Fuzzing the brain: automated stress testing for the safety of ML-driven neurostimulation
Mara Downing, Matthew Peng, Jacob Granley, Michael Beyeler, Tevfik Bultan
TL;DR
This paper introduces a new method to test the safety of machine learning models used in neurostimulation devices by identifying potentially harmful stimulation patterns.
Contribution
The novel contribution is applying coverage-guided fuzzing to detect unsafe ML-driven neurostimulation outputs.
Findings
Fuzzing reveals diverse unsafe stimulation regimes exceeding biophysical limits in ML models for the retina and cortex.
Violation-output coverage metrics identify the highest number and diversity of unsafe outputs across different model architectures.
Abstract
Objective. Machine learning (ML) models are increasingly used to generate electrical stimulation patterns in neuroprosthetic devices such as visual prostheses. While these models promise precise and personalized control, they also introduce new safety risks when model outputs are delivered directly to neural tissue. We propose a systematic, quantitative approach to detect and characterize unsafe stimulation patterns in ML-driven neurostimulation systems. Approach. We adapt an automated software testing technique known as coverage-guided fuzzing to the domain of neural stimulation. Here, fuzzing performs stress testing by perturbing model inputs and tracking whether resulting stimulation violates biophysical limits on charge density, instantaneous current, or electrode co-activation. The framework treats encoders as black boxes and steers exploration with coverage metrics that quantify…
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Taxonomy
TopicsNeuroscience and Neural Engineering · Advanced Memory and Neural Computing · Photoreceptor and optogenetics research