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On a spike train probability model with interacting neural units
Synaptic energy drives the information processing mechanisms in spiking neural networks
1.  Faculty of Mathematics and Computer Science, Dept. of Computer Engineering, Leipzig University, Germany, Germany 
References:
[1] 
A. B. Barrett, G. O. Billings, R. G. M. Morris and M. C. W. van Rossum, State based model of longterm potentiation and synaptic tagging and capture, PLoS Comput. Biol., 5 (2009), e1000259, 12 pp. doi: 10.1371/journal.pcbi.1000259. 
[2] 
K. Ellaithy, Towards a BrainInspired Information Processing System: Modeling and Analysis of Synaptic Dynamics, Ph.D thesis, Leipzig University, 2011. 
[3] 
K. ElLaithy and M. Bogdan, Synchrony state generation in artificial neural networks with stochastic synapses, in Artificial Neural Networks  ICANN 2009, Lecture Notes in Computer Science, 5768, Springer, 2009, 181190. 
[4] 
K. ElLaithy and M. Bogdan, Predicting spiketiming of a thalamic neuron using a stochastic synaptic model, in ESANN Proceedings, 2010, 357362. 
[5] 
K. ElLaithy and M. Bogdan, A hypothetical free synaptic energy function and related states of synchrony, in Artificial Neural Networks and Machine Learning  ICANN 2011, Lecture Notes in Computer Science, 6792, Springer, 2011, 4047. 
[6] 
K. ElLaithy and M. Bogdan, On the capacity of transient internal states in liquidstate machines, in Artificial Neural Networks and Machine Learning  ICANN 2011, Lecture Notes in Computer Science, 6792, Springer, 2011, 5663. 
[7] 
K. Ellaithy and M. Bogdan, Synchrony state generation: An approach using stochastic synapses, J. of Artificial Intelligence and Soft Computing Research, 1 (2011), 1726. 
[8] 
K. ElLaithy and M. Bogdan, Temporal finitestate machines: A novel framework for the general class of dynamic networks, in ICONIP 2012, LNCS, 7664, Springer, 2012, 425434. 
[9] 
C. Eliasmith and C. H. Anderson, Neural Engineering. Computation, Representation, and Dynamics in Neurobiological Systems, Computational Neuroscience, A Bradford Book, MIT Press, Cambridge, MA, 2003. 
[10] 
A. K. Engel, P. Fries, P. Knig, M. Brecht and W. Singer, Temporal binding, binocular rivalry, and consciousness, Consciousness and Cognition, 8 (1999), 128151. 
[11] 
A. K. Engel and W. Singer, Temporal binding and the neural correlates of sensory awareness, Trends in Cognitive Sciences, 5 (2001), 1625. 
[12] 
K. Friston, The freeenergy principle: A rough guide to the brain?, Trends in Cognitive Sciences, 13 (2009), 293301. 
[13] 
K. Friston, The freeenergy principle: A unified brain theory Nature Reviews Neuroscience, 11 (2010), 127138. 
[14] 
H. M. Fuchs, Neural Networks with Dynamic Synapses, Ph.D thesis, Institute of Theoretical Computer Science, Austria, 1998. 
[15] 
J. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, Proc. Nat. Acad. Sci. U.S.A., 79 (1982), 25542558. doi: 10.1073/pnas.79.8.2554. 
[16] 
Y. Ikegaya, G. Aaron, R. Cossart, D. Aronov, I. Lampl, D. Ferster and R. Yuste, Synfire chains and cortical songs: Temporal modules of cortical activity, Science, 304 (2004), 559564. doi: 10.1126/science.1093173. 
[17] 
E. Izhikevich, Which model to use for cortical spiking neurons? IEEE Transactions on Neural Networks, 15 (2004), 10631070. doi: 10.1109/TNN.2004.832719. 
[18] 
E. M. Izhikevich, Polychronization: Computation with spikes, Neural Comput., 18 (2006), 245282. doi: 10.1162/089976606775093882. 
[19] 
A. Levina, J. M. Herrmann and T. Geisel, Phase transitions towards criticality in a neural system with adaptive interactions, Physical Review Letters, 102 (2009), 118110, 4 pp. doi: 10.1103/PhysRevLett.102.118110. 
