On the biological plausibility of orthogonal initialisation for solving gradient instability in deep neural networks

Nikolay Manchev; Michael Spratling

doi:10.1109/iscmi56532.2022.10068489

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arXiv · 2211.08408

On the biological plausibility of orthogonal initialisation for solving gradient instability in deep neural networks

Nikolay Manchev·Michael Spratling

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Abstract

Initialising the synaptic weights of artificial neural networks (ANNs) with orthogonal matrices is known to alleviate vanishing and exploding gradient problems. A major objection against such initialisation schemes is that they are deemed biologically implausible as they mandate factorization techniques that are difficult to attribute to a neurobiological process. This paper presents two initialisation schemes that allow a network to naturally evolve its weights to form orthogonal matrices, provides theoretical analysis that pre-training orthogonalisation always converges, and empirically confirms that the proposed schemes outperform randomly initialised recurrent and feedforward networks.

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Nikolay Manchev, Michael Spratling. 2022-10-27. On the biological plausibility of orthogonal initialisation for solving gradient instability in deep neural networks. https://doi.org/10.1109/iscmi56532.2022.10068489

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