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From A to B: Generalizing the mathematics of artificial neural networks (ANNs) to biological neural networks (BNNs)

CORDIS · observation · Publication date unknown

the core of the AI revolution. In the past years, enormous efforts have been made to unravel their mathematical properties, leading to fundamental insights and mathematical guarantees on when and why deep learning works well. ANNs are inspired by biological neural networks (BNNs) but differ in many respects: ANNs represent functions while BNNs represent stochastic processes, and the gradient-based deep learning applied for ANNs is very different from the local updating of BNNs. BNNs are superior to ANNs in the sense that the brain learns faster and generalizes better. Despite the urgency for answers and the rich and interesting mathematical structures that BNNs create, scarcely any theoretical attempts have been made to understand learning in the brain. The stochastic process structure of BNNs and the need to understand the statistical convergence behavior call for a mathematical statistics approach. This project proposes the development of advanced mathematical tools in nonparametric and high- dimensional statistics to analyze learning in BNNs as a statistical method. The starting

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recordType

award

status

SIGNED

region

EU

value

2000000

unit

EUR