Towards Deterministic and Stochastic Computations with the Izhikevich Spiking-Neuron Model
- 2.4k Downloads
In this paper we analyze simple computations with spiking neural networks (SNN), laying the foundation for more sophisticated calculations. We consider both a deterministic and a stochastic computation framework with SNNs, by utilizing the Izhikevich neuron model in various simulated experiments. Within the deterministic-computation framework, we design and implement fundamental mathematical operators such as addition, subtraction, multiplexing and multiplication. We show that cross-inhibition of groups of neurons in a winner-takes-all (WTA) network-configuration produces considerable computation power and results in the generation of selective behavior that can be exploited in various robotic control tasks. In the stochastic-computation framework, we discuss an alternative computation paradigm to the classic von Neumann architecture, which supports information storage and decision making. This paradigm uses the experimentally-verified property of networks of randomly connected spiking neurons, of storing information as a stationary probability distribution in each of the sub-network of the SNNs. We reproduce this property by simulating the behavior of a toy-network of randomly-connected stochastic Izhikevich neurons.
This work was partially supported by the NSF-Frontiers CyberCardia Award, FWF-NFN RiSE Award, FWF-DC LMCS Award, FFG Harmonia Award, FFG Em2Apps Award, and the TUW CPPS-DK Award.
- 5.Koch, C., Segev, I.: Methods in Neuronal Modeling: From Ions to Networks. MIT Press, Cambridge (1998)Google Scholar
- 8.Pfeil, T., Grubl, A., Jeltsch, S., Muller, E., Muller, P., Petrovici, M.A., Schmuker, M., Bruderle, D., Schemmel, J., Meier, K.: Six networks on a universal neuromorphic computing substrate. arXiv preprint arXiv:1210.7083 (2012)
- 12.Hasani, R.M.: Design of CMOS silicon neurons for noise assisted computations in spiking neural networks. Politesi Digital Library of PhD and Post Graduate Theses, Politecnico di Milano (2015)Google Scholar
- 13.Hasani, R.M., Ferrari, G., Yamamoto, H., Kono, S., Ishihara, K., Fujimori, S., Tanii, T., Prati, E.: Control of the correlation of spontaneous neuron activity in biological and noise-activated CMOS artificial neural icrocircuits. arXiv preprint arXiv:1702.07426 (2017)
- 21.Binas, J., Indiveri, G., Pfeiffer, M.: Spiking analog VLSI neuron assemblies as constraint satisfaction problem solvers. In: IEEE International Symposium on Circuits and Systems (ISCAS), pp. 2094–2097. IEEE (2016)Google Scholar