“We’ve shown that efficient learning on an artificial tree architecture, where each weight has a single route to an output unit, can achieve better classification success rates than previously achieved by DL architectures consisting of more layers and filters. This finding paves the way for efficient, biologically-inspired new AI hardware and algorithms,” said Prof. Ido Kanter, of Bar-Ilan’s Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center, who led the research.

“Highly pruned tree architectures represent a step toward a plausible biological realization of efficient dendritic tree learning by a single or several neurons, with reduced complexity and energy consumption, and biological realization of backpropagation mechanism, which is currently the central technique in AI,” added Yuval Meir, a PhD student and contributor to this work.

Efficient dendritic tree learning is based on previous research by Kanter and his experimental research team — and conducted by Dr. Roni Vardi — indicating evidence for sub-dendritic adaptation using neuronal cultures, together with other anisotropic properties of neurons, like different spike waveforms, refractory periods and maximal transmission rates.

The efficient implementation of highly pruned tree training requires a new type of hardware that differs from emerging GPUs that are better fitted to the current DL strategy. The emergence of new hardware is required to efficiently imitate brain dynamics.

Reference: “Learning on tree architectures outperforms a convolutional feedforward network” by Yuval Meir, Itamar Ben-Noam, Yarden Tzach, Shiri Hodassman and Ido Kanter, 30 January 2023, Scientific Reports.DOI: 10.1038/s41598-023-27986-6