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CCN talk August 15, 2016


Nikolaus Kriegeskorte

MRC Cognition and Brain Sciences Unit, Cambridge, UK

Testing complex brain-computational models to understand how the brain works

Time: 4:00-6:00

Location: Moore Hall, Filene Auditorium


Recent advances in neural network modelling have enabled major strides in computer vision and other artificial intelligence applications. This brain-inspired technology provides the basis for tomorrow's computational neuroscience. Deep convolutional neural nets trained for visual object recognition have internal representational spaces remarkably similar to those of the human and monkey ventral visual pathway. High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans, but a major challenge is to leverage such data to gain insight into the brain's computational mechanisms. We are only beginning to develop statistical inference for adjudicating between alternative brain-computational models (BCMs). I will share first steps with a new method called probabilistic representational similarity analysis (pRSA), which accounts for the distorted reflection of representational spaces in activity measurements that subsample the representation (e.g. by local averaging in fMRI and by sparse sampling in array recordings). We are entering an exciting new era, in which we will be able to build neurobiologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence. 

Inferring brain-computational mechanisms with models of activity measurements. Kriegeskorte N, Diedrichsen J (in press) Philosophical Transactions of the Royal Society B.

Deep neural networks: A new framework for modeling biological vision and brain information processing. Krieseskorte N (2015) Annu. Rev. Vis. Sci. 2015. 1:417-46. http://biorxiv.org.content/birxiv/early/2015/10/26/029876.full.pdf

Deep Supervised, but Not Unsupervised,  Models May Explain IT Cortical Representation. SM Khaligh-Razavi, N Krieseskorte PLoS computational biology 10 (11), e1003915