Embedded thumbnail for Jim DiCarlo: Introduction to the Visual System, Part 1
Jim DiCarlo: Introduction to the Visual System, Part 1
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;...
Embedded thumbnail for Emily Mackevicius: Learning from a Computational Neuroscience Perspective
Emily Mackevicius: Learning from a Computational Neuroscience Perspective
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...
Embedded thumbnail for Gabriel Kreiman: Neurons and Models
Gabriel Kreiman: Neurons and Models
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...
Embedded thumbnail for Jed Singer: Neural Coding
Jed Singer: Neural Coding
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...
Embedded thumbnail for Sam Gershman (continuation of previous talk), and Josh Tenenbaum: Bayesian Inference
Sam Gershman (continuation of previous talk), and Josh Tenenbaum: Bayesian Inference
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...
Embedded thumbnail for Sam Gershman: Structure Learning, Clusters, Features, and Functions
Sam Gershman: Structure Learning, Clusters, Features, and Functions
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...
Embedded thumbnail for Lorenzo Rosasco: Learning Theory, Part 1 (local methods, bias-variance, cross validation) and Part 2 (regularization: linear least squares, kernel least squares)
Lorenzo Rosasco: Learning Theory, Part 1 (local methods, bias-variance, cross validation) and Part 2 (regularization: linear least squares, kernel least squares)
Recorded:
May 30, 2014
Uploaded:
May 30, 2014
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2014
CBMM Speaker(s):
Lorenzo Rosasco
Topics: Supervised learning, nearest neighbor methods and overfitting, k-nearest neighbors - choosing k, bias-variance tradeoff, cross validation, regularization, least squares, linear systems, computational complexity, kernel least squares using...
Embedded thumbnail for Lorenzo Rosasco: Learning Theory, Part 3 (variable selection (OMP), dimensionality reduction (PCA))
Lorenzo Rosasco: Learning Theory, Part 3 (variable selection (OMP), dimensionality reduction (PCA))
Recorded:
May 30, 2014
Uploaded:
May 30, 2014
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2014
CBMM Speaker(s):
Lorenzo Rosasco
Topics: Determining which variables are important for prediction (e.g. given n patients and p genes, which genes are most important for prediction), sparsity (only some coefficients are non-zero), brute force, greedy approaches/matching pursuit,...
Embedded thumbnail for Lorenzo Rosasco: Learning Theory, MATLAB practical session
Lorenzo Rosasco: Learning Theory, MATLAB practical session
Recorded:
May 30, 2014
Uploaded:
May 30, 2014
Part of
Brains, Minds and Machines Summer Course 2014
CBMM Speaker(s):
Lorenzo Rosasco
Embedded thumbnail for Tomaso Poggio, Introduction, Summer Course on Brains, Minds and Machines
Tomaso Poggio, Introduction, Summer Course on Brains, Minds and Machines
Recorded:
May 29, 2014
Uploaded:
May 29, 2014
Part of
All Captioned Videos, Brains, Minds and Machines Summer Course 2014
CBMM Speaker(s):
Tomaso Poggio
Topics: Introduction to the Center for Brains, Minds and Machines; aims of the summer course; understanding intelligence - how the brain and mind perform intelligent computations and how to make smarter machines - requires the integration of methods...

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