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Decoding Natural Behavior from Neuroethological Embedding

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Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting.

Zeke Xie¹, Fengxiang He², Shaopeng Fu³

¹University of Tokyo, Bunkyo-ku, Tokyo 113-0333, Japan and RIKEN Center for AIP, Chuo-ku, Tokyo 103-0027, Japan xie@ms.k.u-tokyo.ac.jp.

Neural Computation

|July 26, 2021

Summary

Artificial neural variability (ANV) mimics human brain flexibility to improve deep learning. This approach enhances generalization and prevents catastrophic forgetting in artificial neural networks.

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Area of Science:

Artificial Intelligence
Neuroscience
Machine Learning

Background:

Deep learning models suffer from overfitting and catastrophic forgetting, unlike natural nervous systems.
Human brains exhibit neural variability, balancing accuracy with plasticity for effective learning.
This variability is crucial for motor learning and adapting to new information.

Purpose of the Study:

To introduce artificial neural variability (ANV) as a mechanism inspired by neuroscience.
To theoretically guarantee ANV's benefits for deep learning models.
To develop practical methods for implementing ANV in artificial neural networks.

Main Methods:

Proving ANV acts as an implicit regularizer of mutual information between data and models.
Devising a neural variable risk minimization (NVRM) framework.
Creating neural variable optimizers for conventional network architectures.

Main Results:

ANV theoretically guarantees improved generalizability and robustness to label noise.
ANV provides robustness against catastrophic forgetting in continual learning scenarios.
NVRM effectively mitigates overfitting, label noise memorization, and catastrophic forgetting with minimal cost.

Conclusions:

Artificial neural variability offers a promising approach to address key limitations in deep learning.
The NVRM framework provides a practical solution for implementing ANV.
This neuroscience-inspired mechanism enhances the performance and reliability of artificial neural networks.