Function approximation by hardware spiking neural network

Spiking neural networks (SNN) represent a special class of artificial neural networks, where neu-ron models communicate by sequences of spikes. SNNs are often referred to as the third generation of neural networks that highly inspired from natural computing in the brain and recent advances in neuroscience. In this paper we implement biologically-inspired, hardware-realizable SNN architecture using integrate-and-fire units, which is capable of approximating a real-valued function. Based on the results of MATLAB simulations, hardware synthesis and FPGA implementation, it is demonstrated that the implemented hardware can approximate linear and nonlinear functions with low minimum relative error. This framework may represent a fundamental computational unit for the development of artificial SNN, opening new perspectives in pattern recognition tasks.

[1]  Peter J. Bentley,et al.  A Cellular Structure for Online Routing of Digital Spiking Neuron Axons and Dendrites on FPGAs , 2008, ICES.

[2]  Mostafa Rahimi Azghadi,et al.  A neuromorphic VLSI design for spike timing and rate based synaptic plasticity , 2013, Neural Networks.

[3]  Thomas P. Trappenberg,et al.  Fundamentals of Computational Neuroscience , 2002 .

[4]  Romain Brette,et al.  Simulating spiking neural networks on GPU , 2012, Network.

[5]  Wayne Luk,et al.  FPGA Accelerated Simulation of Biologically Plausible Spiking Neural Networks , 2009, 2009 17th IEEE Symposium on Field Programmable Custom Computing Machines.

[6]  Theodore W. Berger,et al.  Real-Time Prediction of Neuronal Population Spiking Activity Using FPGA , 2013, IEEE Transactions on Biomedical Circuits and Systems.

[7]  Steve Kilts.  Control System Components , 2008 .

[8]  Francois D. Szymanski,et al.  A Bidirectional Brain-Machine Interface Algorithm That Approximates Arbitrary Force-Fields , 2014, PloS one.

[9]  Ferdinando A. Mussa-Ivaldi,et al.  Shaping the Dynamics of a Bidirectional Neural Interface , 2012, PLoS Comput. Biol..

[10]  Michael N. Shadlen,et al.  Noise, neural codes and cortical organization , 1994, Current Opinion in Neurobiology.

[11]  Karim Faez,et al.  A novel digital implementation of neuron–astrocyte interactions , 2015 .

[12]  John J. Wade,et al.  A Biologically Inspired Training Algorithm for Spiking Neural Networks , 2007 .

[13]  Jonathan Touboul,et al.  Dynamics and bifurcations of the adaptive exponential integrate-and-fire model , 2008, Biological Cybernetics.

[14]  William Bialek,et al.  Spikes: Exploring the Neural Code , 1996 .

[15]  S. Thorpe,et al.  Seeking Categories in the Brain , 2001, Science.

[16]  B. Mandelbrot,et al.  RANDOM WALK MODELS FOR THE SPIKE ACTIVITY OF A SINGLE NEURON. , 1964, Biophysical journal.

[17]  S. Barry Cooper,et al.  Computability In Context: Computation and Logic in the Real World , 2009 .

[18]  W. Singer,et al.  Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. , 1989, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Bruno Cessac,et al.  On Dynamics of Integrate-and-Fire Neural Networks with Adaptive Conductances , 2007 .

[20]  Narayanan Vijaykrishnan,et al.  Emulating Mammalian Vision on Reconfigurable Hardware , 2012, 2012 IEEE 20th International Symposium on Field-Programmable Custom Computing Machines.

[21]  Andrew D. Back,et al.  A spiking neural network architecture for nonlinear function approximation , 2001, Neural Networks.

[22]  Clive ldMax rd Maxfield,et al.  The design warrior's guide to FPGAs , 2004 .

[23]  Karim Faez,et al.  Multiplier-less digital implementation of neuron-astrocyte signalling on FPGA , 2015, Neurocomputing.

[24]  Eugene M. Izhikevich,et al.  Which model to use for cortical spiking neurons? , 2004, IEEE Transactions on Neural Networks.

[25]  Sander M. Bohte,et al.  Computing with Spiking Neuron Networks , 2012, Handbook of Natural Computing.

[26]  Denis Fize,et al.  Speed of processing in the human visual system , 1996, Nature.

[27]  Bruno Cessac,et al.  On Dynamics of Integrate-and-Fire Neural Networks with Conductance Based Synapses , 2007, Frontiers Comput. Neurosci..

[28]  Clive Maxfield,et al.  The Design Warrior's Guide to FPGAs: Devices, Tools and Flows , 2004 .

[29]  Silvestro Micera,et al.  Brain–machine interface: closer to therapeutic reality? , 2013, The Lancet.

[30]  Russell Tessier,et al.  FPGA Architecture: Survey and Challenges , 2008, Found. Trends Electron. Des. Autom..

[31]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[32]  Benjamin Schrauwen,et al.  An experimental unification of reservoir computing methods , 2007, Neural Networks.

[33]  Wofgang Maas,et al.  Networks of spiking neurons: the third generation of neural network models , 1997 .

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

[35]  Ben Goertzel,et al.  A world survey of artificial brain projects, Part I: Large-scale brain simulations , 2010, Neurocomputing.

[36]  de GarisHugo,et al.  A world survey of artificial brain projects, Part II , 2010 .