AI 精选动态
智能评分 70
让猴子视觉神经元用人类语言描述图像
AI 推荐理由
该方法首次实现从猴子视觉神经元到人类语言的可验证双向映射，值得关注其在脑机接口和AI解释性中的应用。核心解读
Surya Ganguli团队展示了一种方法，通过构建猴子视觉区域的数字孪生、进行脑内实验、使用视觉语言模型描述图像，并利用语言条件扩散模型验证，首次实现从猴子视觉神经元到人类语言的双向映射，并可从语言生成无限图像来刺激特定神经元。
全文
(1) What
> **引用原帖 Surya Ganguli (@SuryaGanguli):**
> Our new paper lead by @vedanglad w/@AToliasLab "Letting the neural code speak..." https://t.co/B6H1Vvcmso
> We show how to get *monkey* visual neurons to TELL us in *human* language what images make them fire. We do this is an automated verifiable way at scale! How?
> 1) Build a digital twin of monkey visual areas that can accurately map visual inputs to neural activity.
> 2) Perform in-silico experiments on this twin to find many complex images that make a model neuron fire.
> 3) Use a vision-language model to describe these complex images.
> 4) Verification: use a language-conditioned diffusion model to generate new images, and check they make the monkey digital twin neurons fire a lot.
> To our knowledge, for the first time, we have a way to convert monkey vision to human language, and from *human* language to sample infinitely many images that make any given *monkey* visual neuron fire, all in an algorithmic fashion.
> For more exciting details, see @vedanglad's excellent thread!
> https://x.com/SuryaGanguli/status/2066963477445689678