[20] 
J. M. Montgomery and D. V. Madison, Statedependent heterogeneity in synaptic depression between pyramidal cell pairs, Neuron, 33 (2002), 765777. doi: 10.1016/S08966273(02)006062. 
[21] 
J. M. Montgomery and D. V. Madison, Discrete synaptic states define a major mechanism of synapse plasticity, Trends in Neurosciences, 27 (2004), 744750. doi: 10.1016/j.tins.2004.10.006. 
[22] 
A. Morrison, M. Diesmann and W. Gerstner, Phenomenological models of synaptic plasticity based on spike timing, Biol. Cybernet., 98 (2008), 459478. doi: 10.1007/s0042200802331. 
[23] 
P. S. Neelakanta and D. DeGroff, Neural Network Modeling: Statistical Mechanics and Cybernetics Perspectives, CRC Press, 1994. 
[24] 
A. Revonsuo and J. Newman, Binding and consciousness, Consciousness and Cognition, 8 (1999), 123127. doi: 10.1006/ccog.1999.0393. 
[25] 
C. Sarasola, A. d'Anjou, F. J. Torrealdea and M. Graña, Minimization of the energy flow in the synchronization of nonidentical chaotic systems, Phys. Rev. E, 72 (2005), 026223, 6 pp. doi: 10.1103/PhysRevE.72.026223. 
[26] 
A. P. Shon and R. P. N. Rao, Temporal Sequence Learning with Dynamic Synapses, Technical report, UW CSE, 2003. 
[27] 
W. Singer, Understanding the brain, EMBO Reports, 8 (2007), S16S19. doi: 10.1038/sj.embor.7400994. 
[28] 
S. Stringer, E. Rolls and T. Trappenberg, Selforganizing continuous attractor network models of hippocampal spatial view cells, Neurobiology of Learning and Memory, 83 (2005), 7992. doi: 10.1016/j.nlm.2004.08.003. 
[29] 
F. J. Torrealdea, A. d'Anjou, M. Graña and C. Sarasola, Energy aspects of the synchronization of model neurons, Physical Review E, 74 (2006), 011905, 6 pp. doi: 10.1103/PhysRevE.74.011905. 
[30] 
C. von der Malsburg, The what and why of binding: The modeler's perspective, Neuron, 24 (1999), 95104. 
show all references
References:
[1] 
A. B. Barrett, G. O. Billings, R. G. M. Morris and M. C. W. van Rossum, State based model of longterm potentiation and synaptic tagging and capture, PLoS Comput. Biol., 5 (2009), e1000259, 12 pp. doi: 10.1371/journal.pcbi.1000259. 
[2] 
K. Ellaithy, Towards a BrainInspired Information Processing System: Modeling and Analysis of Synaptic Dynamics, Ph.D thesis, Leipzig University, 2011. 
[3] 
K. ElLaithy and M. Bogdan, Synchrony state generation in artificial neural networks with stochastic synapses, in Artificial Neural Networks  ICANN 2009, Lecture Notes in Computer Science, 5768, Springer, 2009, 181190. 
[4] 
K. ElLaithy and M. Bogdan, Predicting spiketiming of a thalamic neuron using a stochastic synaptic model, in ESANN Proceedings, 2010, 357362. 
[5] 
K. ElLaithy and M. Bogdan, A hypothetical free synaptic energy function and related states of synchrony, in Artificial Neural Networks and Machine Learning  ICANN 2011, Lecture Notes in Computer Science, 6792, Springer, 2011, 4047. 
[6] 
K. ElLaithy and M. Bogdan, On the capacity of transient internal states in liquidstate machines, in Artificial Neural Networks and Machine Learning  ICANN 2011, Lecture Notes in Computer Science, 6792, Springer, 2011, 5663. 
[7] 
K. Ellaithy and M. Bogdan, Synchrony state generation: An approach using stochastic synapses, J. of Artificial Intelligence and Soft Computing Research, 1 (2011), 1726. 
[8] 
K. ElLaithy and M. Bogdan, Temporal finitestate machines: A novel framework for the general class of dynamic networks, in ICONIP 2012, LNCS, 7664, Springer, 2012, 425434. 
[9] 
C. Eliasmith and C. H. Anderson, Neural Engineering. Computation, Representation, and Dynamics in Neurobiological Systems, Computational Neuroscience, A Bradford Book, MIT Press, Cambridge, MA, 2003. 
