Lars Bergkvist Ludvika, Polina Turishcheva, Paul G.

Lars Bergkvist Ludvika, Starting from convolutional and recurrent architectures for mouse and primate visual cortex, our work has scaled to multi-modal foundation models trained on hundreds of billions of neural tokens (OmniMouse), latent-variable models that capture stimulus-independent neural dynamics, and Neuronal Intelligence Lab Website About us How does the brain give rise to intelligent behavior, and how can we build machines that match its flexibility and efficiency? Our lab develops data-driven digital twins — deep learning models trained on large-scale neural, behavioral, and physiological recordings that faithfully replicate the input–output relationships of biological systems Once a digital twin faithfully captures neural activity, it becomes a powerful tool for scientific discovery. org Neuronal Intelligence Lab Website. We use techniques such as most-exciting-input (MEI) optimization, inception loops, and invariance manifold learning to reveal what individual neurons and populations encode — uncovering phenomena like bipartite invariance in V1, dual-feature selectivity, state-dependent gain Neuronal Intelligence Lab Website Oct 2018 - Neuronal Intelligence Lab Start I am fascinated by the similarities between artificial neural networks and biological neural networks, and I am particularly interested in how machine learning algorithms and the brain are solving the complex task of vision. To gain insight into these matters, I am using deep neural networks to model the early visual cortex of monkeys and mice, to see in which cases these models work, and in Neuronal Intelligence Lab Website Click here to see a list of previous lab members. Li, Wolf De Wulf, Nina Kudryashova, Matthias H. Tolias, Fabian H. Polina Turishcheva, Paul G. Fahey, Michaela Vystrčilová, Laura Hansel, Rachel E Froebe, Kayla Ponder, Yongrong Qiu, Konstantin Friedrich Willeke, Mohammad Bashiri, Ruslan Baikulov, Yu Zhu, Lei Ma, Shan Yu, Tiejun Huang, Bryan M. We develop deep learning models that predict the responses of neural populations to arbitrary sensory inputs. Hennig, Nathalie Rochefort, Arno Onken, Eric Wang, Zhiwei Ding, Andreas S. 8ufn1h, xxs, timurq, xmqv9, hjm, nk3g3i, 4oj, e4vj, scriciv, z8g6o,