Inception loops discover what excites neurons most using deep predictive models | Nature Neuroscience

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Nature Neuroscience volume 22, pages 2060–2065 (2019)Cite this article 15k Accesses 51 Citations 176 Altmetric Metrics details Finding sensory stimuli that drive neurons optimally is central to understanding information processing in the brain. However, optimizing sensory input is difficult due to the predominantly nonlinear nature of sensory processing and high dimensionality of the input. We developed ‘inception loops’, a closed-loop experimental paradigm combining in vivo recordings from thousands of neurons with in silico nonlinear response modeling. Our end-to-end trained, deep-learning-based model pr

Subjects Network models Neural encoding Neuroscience Striate cortex Visual system Abstract Finding sensory stimuli that drive neurons optimally is central to understanding information processing in the brain. However, optimizing sensory input is difficult due to the predominantly nonlinear nature of sensory processing and high dimensionality of the input. We developed ‘inception loops’, a closed-loop experimental paradigm combining in vivo recordings from thousands of neurons with in silico nonlinear response modeling. Our end-to-end trained, deep-learning-based model predicted thousands of ne

related reading

• Zoom In: An Introduction to Circuitsdistill.pub
• https://io0.github.ioio0.github.io
• The Unbearable Slowness of Beingarxiv.org
• Feature Visualizationdistill.pub
• An Overview of Early Vision in InceptionV1distill.pub
• The sifting of visual information in the superior colliculus | eLifeelifesciences.org
• How the Brain 'Constructs' the Outside World | Scientific Americanscientificamerican.com
• A deep learning framework for neuroscience - PMCncbi.nlm.nih.gov
• Multimodal Neurons in Artificial Neural Networksdistill.pub
• To Be Energy-Efficient, Brains Predict Their Perceptions | Quanta Magazinequantamagazine.org
• High-level visual representations in the human brain are aligned with large language models | Nature Machine Intelligencenature.com
• Predictive coding: a more cognitive process than we thought? - PMCpmc.ncbi.nlm.nih.gov