[10] 
A. K. Engel, P. Fries, P. Knig, M. Brecht and W. Singer, Temporal binding, binocular rivalry, and consciousness, Consciousness and Cognition, 8 (1999), 128151. 
[11] 
A. K. Engel and W. Singer, Temporal binding and the neural correlates of sensory awareness, Trends in Cognitive Sciences, 5 (2001), 1625. 
[12] 
K. Friston, The freeenergy principle: A rough guide to the brain?, Trends in Cognitive Sciences, 13 (2009), 293301. 
[13] 
K. Friston, The freeenergy principle: A unified brain theory Nature Reviews Neuroscience, 11 (2010), 127138. 
[14] 
H. M. Fuchs, Neural Networks with Dynamic Synapses, Ph.D thesis, Institute of Theoretical Computer Science, Austria, 1998. 
[15] 
J. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, Proc. Nat. Acad. Sci. U.S.A., 79 (1982), 25542558. doi: 10.1073/pnas.79.8.2554. 
[16] 
Y. Ikegaya, G. Aaron, R. Cossart, D. Aronov, I. Lampl, D. Ferster and R. Yuste, Synfire chains and cortical songs: Temporal modules of cortical activity, Science, 304 (2004), 559564. doi: 10.1126/science.1093173. 
[17] 
E. Izhikevich, Which model to use for cortical spiking neurons? IEEE Transactions on Neural Networks, 15 (2004), 10631070. doi: 10.1109/TNN.2004.832719. 
[18] 
E. M. Izhikevich, Polychronization: Computation with spikes, Neural Comput., 18 (2006), 245282. doi: 10.1162/089976606775093882. 
[19] 
A. Levina, J. M. Herrmann and T. Geisel, Phase transitions towards criticality in a neural system with adaptive interactions, Physical Review Letters, 102 (2009), 118110, 4 pp. doi: 10.1103/PhysRevLett.102.118110. 
[20] 
J. M. Montgomery and D. V. Madison, Statedependent heterogeneity in synaptic depression between pyramidal cell pairs, Neuron, 33 (2002), 765777. doi: 10.1016/S08966273(02)006062. 
[21] 
J. M. Montgomery and D. V. Madison, Discrete synaptic states define a major mechanism of synapse plasticity, Trends in Neurosciences, 27 (2004), 744750. doi: 10.1016/j.tins.2004.10.006. 
[22] 
A. Morrison, M. Diesmann and W. Gerstner, Phenomenological models of synaptic plasticity based on spike timing, Biol. Cybernet., 98 (2008), 459478. doi: 10.1007/s0042200802331. 
[23] 
P. S. Neelakanta and D. DeGroff, Neural Network Modeling: Statistical Mechanics and Cybernetics Perspectives, CRC Press, 1994. 
[24] 
A. Revonsuo and J. Newman, Binding and consciousness, Consciousness and Cognition, 8 (1999), 123127. doi: 10.1006/ccog.1999.0393. 
[25] 
C. Sarasola, A. d'Anjou, F. J. Torrealdea and M. Graña, Minimization of the energy flow in the synchronization of nonidentical chaotic systems, Phys. Rev. E, 72 (2005), 026223, 6 pp. doi: 10.1103/PhysRevE.72.026223. 
[26] 
A. P. Shon and R. P. N. Rao, Temporal Sequence Learning with Dynamic Synapses, Technical report, UW CSE, 2003. 
[27] 
W. Singer, Understanding the brain, EMBO Reports, 8 (2007), S16S19. doi: 10.1038/sj.embor.7400994. 
[28] 
S. Stringer, E. Rolls and T. Trappenberg, Selforganizing continuous attractor network models of hippocampal spatial view cells, Neurobiology of Learning and Memory, 83 (2005), 7992. doi: 10.1016/j.nlm.2004.08.003. 
[29] 
F. J. Torrealdea, A. d'Anjou, M. Graña and C. Sarasola, Energy aspects of the synchronization of model neurons, Physical Review E, 74 (2006), 011905, 6 pp. doi: 10.1103/PhysRevE.74.011905. 
[30] 
C. von der Malsburg, The what and why of binding: The modeler's perspective, Neuron, 24 (1999), 95104. 
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