Building Smart Machines by Utilizing Spiking Neural Networks; Current Perspectives

In this paper we survey the current state of the art in spiking neural networks research and outline our approach to building smart machines. A thorough understanding of the history, open questions, and limitations of these networks can help the research community to gain a better grip on this new technology and to bridge the missing gaps. It is necessary to look at various aspects of spiking neural networks, such as the different modeling approaches, encoding schemes, simulators and learning techniques in order to efficiently make use of these networks. One paramount characteristic of spiking neural networks is the precise timing of inputs and outputs. As a dynamic system itself, it naturally lends itself to solving problems in the continuous domain such as time series analysis. This will be the focal point of our efforts to develop a smart machine utilizing spiking neural networks

[1]  J. Fodor.  The Modularity of mind. An essay on faculty psychology , 1986 .

[2]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[3]  Dezhe Z Jin,et al.  Spiking neural network for recognizing spatiotemporal sequences of spikes. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  G. Rees.  The new phrenology: the limits of localizing cognitive processes in the brain , 2002 .

[5]  Christopher J. Bishop,et al.  Pulsed Neural Networks , 1998 .

[6]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[7]  Wulfram Gerstner,et al.  Mathematical formulations of Hebbian learning , 2002, Biological Cybernetics.

[8]  Henry Markram,et al.  Synapses as dynamic memory buffers , 2002, Neural Networks.

[9]  J J Hopfield,et al.  What is a moment? Transient synchrony as a collective mechanism for spatiotemporal integration. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Sandy Lovie.  How the mind works , 1980, Nature.

[11]  Gustavo Deco,et al.  Temporal clustering with spiking neurons and dynamic synapses: towards technological applications , 2001, Neural Networks.

[12]  V. Mountcastle,et al.  An organizing principle for cerebral function : the unit module and the distributed system , 1978 .

[13]  J J Hopfield,et al.  What is a moment? "Cortical" sensory integration over a brief interval. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Byoung-Tak Zhang,et al.  Temporal pattern recognition using a spiking neural network with delays , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[15]  B. Hamber.  Publications , 1998, Weed Technology.

[16]  SimSPiNN - A Simulator for Spike-Processing Neural Networks , .

[17]  Lloyd Watts,et al.  Event-Driven Simulation of Networks of Spiking Neurons , 1993, NIPS.

[18]  Hojjat Adeli,et al.  Spiking Neural Networks , 2009, Int. J. Neural Syst..

[19]  Olaf Booij,et al.  Temporal Pattern Classification using Spiking Neural Networks , 2004 .

[20]  Henry Markram,et al.  Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.

[21]  G. Kane.  Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[22]  J. Fodor,et al.  The Modularity of Mind: An Essay on Faculty Psychology , 1984 .

[23]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[24]  J. Hawkins,et al.  On Intelligence , 2004 .

[25]  Rich Drewes.  Modeling the brain with NCS and brainlab , 2005 .

[26]  H. Markram,et al.  Differential signaling via the same axon of neocortical pyramidal neurons. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Sander M. Bohte,et al.  Applications of spiking neural networks , 2005, Inf. Process. Lett..