Computational Principles of Sensorimotor Control (Lecture 1) by Daniel Wolpert скачать в хорошем качестве

Computational Principles of Sensorimotor Control (Lecture 1) by Daniel Wolpert

PROGRAM ICTP-ICTS WINTER SCHOOL ON QUANTITATIVE SYSTEMS BIOLOGY (ONLINE) ORGANIZERS: Vijaykumar Krishnamurthy (ICTS-TIFR, India), Venkatesh N. Murthy (Harvard University, USA), Sharad Ramanathan (Harvard University, USA), Sanjay Sane (NCBS-TIFR, India) and Vatsala Thirumalai (NCBS-TIFR, India) DATE: 06 December 2021 to 17 December 2021 VENUE: Online How organisms sense the world and generate behaviors is an exciting question that has motivated neuroscientists over more than a century. Neural command for generating behavioral output arises from operations at multiple scales, ranging from the flip-flops of ion channels to dynamics in circuits comprising ensembles of neurons. New tools to genetically manipulate organisms, monitor and perturb neural activity, and advanced microscopy that enables large scale imaging of neurons in vivo have yielded a hitherto unprecedented quantum of data with high resolution. Quantitative approaches are needed to mine these data sets for generating testable hypotheses regarding nervous system function. This is the tenth school in the series on Quantitative Systems Biology, held alternately at Trieste and Bangalore. The school responds to the strong need, especially at the Ph.D. and postdoc level, for providing scientists with a broad exposure to quantitative problems in the study of living systems. The audience will range from Ph.D. students to young faculty, who either work in this area or plan to do so. QSB2021 is focussed on Sensori-motor control. The aim of this School is to expose students from different backgrounds to the latest research in systems neuroscience, with an emphasis on the usage of quantitative methods and theory. We will begin the workshop with a brief introduction to neuroscience, including electrical properties of neuronal membranes and single neuronal biophysics. With this foundation in place, we will delve into how circuit dynamics emerge in diverse circuits using invertebrate and vertebrate model organisms as examples. We will cover questions in population coding, variability and stochasticity, and plasticity. We will then introduce students to applications of quantitative tools to neuroscience data sets such as whole-brain imaging data sets or behavioral clustering data sets. Throughout, we will also explore how theory can contribute to a normative understanding of various phenomena, and motivate future experiments. Scientists and students from all over the world can apply for the School. Researchers from developing countries are particularly encouraged to apply. As the program will be conducted in English, participants should have an adequate working knowledge of this language. There is no registration fee. SCIENTIFIC ORGANISING COMMITTEE 1. Venkatesh N. Murthy (Harvard University, USA) 2. Sharad Ramanathan (Harvard University, USA) 3. Sanjay P. Sane (NCBS, India) 4. Vatsala Thirumalai (NCBS, India) SCIENTIFIC ADVISORY COMMITTEE 1. Vijay Balasubramaniam (University of Pennsylvania, USA) 2. Upinder Bhalla (NCBS, India) 3. Antonio Celani (ICTP, Italy) 4. Sanjay Jain (University of Delhi, India) 5. Vijaykumar Krishnamurthy (ICTS, India) (Local organizer) 6. Matteo Marsili (ICTP, Italy) 7. Mukund Thattai (NCBS, India) Previous editions of this school 2020 2019 2018 2017 2016 2015 2014 2013 2012 CONTACT US: [email protected] PROGRAM LINK: https://www.icts.res.in/program/qsb2021 Table of Contents (powered by https://videoken.com) 0:04:19 Complexity of human movement control 0:05:35 Modest success in robotics: Manipulation 0:06:48 Normative approach to human movement control 0:09:23 Reverse-engineering sensorimotor control 0:10:23 Motor planning 0:12:12 Arm movements: Paths 0:13:30 Eye movements: saccades 0:14:39 Models 0:15:22 The Assumption of Optimality 0:17:52 The ideal cost for goal-directed movement 0:19:37 Motor noise is signal-dependent 0:20:59 Signal-dependent noise and optimal control 0:22:56 Pointing movements: (minimize variability) 0:28:15 Motor control in the late 0:29:04 The demise of the desired trajectory 0:31:13 Motor control in the early 0:32:10 Optimal Feedback Control (Todorov, Kappen) 0:34:34 Optimal control and planning 0:37:31 State estimation Interpreting the uncertain state of the world 0:38:03 Generative model of state evolution 0:38:49 Kalman filter is the Bayesian estimator 0:41:33 Motor prediction with forward model 0:43:05 How is eye position estimated 0:46:42 Motor prediction 0:47:56 Types of (Kalman) estimation problems 0:48:56 Minimizing delays 0:55:33 Types of Motor Learning 0:57:07 Representations in motor learning 0:58:27 Mechanistic models 0:59:01 Normative models 0:59:43 Impedance 1:00:23 Measuring stiffness 1:01:40 Controlling stiffness 1:03:55 Bayesian Decision Theory 1:04:46 Sensorimotor learning and Bayes rule 1:06:11 Loss Functions in movement 1:07:26 Virtual pea shooter 1:08:06 Predictions 1:09:25 Loss function is robust to outliers 1:10:56 Imposed loss function 1:12:47 Summary