Title: "What Bayes can and cannot tell us about the neuroscience of vision" Nikolaus Kriegeskorte's Group is hosting Dr.Richard Lange, Assistant Professor in the Department of Computer Science at Rochester Institute of Technology. He will be giving a talk at Zuckerman Institute.

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 new perspective by looking at learning algorithms (LAs) as memory mechanisms with their own decision making problem. I will present a natural solution to CL…

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 perturbation leads to behavioural adaptation that counteracts its effects. Primary motor cortex (M1) is intimately involved in both processes, integrating inputs from various sensorimotor brain…

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 corollary the first complete wiring diagram of a visual system, which has been used to identify all 200+ cell types that are intrinsic to the…

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 organisms. For example, we cannot see every feature of the light field that reaches our eyes; temporal resolution is limited by transmission noise and delays,…

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 relying on in-context learning for most online adaptation. It is crucial to equip foundation models with lifelong and human-like learning abilities to enable more flexible…

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. If you are interested in learning more about ARNI over-all, join this Zoom link from 9am to 12pm or 2pm to 4:30pm.

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