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

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. Functional imaging and invasive neuronal recording provide increasingly rich measurements of brain activity in humans and animals, 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. I will share first steps with a new method called probabilistic representational similarity analysis, 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 build and test feedforward and recurrent neural network models of how biological brains perform high-level feats of intelligence.

Inferring brain-computational mechanisms with models of activity measurements
Kriegeskorte N, Diedrichsen J (2016) Philosophical Transactions of the Royal Society B.

Deep neural networks: A new framework for modeling biological vision and brain information processing
Kriegeskorte N (2015) Annu. Rev. Vis. Sci. 2015. 1:417-46.

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