Title: Continual learning, machine self-reference, and the problem of problem-awareness Abstract: Continual learning (CL) without forgetting has been a long-standing problem in machine learning with neural networks. Here I will bring a…

Title: Feedback-based motor control can guide plasticity and drive rapid learning Abstract: Animals use afferent feedback to rapidly correct ongoing movements in the presence of a perturbation. Repeated exposure to a predictable…

Title:  Insights into vision from interpreting a neuronal wiring diagram Host: Marcus Triplett Abstract:  In 2023, the FlyWire Consortium released the neuronal wiring diagram of an adult fly brain. This contains as a…

Title: How behavioral and evolutionary constraints sculpt early visual processing Abstract: Biological systems must selectively encode partial information about the environment, as dictated by the capacity constraints at work in all living…

Speaker: Mengye Ren Title: Lifelong and Human-like Learning in Foundation Models Abstract: Real-world agents, including humans, learn from online, lifelong experiences. However, today’s foundation models primarily acquire knowledge through offline, iid learning, while…

NSF Site Visit - The NSF team will evaluate the progress and achievements of ARNI’s projects to date and provide recommendations to steer future directions and funding for the project.…

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…

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…

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…