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"Monkey Upload" - Using Neural Data for Developing Better Neural Networks.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	"Monkey Upload" - Using Neural Data for Developing Better Neural Networks./
Author:	Jain, Utkarsh.
Description:	1 online resource (84 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798383058275

"Monkey Upload" - Using Neural Data for Developing Better Neural Networks.
Jain, Utkarsh.

"Monkey Upload" - Using Neural Data for Developing Better Neural Networks. - 1 online resource (84 pages)

Source: Masters Abstracts International, Volume: 85-12.

Thesis (M.S.)--University of California, San Diego, 2024.

Includes bibliographical references

The neural co-training hypothesis (Sinz et al., 2019) proposes that training a network on a downstream task alongside predicting neural responses can transfer useful inductive biases from primate vision into neural networks, thus enhancing their robustness and ability to generalize to out-of-distribution (OOD) data. Prior research (Federer et al., 2020; Li et al., 2019; Pirlot et al., 2022; Safarani et al., 2021) has shown the benefits of regularizing neural networks with primate brain data, particularly from the V1 cortex. However, similar gains are observed when V1 data is replaced with noise distributions of similar statistical properties. This is likely because the V1 cortex encodes simpler Gabor-like filters and is highly sensitive to perturbations, resulting in inherently noisy representations. Only one previous study (Dapello et al., 2022) has examined the impact of using IT representations, which are known to be more stable and encode well-defined object identity solutions, on a network's adversarial robustness. In this research, we investigate the effects of aligning a network's representations with macaque brain data from the V1, V4, and IT cortices simultaneously while training the network on an object categorization task. We find significant improvements in the model's robustness across 19 types of corruptions, even surpassing the gains from single-stage neural alignment. Additionally, past studies often use sophisticated similarity indices, like centered kernel alignment (Kornblith et al., 2019) and representational similarity matrices (Kriegeskorte et al., 2008), for alignment. We explore the use of contrastive loss functions for neural alignment and report further improvements in the network's corruption robustness. Overall, our results highlight the utility of neural data in developing better neural networks.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383058275Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Cognitive ScienceIndex Terms--Genre/Form:

554714
Electronic books.

"Monkey Upload" - Using Neural Data for Developing Better Neural Networks.
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