Recorded:
May 31, 2017
Uploaded:
August 8, 2017
Part of
CBMM Research
CBMM Speaker(s):
Kim Scott
Wondering what's going on in there as your newborn stares at your hairline or your toddler tries to feed her stuffed animal? Families can now participate from home in studies about how kids learn by logging on to MIT's Lookit platform (https://...
Recorded:
May 31, 2017
Uploaded:
August 8, 2017
Part of
CBMM Research
CBMM Speaker(s):
Kim Scott
Researchers at MIT are working to make conducting and participating in developmental science more accessible by putting studies online. In this video, founder Kim Scott discusses the advantages and challenges of online research with kids, and some...
Recorded:
Jul 11, 2017
Uploaded:
July 26, 2017
Part of
fMRI Bootcamp
CBMM Speaker(s):
Rebecca Saxe
Rebecca Saxe - MIT
Overview of the time course of the fMRI signal and its underlying physical basis: the hemodynamic response function (HRF), blood oxygen-level dependent (BOLD) signal, and relationship between the BOLD signal and the time course of...
Recorded:
Jul 11, 2017
Uploaded:
July 26, 2017
Part of
fMRI Bootcamp
CBMM Speaker(s):
Rebecca Saxe
Rebecca Saxe - MIT
Introduction to the basics of anatomical and functional MRI.
Recorded:
Jul 26, 2017
Uploaded:
July 26, 2017
Part of
All Captioned Videos, CBMM Summer Lecture Series
CBMM Speaker(s):
Samuel Gershman
Sam Gershman, Professor of Psychology at Harvard, discusses how we might build machines that learn and think like people, by combining insights from cognitive science, artificial intelligence, and computational neuroscience. Dr. Gershman elaborates...
Recorded:
Jul 12, 2017
Uploaded:
July 17, 2017
Part of
Computational Tutorials
Speaker(s):
Pengcheng Zhou
[recording cut short due to technical issues]
In vivo calcium imaging through microendoscopic lenses enables imaging of previously inaccessible neuronal populations deep in the brains of freely moving animals. It is computationally challenging to...
Recorded:
Aug 26, 2016
Uploaded:
July 5, 2017
Part of
Brains, Minds and Machines Summer Course 2016
CBMM Speaker(s):
Nancy Kanwisher
Speaker(s):
James Haxby
Nancy Kanwisher, MIT & James Haxby, Dartmouth College
This debate highlights different perspectives on functional specificity in the human brain, addressing how studies from neuroscience and cognition are used to study the nature of neural...
This debate highlights different perspectives on functional specificity in the human brain, addressing how studies from neuroscience and cognition are used to study the nature of neural...
Recorded:
Aug 29, 2016
Uploaded:
July 5, 2017
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2016
CBMM Speaker(s):
Max Tegmark
Max Tegmark, MIT
The study of deep learning lies at the intersection between AI and machine learning, physics, and neuroscience. Exploring connections between physics and deep learning can yield important insights about the theory and behavior...
The study of deep learning lies at the intersection between AI and machine learning, physics, and neuroscience. Exploring connections between physics and deep learning can yield important insights about the theory and behavior...
Recorded:
Aug 26, 2016
Uploaded:
July 5, 2017
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2016
Speaker(s):
James Haxby
James Haxby, Dartmouth College
There is substantial evidence that processing in ventral temporal cortex is optimized for face and body perception. Recent research employing a greater range of static and dynamic stimuli highlight the important...
There is substantial evidence that processing in ventral temporal cortex is optimized for face and body perception. Recent research employing a greater range of static and dynamic stimuli highlight the important...
Recorded:
Aug 26, 2016
Uploaded:
July 5, 2017
Part of
Brains, Minds and Machines Summer Course 2016
CBMM Speaker(s):
Alan L. Yuille
Alan Yuille, Johns Hopkins University
Introduction to deep networks for visual tasks such as object detection and semantic segmentation; complex models that integrate deep networks with representations of parts and spatial relations, grammars...
Introduction to deep networks for visual tasks such as object detection and semantic segmentation; complex models that integrate deep networks with representations of parts and spatial relations, grammars...