Lightnews — Scholar-powered news

Hannes Mehrer @hannesmehrer.bsky.social · 21h

But to come back to focal neural effects of stimulation: without topography in the model I find it hard to see how to explicitly model the interaction of stimulation at multiple sites that might allow to obtain stronger behavioral effects than we observed using single-site stimulation.

1 2

Hannes Mehrer @hannesmehrer.bsky.social · 21h

Defining face-selective units in a non-topo model, increasing their activation level, and projecting that to the latents of a GAN used for image generation probably also results in face-related changes in model percepts. Is that more what you are asking?

1 2

Hannes Mehrer @hannesmehrer.bsky.social · 21h

This allows eg our visualizations, where we stimulate face-selective regions - defined using a standard face-localizer - which then lead to face-related changes to model percepts (Figs 5, 9-15).

1 2

Hannes Mehrer @hannesmehrer.bsky.social · 21h

Thanks, Adrian! The main purpose of using topo models is to allow the implementation of neural activation changes and their propagation across the cortical sheet using the topo model's in-silico equivalent of the cortex.

1 1

Hannes Mehrer @hannesmehrer.bsky.social · 1d

Absolutely agree that neural responses to the perturbations would have been very useful. While that type of data was not recorded in our experiments, the groups of Michael Beyeler @mbeyeler.bsky.social and Eduardo Fernandez just presented some great work in this direction: bsky.app/profile/mbey...

Michael Beyeler @mbeyeler.bsky.social · 13d

👁️🧠 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.

1 4

Hannes Mehrer @hannesmehrer.bsky.social · 1d

Thanks for the question, Konrad! Do you mean what percentage of variance of the neural effects of perturbations our topo models can explain? Would be great if we could investigate that, but we haven't recorded neural data during perturbation trials. Or what did you mean by observational data?

1 1

Hannes Mehrer @hannesmehrer.bsky.social · 2d

@neuroxepfl.bsky.social @icepfl.bsky.social @epfl-ai-center.bsky.social

1

Hannes Mehrer @hannesmehrer.bsky.social · 2d

Special thanks go out to Paolo Papale and Anna Mitola who initiated this collaboration and performed the in-vivo experiments. And to the rest of a great team: Ben Lonnqvist @benlonnqvist.bsky.social, Abdulkadir Gokce @akgokce.bsky.social, Martin Schrimpf @mschrimpf.bsky.social

1 2

Hannes Mehrer @hannesmehrer.bsky.social · 2d

Take-home-message

Proof-of-principle that topographic models can guide stimulation of high-level cortex to bias object-level behavioral choices. A step toward next-generation visual prosthetics allowing more complex visual experience.

2 3

Hannes Mehrer @hannesmehrer.bsky.social · 2d

Model perceptual changes via simulated microstimulation

We visualized perceptual changes from simulated stimulations in model face-selective regions. This results in face-related changes: additional faces appear (#1), face becomes larger (#1161), or specific face-features get enhanced (#533).

1 3

Hannes Mehrer @hannesmehrer.bsky.social · 2d

Experiment 2

With a slightly different site-selection criterion, stimulation shifted behavior above baseline in monkey 1 (Cohen’s d=0.67), though our model was not able to accurately predict monkey behavior anymore.

1 3

Hannes Mehrer @hannesmehrer.bsky.social · 2d

Experiment 1

Model-predicted behavioral shifts correlated with stimulation-evoked behavioral shifts in both monkeys. While predicted model responses were strong, monkey behavior was not shifted above baseline.

1 3

Hannes Mehrer @hannesmehrer.bsky.social · 2d

Visual stimuli via GANs

We generate image sequences that smoothly modulate neural activity along a stimulation site’s tuning dimension. This links visual input to the direction of activation changes resulting from microstimulation (Papale et al. 2024: www.biorxiv.org/content/10.1...)

