Neural encoding and spike generation for Spiking Neural Networks implemented in FPGA

In this article a new digital system for generating spike pulses is presented. In real time, the system can convert digital values into artificial neural spikes for Spiking Neural Networks (SNN). The digital system can perform three basic functions: to generate spikes, to convert digital data values into pulse trains and, additionally, to encode spike trains on three major code classes reported in the scientific literature of neuroscience: rate coding, temporal coding or population coding. This system is therefore a digital interface between the physical world, computer systems, or digital data and SNN that are processed in real time. A functional prototype module was developed in a Ciclone IV FPGA using less then 300 logic blocks.

[1]  André van Schaik,et al.  An FPGA design framework for large-scale spiking neural networks , 2014, 2014 IEEE International Symposium on Circuits and Systems (ISCAS).

[2]  T. Albright,et al.  Gauging sensory representations in the brain , 1999, Trends in Neurosciences.

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

[4]  R. Kass,et al.  Multiple neural spike train data analysis: state-of-the-art and future challenges , 2004, Nature Neuroscience.

[5]  Chi-Sang Poon,et al.  Neuromorphic Silicon Neurons and Large-Scale Neural Networks: Challenges and Opportunities , 2011, Front. Neurosci..

[6]  E. Adrian,et al.  The impulses produced by sensory nerve-endings: Part II. The response of a Single End-Organ. , 2006, The Journal of physiology.

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

[8]  Rodrigo Alvarez-Icaza,et al.  Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations , 2014, Proceedings of the IEEE.

[9]  Janusz Szczepanski,et al.  Information transmission efficiency in neuronal communication systems , 2013, BMC Neuroscience.

[10]  Wayne Luk,et al.  A Large-Scale Spiking Neural Network Accelerator for FPGA Systems , 2012, ICANN.

[11]  Toby Berger,et al.  A Berger-Levy energy efficient neuron model with unequal synaptic weights , 2012, 2012 IEEE International Symposium on Information Theory Proceedings.

[12]  Jean-Pierre Rospars,et al.  REVIEW ARTICLE: Neuronal coding and spiking randomness , 2007, The European journal of neuroscience.

[13]  Wulfram Gerstner,et al.  SPIKING NEURON MODELS Single Neurons , Populations , Plasticity , 2002 .

[14]  A. Aertsen,et al.  Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding , 2010, Nature Reviews Neuroscience.

[15]  Giacomo Indiveri,et al.  Synthesizing cognition in neuromorphic electronic systems , 2013, Proceedings of the National Academy of Sciences.

[16]  Siavash Ghavami,et al.  Synchrony in neuronal communication: An energy efficient scheme , 2014, 2015 Iran Workshop on Communication and Information Theory (IWCIT).

[17]  Gregory Cohen,et al.  An FPGA Implementation of a Polychronous Spiking Neural Network with Delay Adaptation , 2013, Front. Neurosci..

[18]  A V Herz,et al.  Neural codes: firing rates and beyond. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

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

[20]  Rafael Cavalcanti-Neto,et al.  Magnitude comparison in analog spiking neural assemblies , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[21]  E. Adrian,et al.  The impulses produced by sensory nerve‐endings , 1926 .

[22]  Tarek M. Taha,et al.  FPGA Implementation of Izhikevich Spiking Neural Networks for Character Recognition , 2009, 2009 International Conference on Reconfigurable Computing and FPGAs.

[23]  Si Wu,et al.  Population Coding and Decoding in a Neural Field: A Computational Study , 2002, Neural Computation.

[24]  Joao Ranhel,et al.  Neural Assemblies and Finite State Automata , 2013, 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence.