [21] J. Liaw and T. Berger. Dynamic synapse: A new concept of neural representation and computation. Hippocampus, 6:591–600, 1996.
• [22] J. Liaw and T. Berger. Computing with dynamic synapses: A case study of speech recognition. In Proc. IEEE Int. Conf. Neural Networks, pages 352–355, 1997.
• [23] B. Lindner, L. Schimansky-Geier, and A. Longtin. Maximizing spike train coherence or incoherence in the leaky integrate-and-fire model. Phys. Rev. E Stat. Nonlinear Soft Matter Phys., 66(3 Pt 1):031916, 2002.
• [24] W. Maass and A. Zador. Dynamic stochastic synapses as computational units. Neural Computation, 11:903–917, 1999.
• [25] H. Markram, Y. Wang, and M. Tsodyks. Differential signaling via the same axon of neocortical pyramidal neurons. Proc. of the Nat. Academy of Sciences of the USA, 95(9):5323–5328, 1998.
• [26] S. Mikula and E. Niebur. Rate and synchrony in feedforward networks of coincidence detectors: Analytical solution. Neural Comput., 17(4):881–902, 2005.
• [27] H. Namarvar, J. Liaw, and T. Berger. A new dynamic synapse neural network for speech recognition. In Proc. IEEE Int. Conf. Neural Networks, volume 4 pages 2985 2990 2001
• [28] T. Natschl‥ager. Efficient Computation in Networks of Spiking Neurons Simulations and Theory. PhD thesis, Institute of Theoretical Computer Science, Austria, 1999.
• [29] T. Natschl‥ager, W. Maass, and A. Zador. Efficient temporal processing with biologically realistic dynamic synapses. Computation in neural system, 12:75–78, 2001.
• [30] R. Naud, T. Berger, L. Badel, A. Roth, andW. Gerstner. Quantitative single-neuron modeling: competition 2008. In Neuroinformatics 2008, 2008.
• [31] L. Neltner and D. Hansel. On synchrony of weakly coupled neurons at low firing rate. Neural Comput., 13(4):765–774, 2001.
• [32] E. Salinas and T. Sejnowski. Correlated neuronal activity and the flow of neural information. Nature Reviews Neuroscience, 2:539–550, 2001.
• [33] S. Schreiber, J. M. Fellous, D. Whitmer, P. Tiesinga, and T. J. Sejnowski. A new correlationbased measure of spike timing reliability. Neurocomputing, 52-54:925–931, 2003.
• [34] T. Sejnowski and O. Paulsen. Network Oscillations: Emerging Computational Principles. J. Neuroscience, 26(6):1673–1676, 2006.
• [35] W. Singer. Neuronal synchrony: a versatile code for the definition of relations. Neuron, 24:49–65, 1999.
• [36] W. Singer. Understanding the brain. European Molecular Biology Org., 8:16 –19, 2007.
• [37] W. Singer and C. Gray. Visual feature integration and the temporal correlation hypothesis. Annu. Rev. Neuroscience, 18:555–586, 1995.
• [38] C. Tallon-Baudry. Attention and awareness in synchrony. Trends in Cognitive Sciences, 8(12):523–525, 2004.
• [39] M. Tsodyks, K. Pawelzik, and H. Markram. Neural networks with dynamic synapses. Neural Computation, 10:821–835, 1998.
• [40] M. Tsodyks, A. Uziel, and H. Markram. Synchrony generation in recurrent networks with frequency-dependent synapses. J. Neuroscience, 20:50, 2000.
• [41] C. von der Malsburg. The what and why of binding: The modeler’s perspective, September 1999.
• [42] C. Van Vreeswijk and D. Hansel. Patterns of synchrony in neural networks with spike adaptation. Neural Comput., 13(5):959–992, 2001.