Recorded:
Aug 16, 2018
Uploaded:
August 17, 2018
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2018
Speaker(s):
Jeremy M. Wolfe
Jeremy M. Wolfe, Brigham & Women's Hospital; Harvard Medical School
Introduction to visual search that examines Treisman’s Feature Integration Theory, features that guide shifts of attention during search, challenges for model development...
Introduction to visual search that examines Treisman’s Feature Integration Theory, features that guide shifts of attention during search, challenges for model development...
Recorded:
Aug 16, 2018
Uploaded:
August 16, 2018
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2018
CBMM Speaker(s):
Joshua Tenenbaum
Josh Tenenbaum, MIT
In this third lecture, Josh Tenenbaum first provides an overview of recent efforts to formulate neurally plausible models for face recognition, intuitive physics, and psychology, that integrate the probabilistic programming...
In this third lecture, Josh Tenenbaum first provides an overview of recent efforts to formulate neurally plausible models for face recognition, intuitive physics, and psychology, that integrate the probabilistic programming...
Recorded:
Aug 16, 2018
Uploaded:
August 16, 2018
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2018
CBMM Speaker(s):
Joshua Tenenbaum
Josh Tenenbaum, MIT
In this second lecture, Josh Tenenbaum first elaborates on an intuitive physics engine that uses a probabilistic framework combined with inverse graphics, to capture aspects of human understanding of the physical behavior...
In this second lecture, Josh Tenenbaum first elaborates on an intuitive physics engine that uses a probabilistic framework combined with inverse graphics, to capture aspects of human understanding of the physical behavior...
Recorded:
Aug 16, 2018
Uploaded:
August 16, 2018
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2018
CBMM Speaker(s):
Joshua Tenenbaum
Josh Tenenbaum, MIT
Past work on human intelligence has framed the underlying processes as pattern recognition engines, as manifested in deep convolutional neural networks; prediction engines, as captured in Bayesian networks, causal models,...
Past work on human intelligence has framed the underlying processes as pattern recognition engines, as manifested in deep convolutional neural networks; prediction engines, as captured in Bayesian networks, causal models,...
Recorded:
Aug 15, 2018
Uploaded:
August 15, 2018
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2018
Speaker(s):
Kohitij Kar
Kohitij Kar, MIT
Introduction to common psychophysical methods, including magnitude estimation, matching, detection and discrimination, the two-alternative forced choice paradigm, psychometric curves and signal detection theory, and using...
Introduction to common psychophysical methods, including magnitude estimation, matching, detection and discrimination, the two-alternative forced choice paradigm, psychometric curves and signal detection theory, and using...
Recorded:
Aug 15, 2018
Uploaded:
August 15, 2018
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2018
CBMM Speaker(s):
Kevin Smith
Kevin Smith, MIT
Probabilistic programming languages facilitate the implementation of generative models of the physical and social worlds that enable probabilistic inference about objects, agents, and events. This tutorial introduces WebPPL...
Probabilistic programming languages facilitate the implementation of generative models of the physical and social worlds that enable probabilistic inference about objects, agents, and events. This tutorial introduces WebPPL...
Recorded:
Aug 15, 2018
Uploaded:
August 15, 2018
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2018
CBMM Speaker(s):
Yen-Ling Kuo, Xavier Boix
Xavier Boix & Yen-Ling Kuo, MIT
Introduction to reinforcement learning, its relation to supervised learning, and value-, policy-, and model-based reinforcement learning methods. Hands-on exploration of the Deep Q-Network and its...
Introduction to reinforcement learning, its relation to supervised learning, and value-, policy-, and model-based reinforcement learning methods. Hands-on exploration of the Deep Q-Network and its...
Recorded:
Aug 15, 2018
Uploaded:
August 15, 2018
Part of
Publication Releases
CBMM Speaker(s):
Gabriel Kreiman, Martin Schrimpf
Prof. Gabriel Kreiman (Boston Children's Hospital, Harvard Medical School) and graduate student Martin Schrimpf (now a PhD candidate in the Brain and Cognitive Sciences Department at MIT) describe some of the key components to their latest paper...
Recorded:
Aug 12, 2018
Uploaded:
August 14, 2018
Part of
Brains, Minds and Machines Summer Course 2018
Speaker(s):
Nikos K. Logothetis
Nikos K. Logothetis, Max Planck Institute for Biological Cybernetics in Tübingen
Overview of the integration of concurrent physiological multi-site recordings with fMRI imaging, and it application to the study of dynamic connectivity related...
Overview of the integration of concurrent physiological multi-site recordings with fMRI imaging, and it application to the study of dynamic connectivity related...
Recorded:
Aug 13, 2018
Uploaded:
August 13, 2018
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2018
CBMM Speaker(s):
Ethan Meyers
Ethan Meyers, Hampshire College/MIT
Introduction to neural decoding methods to study the neural representations of sensory information in the brain to support recognition, their modulation by task-relevant information from top-down attention,...
Introduction to neural decoding methods to study the neural representations of sensory information in the brain to support recognition, their modulation by task-relevant information from top-down attention,...