September 14th, 2021
Inspired by more detailed modeling of biological neurons, Spiking neural networks (SNNs) have been investigated both as more biologically plausible and potentially more powerful models of neural computation, and also with the aim of extracting biological neurons’ energy efficiency; the performance of such networks however has remained lacking compared to classical artificial neural networks (ANNs). Here, we demonstrate how a novel surrogate gradient combined with recurrent networks of tunable and adaptive spiking neurons yields state-of-the-art for SNNs on challenging benchmarks in the time-domain, like speech and gesture recognition. This also exceeds the performance of standard classical recurrent neural networks (RNNs) and approaches that of the best modern ANNs. As these SNNs exhibit sparse spiking, we show that theoretically they are one to three orders of magnitude more computationally efficient compared to RNNs with comparable performance.
Together, we argue that this positions SNNs as an attractive solution for AI hardware implementations.
Prof. Dr. Sander M. Bohté
Seminarraum IGI - Inffeldgasse 16b/I
Friday, 17 September 2021 11.00 – 12.45
IGI TU Graz