Task-relevant information

Neural oscillations are often proposed to support brain computation by routing information, organizing cell assemblies, or shaping coding dynamics. Yet these ideas usually assume rhythms that are strong, sustained, and regular, whereas in vivo oscillations are often weak, transient, noisy, and variable in frequency and phase. In this talk, I will argue that such “no-metronome” oscillations are not just noisy fluctuations, but coordinated complex dynamics with functional consequences. Combining analyses of neural activity recordings during actual behavior (mice and non-human-primate LFPs and hu

Integrating task-relevant information into neural representations is essential for both biological and artificial intelligence. However, current experimental limitations in tracking synaptic weight changes make it challenging to understand learning mechanisms in animals. To address this, we propose a framework based on neural manifold geometry to study representation learning directly from neural activity. A neural manifold represents the collection of population responses to the same task condition (e.g., a cat manifold for responses to images of cats) and can be estimated from recorded activ

Visual working memory (VWM) allows us to maintain a small amount of task-relevant information in mind so that we can use them to guide our behavior. Although past studies have successfully characterized its capacity limit and representational quality during maintenance, the consequence of its usage for task-relevant behaviors has been largely unknown. In this talk, I will demonstrate that VWM representations get distorted when they are used for perceptual comparisons with new visual inputs, especially when the inputs are subjectively similar to the VWM representations. Furthermore, I will show