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jacobgranley.bsky.social

@jacobgranley.bsky.social

Machine Learning Engineer at Neuralink. Previous Postdoc at UCSB studying Machine Learning, Computational Neuroscience, and NeuroAI

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Reposted by @jacobgranley.bsky.social

Hannes Mehrer @CCN2026 @hannesmehrer.bsky.social · 07/10/2025

🧠 New preprint: we show that model-guided microstimulation can steer monkey visual behavior. Paper: arxiv.org/abs/2510.03684 🧵

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Reposted by @jacobgranley.bsky.social

Martin Schrimpf @mschrimpf.bsky.social · 08/10/2025

A glimpse at what #NeuroAI brain models might enable: a topographic vision model predicts stimulation patterns that steer complex object recognition behavior in primates. This could be a key 'software' component for visual prosthetic hardware 🧠🤖🧪

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Reposted by @jacobgranley.bsky.social

Martin Schrimpf @mschrimpf.bsky.social · 02/10/2025

I've been arguing that #NeuroAI should model the brain in health *and* in disease -- very excited to share a first step from Melika Honarmand: inducing dyslexia in vision-language-models via targeted perturbations of visual-word-form units (analogous to human VWFA) 🧠🤖🧪 arxiv.org/abs/2509.24597

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Michael Beyeler @mbeyeler.bsky.social · 27/09/2025

👁️🧠 New preprint: We demonstrate the first data-driven neural control framework for a visual cortical implant in a blind human! TL;DR Deep learning lets us synthesize efficient stimulation patterns that reliably evoke percepts, outperforming conventional calibration. www.biorxiv.org/content/10.1...

Diagram showing three ways to control brain activity with a visual prosthesis. The goal is to match a desired pattern of brain responses. One method uses a simple one-to-one mapping, another uses an inverse neural network, and a third uses gradient optimization. Each method produces a stimulation pattern, which is tested in both computer simulations and in the brain of a blind participant with an implant. The figure shows that the neural network and gradient methods reproduce the target brain activity more accurately than the simple mapping.

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