1 3

Hannes Mehrer @hannesmehrer.bsky.social · 2d

How it works

1. Map the in-silico cortical sheet of a topographic model to the monkey cortex.
2. Optimize stimulation parameters by prototyping experiments in the model.
3. Only test those parameters in-vivo that are predicted to yield the largest behavioral effects.

1 3

Hannes Mehrer @hannesmehrer.bsky.social · 2d

Stimulate high-level vs early visual cortex

Visual prosthetics in early visual areas can evoke simple percepts (letters), but they are limited by 1. electrode count and 2. low-level features. We target high-level cortex to elicit percepts of more complex objects.

1 3

Hannes Mehrer @hannesmehrer.bsky.social · 2d

🧠 New preprint: we show that model-guided microstimulation can steer monkey visual behavior.

Paper: arxiv.org/abs/2510.03684

🧵

1 16 33

Hannes Mehrer @hannesmehrer.bsky.social · 2d

Take-home-message

Proof-of-principle that topographic models can guide stimulation of high-level cortex to bias object-level behavioral choices. A step toward next-generation visual prosthetics allowing ore complex visual experience.

Hannes Mehrer @hannesmehrer.bsky.social · 2d

Model perceptual changes via simulated microstimulation

We visualized perceptual changes from simulated stimulations in model face-selective regions. This results in face-related changes: additional faces appear (#1), face becomes larger (#1161), or specific face-features get enhanced (#533).

Hannes Mehrer @hannesmehrer.bsky.social · 2d

Model perceptual changes via simulated microstimulation

We visualized perceptual changes from simulated stimulations in model face-selective regions. This results in face-related changes: additional faces appear (#1), face becomes larger (#1161), or specific face-features get enhanced (#533).

Hannes Mehrer @hannesmehrer.bsky.social · 2d

Experiment 2

With a slightly different site-selection criterion, stimulation shifted behavior above baseline in monkey 1 (Cohen’s d=0.67), though our model was not able to accurately predict monkey behavior anymore.

1

Hannes Mehrer @hannesmehrer.bsky.social · 2d

Experiment 1

Model-predicted behavioral shifts correlated with stimulation-evoked behavioral shifts in both monkeys. While predicted model responses were strong, monkey behavior was not shifted above baseline.

1

Hannes Mehrer @hannesmehrer.bsky.social · 2d

Visual stimuli via GANs

We generate image sequences that smoothly modulate neural activity along a stimulation site’s tuning dimension. This links visual input to the direction of activation changes resulting from microstimulation (Papale et al. 2024: www.biorxiv.org/content/10.1...)

1

Hannes Mehrer @hannesmehrer.bsky.social · 2d

How it works

1. Map the in-silico cortical sheet of a topographic model to the monkey cortex.
2. Optimize stimulation parameters by prototyping experiments in the model.
3. Only test those parameters in-vivo that are predicted to yield the largest behavioral effects.

1

Hannes Mehrer @hannesmehrer.bsky.social · 2d

Stimulate high-level vs early visual cortex

Visual prosthetics in early visual areas can evoke simple percepts (letters), but they are limited by 1. electrode count and 2. low-level features. We target high-level cortex to elicit pecepts of more complex objects.

1

Hannes Mehrer @hannesmehrer.bsky.social · 7d

Very happy to be part of this project: Melika Honarmand has done a great job of using vision-language-models to predict the behavior of people with dyslexia. A first step toward modeling various disease states using artificial neural networks.

Martin Schrimpf @mschrimpf.bsky.social · 7d

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

1 3

Reposted by Hannes Mehrer

Anita Devineni @anitadevineni.bsky.social · 9d

My department at Emory is a hiring a tenure-track neuroscientist!

Anyone who's talked to me in the last 4 years knows I cannot say enough good things about my dept and the neuroscience community here. My colleagues are so wonderfully supportive. Postdocs, please apply!

apply.interfolio.com/174371

Apply - Interfolio {{$ctrl.$state.data.pageTitle}} - Apply - Interfolio

apply.interfolio.com

1 97 130