Invariance in visual object recognition requires training: a computational argument - Goris, Robbe L.; Op De Beeck, Hans P. (Frontiers in Neuroscience, 2010)
Visual object recognition is remarkably accurate and robust, yet its neurophysiological underpinnings are poorly understood. Single cells in brain regions thought to underlie object recognition code for many stimulus aspects, which poses a limit on their invariance. Combining the responses of multiple non-invariant neurons via weighted linear summation, i.e. population-coding, has been suggested to offer an optimal decoding strategy able to achieve invariant object recognition. However, because object identification is essentially parameter optimization in this model, the characteristics of the identification task trained to perform are critically important. If this task does not require invariance, a neural population-code is inherently more selective but less tolerant than the single-neurons constituting the population. Nevertheless, tolerance can be learned –provided that it is trained for–, at the cost of selectivity. We argue that this model is the appropriate null-hypothesis to compare behavioural results with and conclude that it may explain several experimental findings.