Recorded:
Jun 5, 2014
Uploaded:
June 5, 2014
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2014
CBMM Speaker(s):
Nancy Kanwisher
Topics: Is the functional organization of the brain based on special-purpose vs. general-purpose machinery? Brief history of efforts to find specialized machinery (Spearman, Gall, lesion studies); introduction to fMRI methods and data; validation of...
Recorded:
Jun 5, 2014
Uploaded:
June 5, 2014
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2014
CBMM Speaker(s):
Ben Deen
Topics: Motivation for multivoxel pattern analysis (MVPA); correlation based classification analysis; results of analysis of EBA and pSTS cortical regions: EBA patterns carry information about body pose that is invariant to body motion kinematics,...
Recorded:
Jun 4, 2014
Uploaded:
June 4, 2014
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2014
CBMM Speaker(s):
Laura Schulz
Topics: Historical perspective on underestimating the challenge of commonsense intelligence in AI; studying children may provide key insights; early representations of objects (e.g. object permanence, Spelke objects, expectations of object behavior...
Recorded:
Jun 4, 2014
Uploaded:
June 4, 2014
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2014
CBMM Speaker(s):
James DiCarlo
Topics: Decoding of IT signals for object classification (Poggio, DiCarlo, Science 2009); 3D object models; detection experiments with objects of different pose placed on random background images; neural population state space; LaWS of RAD IT...
Recorded:
Jun 4, 2014
Uploaded:
June 4, 2014
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2014
CBMM Speaker(s):
James DiCarlo
Topics: Why study object recognition in the brain; comparison of behavior in humans and monkeys; overview of the ventral visual stream and the ventral (what) vs. dorsal (where) pathways; retinal receptive fields; simple and complex cells in V1;...
Recorded:
Jun 2, 2014
Uploaded:
June 2, 2014
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2014
CBMM Speaker(s):
Emily Mackevicius
Topics: Marr levels of analysis, types of learning (unsupervised, supervised, reinforcement), Hebb rule, LTP, correlation and covariance based learning, reinforcement learning, classical conditioning, conditioning paradigms, credit assignment...
Recorded:
Jun 2, 2014
Uploaded:
June 2, 2014
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2014
CBMM Speaker(s):
Gabriel Kreiman
Topics: General features of brain-based computations, brain anatomy, structure of neurons, equivalent electrical circuit, synapses, single neuron models at multiple resolutions, integrate-and-fire model, Hodgkin-Huxley model; empirical methods used...
Recorded:
Jun 2, 2014
Uploaded:
June 2, 2014
Part of
Brains, Minds and Machines Summer Course 2014
CBMM Speaker(s):
Jed Singer
Topics: Characterizing neural firing rates, tuning curves, identifying effective stimuli, modeling spike trains, integrating information across time and across neurons, estimating response using reverse correlation, decoding fundamentals, two-way...
Recorded:
May 31, 2014
Uploaded:
May 31, 2014
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2014
CBMM Speaker(s):
Samuel Gershman, Joshua Tenenbaum
Topics: (Sam Gershman) Application of Bayesian learning to motion perception; automatic structure learning
(Joshua Tenenbaum) Learning to learn: hierarchical Bayes; empirical studies of word learning and the relevant object features; transfer...
(Joshua Tenenbaum) Learning to learn: hierarchical Bayes; empirical studies of word learning and the relevant object features; transfer...
Recorded:
May 31, 2014
Uploaded:
May 31, 2014
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2014
CBMM Speaker(s):
Samuel Gershman
Topics: Basic introduction to parameter learning, structure learning, nonparametric Bayes, mixture models, conditioning as clustering, learning relational concepts, multi-level category learning, latent feature models, function learning, Gaussian...