Modeling invariant object processing based on tight integration of simulated and empirical data in a Common Brain Space - Peters, Judith C.; Reithler, Joel; Goebel, Rainer (Frontiers in Computational Neuroscience, 2012)
Both in the field of Computer Vision and Experimental Neuroscience, recent advances have been made regarding the mechanisms underlying invariant object recognition. However, the differential methodological aims in both fields caused an independent model evolvement. A tighter integration of simulations and empirical observations may contribute to cross-fertilized development of 1) neurobiologically plausible computational models and 2) computationally-defined empirical theories, incrementally merged into a comprehensive brain model. We review recent fMRI findings on object invariance and suggest how they can be quantitatively compared to model simulations by projecting predicted and observed data in one “Common Brain Space". The simultaneous matching of activity patterns within and across multiple processing stages in the simulated and empirical large-scale network may help to clarify how high-order invariant representations are created from low-level features. Given that columnar-level imaging is now in reach, due to the advent of high-resolution fMRI, it is time to capitalize on this new window into the brain and test which predictions of the various object recognition models are supported by this novel empirical evidence.