Session: C. Neuroimaging II
Will talk about: Deep neural networks: a new framework for modelling brain information processing
Nikolaus Kriegeskorte is a brain scientist who studies visual object recognition using computational models and measurements of brain activity. He is a Programme Leader at the Medical Research Council's Cognition and Brain Sciences Unit in Cambridge, UK. He did his thesis research at Maastricht University, and worked as a postdoctoral fellow at the University of Minnesota and at the US National Institute of Mental Health.
Recent advances in neural network modelling have enabled major strides in computer vision and other artificial intelligence applications. Artificial neural networks are inspired by the brain and their computations could be implemented in biological neurons. Although designed with engineering goals, this technology provides the basis for tomorrow’s computational neuroscience, engaging complex cognitive tasks and high-level cortical representations. I will describe a framework for testing such models with massively multivariate brain-activity data. In order to compare representations between brains and models, we characterise the representational spaces by matrices of representational dissimilarities among stimuli. This approach enables us to summarise population codes and test complex computational models as theories of brain computation. 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. Modern neural net technology puts an expanding array of complex cognitive tasks within our computational reach. 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.
Deep neural networks: a new framework for modelling biological vision and brain information processing
Kriegeskorte (2015) Annual Reviews of Vision Science
Deep supervised, but not unsupervised, models may explain IT cortical representation
Khaligh-Razavi SM, Kriegeskorte N (2014) PLoS Comput Biol10(11):e1003915
Representational geometry: integrating cognition, computation, and the brain
Kriegeskorte N, Kievit RA (2013) Trends Cogn Sci 17(8):401-12
Visual Population Codes – Toward a Common Multivariate Framework for Cell Recording and Functional Imaging
Kriegeskorte N, Kreiman G (2011) Edited book. MIT Press.
Matching categorical object representations in inferior temporal cortex of man and monkey
Kriegeskorte N, Mur M, Ruff DA, Kiani R, Bodurka J, Esteky H, Tanaka K, Bandettini PA (2008) Neuron 60(6):1126-41.