Mapping Complex, Large – Scale Spiking Networks on Neural VLSI

Authors: Christian Mayr, Matthias Ehrlich, Stephan Henker, Karsten Wendt, René Schüffny

Abstract:

Traditionally, VLSI implementations of spiking neural nets have featured large neuron counts for fixed computations or small exploratory, configurable nets. This paper presents the system architecture of a large configurable neural net system employing a dedicated mapping algorithm for projecting the targeted biology-analog nets and dynamics onto the hardware with its attendant constraints.

Keywords: Large scale VLSI neural net, topology mapping, complex pulse communication.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1071320

References:

[1] T. Morie, M. Nagata, and A. Iwata, "Design of a Pixel-Parallel Feature Extraction VLSI System for Biologically-Inspired Object Recognition Methods," Proc. International Symposium on Nonlinear Theory and its Application NOLTA ÔÇÿ01, pp. 371-374, 2001.
[2] J. Schreiter, U. Ramacher, A. Heittmann, D. Matolin, and R. Sch├╝ffny, "Cellular pulse coupled neural network with adaptive weights for image segmentation and its VLSI implementation," in Proc. IS&T/SPIE 16th International Symposium on Electronic Imaging: Science and Technology, San Jose (CA), USA, 2004, 5298, pp. 290-296.
[3] G. Indiveri, E. Chicca, and R. Douglas, "A VLSI reconfigurable network of integrate-and-fire neurons with spike-based learning synapses," Proceedings of European Symposium on Artificial Neural Networks ESANN'2004, pp. 405-410, 2004.
[4] S. Saighi, J. Tomas, Y. Bornat, and S. Renaud, "A conductance-based silicon neuron with dynamically tunable model parameters," Proceedings of Second International IEEE EMBS Conference on Neural Engineering, pp. 285-288, 2005.
[5] J. Schemmel, K. Meier, and E Mueller, "A New VLSI Model of Neural Microcircuits Including Spike Time Dependent Plasticity," Proceedings of the 2004 International Joint Conference on Neural Networks IJCNN'04, pp. 1711-1716, 2004.
[6] E. M. Izhikevich and N. S. Desai, "Relating STDP to BCM," Neural Computation, vol. 15, no. 7, pp. 151-1-1523, July 2003.
[7] H. Markram, Y. Wang, and M. Tsodyks, "Differential signaling via the same axon of neocortical pyramidal neurons," Proc. Natl. Acad. Sci., vol. 95, no. 9, pp. 5323-5328, April 1998.
[8] R. Legenstein, C. Näger, and W. Maass, "What can a neuron learn with spike-timing-dependent plasticity?," Neural Computation, vol. 17, no. 11, pp. 2337-2382, Nov. 2005.
[9] T. Binzegger, R. J. Douglas, and K. A. C. Martin, "A Quantitative Map of the Circuit of Cat Primary Visual Cortex," The Journal of Neuroscience, pp. 8441-8453, 2004.
[10] Y.Saad, Iterative Methods for Sparse Linear Systems. Boston MA: PWS Publishing 1996.
[11] S. Haeusler and W. Maass, "A Statistical Analysis of Information- Processing Properties of Lamina-Specific Cortical Microcircuit Models," in Cerebral Cortex, vol. 17, no. 1, pp. 149-162, 2007.
[12] Y. Chen, Z. Nakao, and X. Fang, "A Parallel Genetic Algorithm Based on the Island Model for Image Restoration," Proceedings of the 1996 IEEE Signal Processing Society Workshop , pp. 109-118, 1996.