Understanding Information: From Bits to Brains
Information is the currency of the modern era, and there are surprising similarities in data processing and representation between computer systems and neuroscience. In the first half of this talk, I will discuss how to dynamically identify related blocks or files in trace data and use the resulting data groups to make information storage more efficient and robust. From there, I will discuss how the classical systems metrics of reliability, performance, and availability apply to biologically plausible neural networks, including recent work exploring the balance between classification accuracy and robustness. Finally, I will show how computational models from vision can be applied to understand information flow in the visual cortex, and how algebraic topology is a promising method to classify neurons by network function and categorize visual stimuli.
Avani Wildani is a computer scientist with an obsession for information storage and retrieval in any medium she can get her hands into. She has worked in access prediction, data deduplication, power management, catastrophic fault tolerance, P2P networks, and recently in topological data analysis. She also dabbles in machine learning models, particularly those that incorporate domain-specific expertise. She is currently a Pioneer postdoctoral fellow at the Salk Institute, studying the neurobiology of vision in the primary visual cortex from an algorithmic perspective. She plans to continue as a researcher in computer science searching for the underlying data structures of the brain.