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Neural Computation Workshop Artificial Intelligence (2011)

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 							Neural Computation Workshop Artificial Intelligence (2011)
  
  Dartmouth College Lectures (with
subtitles)
  
  Please note: This is very sophisticated stuff - don't leech this if you're not ready for this.
Don't waste the community's bandwidth.
  
  Neural Computation: Population Coding of High-Level
Representations
  
  The Center for Cognitive Neuroscience at Dartmouth and the Neukom Institute hosted the
workshop, "Neural Computation: Population Coding of High-Level Representations". The workshop, August 18 and 19, 2011
consisted of talks by about a dozen speakers with an emphasis on extensive discussion after each talk.
  
 
The theme of the workshop was computational approaches to modeling how complex stimuli are encoded in population
responses and how to decode brain activity to identify the information content that is represented in population
responses. The talks emphasized work in visual neuroscience but were not limited to visual representation.
  

 In addition to the high-profile speakers, each speaker was invited to bring a junior member from their lab –
grad student, post-doc, or junior faculty. A poster session to highlight the work of the junior colleagues was included
as an effective way to enrich discussion and enable one-on-one discussions with senior investigators.
  
 
Videos of the speakers (in alphabetical order):
  
  01: Charles Cadieu, PhD, Redwood Center for Neuroscience,
UC Berkeley
  Learning Intermediate-Level Representations of Form and Motion from Natural Movies
  
  02:
Ed Connor, Prof. of Neuroscience; Director, The Zanvyl Krieger Mind/Brain Institute, Johns Hopkins
  Neural coding
of object structure in the ventral visual pathway
  
  03: Jim DiCarlo, MD, PhD, Assoc. Prof. of Neuroscience;
McGovern Institute for Brain Research, MIT
  Untangling object recognition: Which neuronal population codes can
explain human object recognition performance?
  
  04: (video not available) Jack Gallant, Prof. of Psychology
and Helen Wills Neuroscience Institute; Director, The Gallant Lab, UC Berkeley
  Using voxel-wise encoding models
to discover brain representations and to decode brain activity
  
  04-2: (replacement for missing video 04)
Brain representations of information in natural movies-HD
  Jack Gallant, Helen Wills Neuroscience Institute,
University of California, Berkeley
  Abstract: How is human visual system organized into functional areas, and what
information is represented in each area? The best current technology for exploring these issues is functional MRI, but
thus far fMRI has only provided coarse and patchy information about the functional organization of the human visual
system. We have developed a new approach for collecting and modeling fMRI data that reveals the functional organization
of the human visual system in much greater detail than was possible previously. Our approach is based on estimation of
quantitative voxel-wise encoding models from fMRI responses evoked by natural movies. Projection of these encoding
models onto flattened maps of individual human brains reveals a highly detailed and systematic representation of
structural and semantic information distributed across wide swaths of visual and non-visual cortex. These patterns are
consistent with the coarse parcellations provided by previous techniques, but provide much more detailed information and
extend well beyond areas identified in earlier studies. Furthermore, examination of voxel-wise encoding models reveals
what specific information is represented within each visual area, and suggests how the visual system exploits simple
spatial and temporal features in order to construct semantic representations of objects and scenes. Finally, one
additional benefit of our approach is that estimated encoding models can be easily converted into decoding models. These
decoding models recover both the structural and semantic information in natural movies, even from slow hemodynamic
signals.
  
  05: Jim Haxby, Evans Family Distinguished Professor; Director, Center for Cognitive
Neuroscience; Prof. of Psychological and Brain Sciences, Dartmouth
  Building common, high-dimensional models of
neural representational spaces
  
  06: Nikolaus Kriegeskorte, Faculty, Cognition and Brain Sciences Unit,
University of Cambridge
  Representational similarity analysis of visual-object population codes
  
  07:
Tom Mitchell, Prof. of Computer Science; Chair, Machine Learning Dept., Carnegie Mellon
  What neural activity
encodes about stimuli, where and when
  
  08: Alice O’Toole, Prof. Cognition and Neuroscience, UT
Dallas
  Understanding neural representation of facial identity, race, and viewpoint: Constraining the neural with
the perceptual
  
  09: Tomaso Poggio, Eugene McDermott Professor in the Brain Sciences and Human Behavior;
Director of the Center for Biological and Computational Learning, MIT
  The computational magic of the ventral
stream: Towards a theory
  
  10: Eero Simoncelli , Investigator, Howard Hughes Medical Institute; Prof. of
Neural Science, Mathematics and Psychology, NYU
  Metamers of the ventral stream
  
  text: Encoding and
decoding with voxel-wise models - Gallant.pdf
  
  source:
http://neukom.dartmouth.edu/...s/neuralcomp-workshop11.html
  
  tags: AI, brain, mind, neural, artificial
intelligence, computing, hardware, software, posthuman 						

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