Task adaption by biologically inspired stochastic comodulation

• URL: http://arxiv.org/abs/2311.15053v1
• Date: Sat, 25 Nov 2023 15:21:03 GMT
• Title: Task adaption by biologically inspired stochastic comodulation
• Authors: Gauthier Boeshertz, Caroline Haimerl and Cristina Savin
• Abstract summary: We show that fine-tuning convolutional networks by gain modulation improves on deterministic gain modulation. Our results suggest that comodulation representations can enhance learning efficiency and performance in multi-task learning.
• Score: 8.59194778459436
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Brain representations must strike a balance between generalizability and adaptability. Neural codes capture general statistical regularities in the world, while dynamically adjusting to reflect current goals. One aspect of this adaptation is stochastically co-modulating neurons' gains based on their task relevance. These fluctuations then propagate downstream to guide decision-making. Here, we test the computational viability of such a scheme in the context of multi-task learning. We show that fine-tuning convolutional networks by stochastic gain modulation improves on deterministic gain modulation, achieving state-of-the-art results on the CelebA dataset. To better understand the mechanisms supporting this improvement, we explore how fine-tuning performance is affected by architecture using Cifar-100. Overall, our results suggest that stochastic comodulation can enhance learning efficiency and performance in multi-task learning, without additional learnable parameters. This offers a promising new direction for developing more flexible and robust intelligent systems.

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