Recorded:
Jan 19, 2016
Uploaded:
January 19, 2016
Part of
Computational Tutorials
CBMM Speaker(s):
Emily Mackevicius
Speaker(s):
Ciccarelli
Taught by: Emily Mackevicius and Greg Ciccarelli
Introduction to dimensionality reduction and the methods of Principal Components Analysis and Singular Value Decomposition. Dimensionality Reduction I tutorial from the tutorial series in...
Uploaded:
January 12, 2016
Part of
Computational Tutorials
Cluster computing and OpenMind tutorial from the tutorial series in computational topics for brain and cognitive sciences. Lecture slides, references, exercises, and sign-ups for future tutorials are posted here: https://stellar.mit.edu/S/project/...
Uploaded:
January 12, 2016
Part of
Computational Tutorials
Cluster computing and OpenMind tutorial from the tutorial series in computational topics for brain and cognitive sciences. Lecture slides, references, exercises, and sign-ups for future tutorials are posted here: https://stellar.mit.edu/S/project/...
Uploaded:
December 16, 2015
Scientists have long wondered if the human brain contains neural mechanisms specific to music perception. Now, for the first time, MIT neuroscientists have identified a neural population in the human auditory cortex that responds selectively to...
Uploaded:
December 10, 2015
Part of
NIPS 2015 Symposium: Brains, Minds and Machines
Panel discussion
Uploaded:
December 10, 2015
Part of
NIPS 2015 Symposium: Brains, Minds and Machines
Neuroscience and the Quest for AI
How systems neuroscience can help in the quest for Artificial General Intelligence
Uploaded:
December 10, 2015
Part of
NIPS 2015 Symposium: Brains, Minds and Machines
Towards Glimpses of a New Science of Brains, Minds and Machines:
Weaving Together Physics, Computer Science, and Neurobiology Our neural circuits exploit the laws of physics to perform computations in ways that are fundamentally different from...
Weaving Together Physics, Computer Science, and Neurobiology Our neural circuits exploit the laws of physics to perform computations in ways that are fundamentally different from...
Uploaded:
December 10, 2015
Part of
NIPS 2015 Symposium: Brains, Minds and Machines
Hallmarks of Deep Learning in the Brain
Anatomically, the brain is deep. To understand the ramifications of depth on learning in the brain requires a clear theory of deep learning. I develop the theory of gradient descent learning in deep linear...
Uploaded:
December 10, 2015
Part of
NIPS 2015 Symposium: Brains, Minds and Machines
The Roles of Recurrent and Feedback Computations in Cortex
There are abundant recurrent connections throughout the brain, yet their functional roles remain poorly understood and these connections are notoriously absent in the successful body of work...
Uploaded:
December 10, 2015
Part of
NIPS 2015 Symposium: Brains, Minds and Machines
The Neuroscience of Intelligence
Yesterday’s scientific research, starting with Hubel and Wiesel’s Nobel-prize winning work on the circuitry underlying visual processing in cortex, gave rise to today’s deep machine learning networks. Likewise, today...