Bayesian Statistics
bayesian statistics
Dr. Tatsuo Okubo
We are a new group at the Chinese Institute for Brain Research (CIBR), Beijing, which focuses on using modern data science and machine learning tools on neuroscience data. We collaborate with various labs within CIBR to develop models and analysis pipelines to accelerate neuroscience research. We are looking for enthusiastic and talented machine learning engineers and data scientists to join this effort.
Gatsby Computational Neuroscience Unit
4-Year PhD Programme in Theoretical Neuroscience and Machine Learning Call for Applications! Deadline: 13 November 2022 The Gatsby Computational Neuroscience Unit is a leading research centre focused on theoretical neuroscience and machine learning. We study (un)supervised and reinforcement learning; inference, coding and neural dynamics; Bayesian and kernel methods; deep learning; with applications to the analysis of perceptual processing and cognition, neural data, signal and image processing, machine vision, network data and nonparametric hypothesis testing. The unit provides a unique opportunity for a critical mass of theoreticians to interact closely with one another and with researchers at the Sainsbury Wellcome Centre for Neural Circuits and Behaviour (SWC), the Centre for Computational Statistics and Machine Learning (CSML) and related UCL departments such as Computer Science; Statistical Science; Artificial Intelligence; the ELLIS Unit at UCL; Neuroscience; and the nearby Alan Turing and Francis Crick Institutes. Our PhD programme provides a rigorous preparation for a research career. Students complete a 4-year PhD in either machine learning or theoretical and computational neuroscience, with minor emphasis in the complementary field. Courses in the first year provide a comprehensive introduction to both fields and systems neuroscience. Students are encouraged to work and interact closely with SWC/CSML researchers to take advantage of this uniquely multidisciplinary research environment. Full funding is available regardless of nationality. The unit also welcomes applicants who have secured or are seeking funding from other sources. To apply, please visit www.ucl.ac.uk/gatsby/study-and-work/phd-programme
Mice alternate between discrete strategies during perceptual decision-making
Classical models of perceptual decision-making assume that animals use a single, consistent strategy to integrate sensory evidence and form decisions during an experiment. In this talk, I aim to convince you that this common view is incorrect. I will show results from applying a latent variable framework, the “GLM-HMM”, to hundreds of thousands of trials of mouse choice data. Our analysis reveals that mice don’t lapse. Instead, mice switch back and forth between engaged and disengaged behavior within a single session, and each mode of behavior lasts tens to hundreds of trials.