High-performance Evolutionary Algorithms for Online Neuron Control

TL;DR

This paper benchmarks various optimization algorithms for neural activation maximization in visual neurons, discovering that CMA and a new SphereCMA outperform traditional methods by significant margins, guided by insights into local search and step size decay.

Contribution

It systematically compares optimizers for neural activation maximization, identifies key principles for success, and develops SphereCMA, a new optimizer based on these insights.

Findings

CMA outperforms GA by 66% in vitro.

CMA surpasses GA by 44% in vivo.

SphereCMA effectively incorporates identified principles.

Abstract

Recently, optimization has become an emerging tool for neuroscientists to study neural code. In the visual system, neurons respond to images with graded and noisy responses. Image patterns eliciting highest responses are diagnostic of the coding content of the neuron. To find these patterns, we have used black-box optimizers to search a 4096d image space, leading to the evolution of images that maximize neuronal responses. Although genetic algorithm (GA) has been commonly used, there haven't been any systematic investigations to reveal the best performing optimizer or the underlying principles necessary to improve them. Here, we conducted a large scale in silico benchmark of optimizers for activation maximization and found that Covariance Matrix Adaptation (CMA) excelled in its achieved activation. We compared CMA against GA and found that CMA surpassed the maximal activation of GA by 66% in silico and 44% in vivo. We analyzed the structure of Evolution trajectories and found that the key to success was not covariance matrix adaptation, but local search towards informative dimensions and an effective step size decay. Guided by these principles and the geometry of the image manifold, we developed SphereCMA optimizer which competed well against CMA, proving the validity of the identified principles. Code available at https://github.com/Animadversio/ActMax-Optimizer-Dev