Spiking dynamic neural fields architectures on FPGA

Neuromorphic engineering is a very active field aiming to design dedicated hardware architectures to simulate the tremendous power and complexity of the brain at real time speed. Many high scaled generic projects are a success but we focus on decentralized embeddable implementations of dynamic neural fields (DNFs): a popular building blocks approach to simulate high level cognitive behaviors. The main difficulty of this approach is its mandatory all-to-all connectivity within the neural network which does not fit hardware constraints. Here we show that it is possible to decentralize the DNF computations using a cellular grid of spiking neurons with stochastic transmissions mapped onto a field programmable gate array (FPGA). The advantages of these randomly spiking dynamic neural fields (RSDNFs) are a dedicated 1-bit probabilistic XY broadcast routing network with inherent synaptic weights computations that provides hardware compatibility thanks to the 4-neighbor cellular connectivity. Moreover, this implementation strategy exhibits fault tolerance properties but it is more area greedy and time consuming than a standard implementation that broadcasts neuron addresses and coordinates using the address event representation (AER) on a centralized bus.

[1]  Misha Anne Mahowald,et al.  VLSI analogs of neuronal visual processing: a synthesis of form and function , 1992 .

[2]  Sylvain Chevallier,et al.  Visual focus with spiking neurons , 2008, ESANN.

[3]  Bernard Girau,et al.  Visual attention using spiking neural maps , 2011, The 2011 International Joint Conference on Neural Networks.

[4]  Leon O. Chua,et al.  Cellular neural networks: applications , 1988 .

[5]  Bernard Girau,et al.  Randomly Spiking Dynamic Neural Fields , 2015, ACM J. Emerg. Technol. Comput. Syst..

[6]  Andrew S. Cassidy,et al.  Design of silicon brains in the nano-CMOS era: Spiking neurons, learning synapses and neural architecture optimization , 2013, Neural Networks.

[7]  Steve B. Furber,et al.  The SpiNNaker Project , 2014, Proceedings of the IEEE.

[8]  J. Cowan,et al.  Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.

[9]  Yulia Sandamirskaya,et al.  Dynamic neural fields as a step toward cognitive neuromorphic architectures , 2014, Front. Neurosci..

[10]  Johannes Schemmel,et al.  A wafer-scale neuromorphic hardware system for large-scale neural modeling , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

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

[12]  Ran Ginosar,et al.  Scalable network-on-chip architecture for configurable neural networks , 2011, Microprocess. Microsystems.

[13]  Jérémy Fix,et al.  A Dynamic Neural Field Approach to the Covert and Overt Deployment of Spatial Attention , 2011, Cognitive Computation.

[14]  Jean-Charles Quinton,et al.  Exploring and optimizing dynamic neural fields parameters using Genetic Algorithms , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[15]  S. Amari.  Dynamics of pattern formation in lateral-inhibition type neural fields , 1977, Biological Cybernetics.

[16]  Gregor Schöner,et al.  A Neural Dynamic Architecture Resolves Phrases about Spatial Relations in Visual Scenes , 2014, ICANN.

[17]  Bernard Girau,et al.  Evolution of 2-Dimensional Cellular Automata as Pseudo-random Number Generators , 2012, ACRI.

[18]  Mathieu Lefort,et al.  Self-organization of neural maps using a modulated BCM rule within a multimodal architecture , 2010, BICS 2010.

[19]  Giacomo Indiveri,et al.  Mapping arbitrary mathematical functions and dynamical systems to neuromorphic VLSI circuits for spike-based neural computation , 2014, 2014 IEEE International Symposium on Circuits and Systems (ISCAS).

[20]  Bernard Girau,et al.  Bio-inspired visual sequences classification , 2010, BICS 2010.