Title: Large-scale pretraining on neural data allows for transfer across individuals, tasks and species Abstract: As neuroscience datasets grow in size and complexity, integrating diverse data sources to achieve a comprehensive understanding of brain function presents both an opportunity and a challenge. In this talk, I will introduce our approach to developing a multi-source foundation model for…

Speaker: Haozhe Shan Title: A theory of continual learning in deep neural networks: task relations, network architecture and learning procedure Abstract: Imagine listening to this talk and afterwards forgetting everything else you’ve ever learned. This absurd scenario would be commonplace if the brain could not perform continual learning (CL) – acquiring new skills and knowledge without…

Title: Bounded optimality: A cognitive perspective on neural computation with resource limitations

Title: Neuronal energy consumption: basic measures and trade-offs, and their effects on efficiency Zoom: https://columbiauniversity.zoom.us/j/98299154214?pwd=1J3J0lEpF6XdqHkHy02c7LuD6xUWx2.1

Title: Taskonomy: Disentangling Task Transfer Learning Abstract: TBD  Link: http://taskonomy.stanford.edu/taskonomy_CVPR2018.pdf

Title: Meta-learning for more powerful behavioral modeling Abstract: Two modeling paradigms have historically been in tension: Bayesian models provide an elegant way to incorporate prior knowledge, but they make simplifying and constraining assumptions; on the other hand, neural networks provide great modeling flexibility, but they make it difficult to incorporate prior knowledge. Here I describe how to…

Title: A Practitioner’s Guide to Continual Multimodal Pretraining Reading: https://arxiv.org/pdf/2408.1447 Zoom: https://columbiauniversity.zoom.us/j/97176853843?pwd=VLZdh6yqHBcOQhdf816lkN5ByIpIsF.1

Title: Target Learning rather than Backpropagation Explains Learning in the Mammalian Neocortex Abstract: Modern computational neuroscience presents two competing hypotheses for hierarchical learning in the neocortex: (1) deep learning-inspired approximations of the backpropagation algorithm, where neurons adjust synapses to minimize error, and (2) target learning algorithms, where neurons reduce the feedback required to achieve a desired activity.…

Title: Meta Continual Learning Revisited: Implicitly Enhancing Online Hessian Approximation via Variance Reduction Reading: https://openreview.net/pdf?id=TpD2aG1h0D Zoom: https://columbiauniversity.zoom.us/j/97176853843?pwd=VLZdh6yqHBcOQhdf816lkN5ByIpIsF.1

General Agenda October 21st, Day 1 from 8:45am to 5pm Breakfast and Lunch Provided Opening 3 Keynote Speakers from ARNI Faculty Research Brainstorming and Discussions Project/Student Poster Session Education and Broader Impact Discussions October 22nd, Day 2 from 9am to 1pm Breakfast and Lunch Provided 1 Keynote Speaker Brainstorming and Discussion on Collaborations & Knowledge…

Title: Unifying neural population dynamics, manifold geometry, and circuit structure. Abstract: Single neurons show complex, heterogeneous responses during cognitive tasks, often forming low-dimensional manifolds in the population state space. Consequently, it is widely accepted that neural computations arise from low-dimensional population dynamics while attributing functional properties to individual neurons is impossible. I will present recent work from…

Title: Mice wiggle a wheel to boost the salience of low visual contrast stimuli Abstract: From the Welsh tidy mouse to the New York City pizza rat, movement belies rodent intelligence. We show that head-fixed mice develop an active sensing strategy while performing a visual perceptual decision-making task (The International Brain Laboratory, 2021). Akin to humans shaking a computer mouse…