Computing with Biophysical and Hardware-Efficient Neural Models.*

K. Selynin, R.M. Hasani, D. Ratasich, E. Bartocci, and R. Grosu.

In this paper we evaluate how the seminal, biophysical, Hodgkin Huxley model, and the hardware-efficient, TrueNorth model, of spiking neurons, can be used to perform computations on spike rates in frequency domain. This side-by-side evaluation allows us to draw conclusions on how fundamental arithmetic operations can be realized by means of spiking neurons, and what assumptions should be made on the input to guarantee the correctness of the computed result. We validated our approach in simulation and consider this work as a first step towards FPGA implementation of neuromorphic accelerators based on spiking models.

In Proc. of IWANN'17, the 14th International Work-Conference on Artificial and Natural Neural Networks, Cadiz, Spain, June, 2017, Springer, LNCS.

*This work was partially supported by the NSF-Frontiers Cyber-Physical Heart Award, FWF-NFN RiSE Award, FWF-DC LMCS Award, FFG Harmonia Award, FFG Em2Apps Award, and the TUW CPPS-DK Award.