Ali Almasi

University of Melbourne and & National Vision Research Institute, Australia

Title: Feature selectivity and invariance in primary visual cortex


Object recognition in scenes develops across a hierarchy of visual areas. Robust recognition requires fine selectivity for particular features of relevance and invariance to irrelevant features. Deep convolutional neural networks have achieved near-human levels of performance in object recognition by iteratively applying filters that select features, followed by pooling of their outputs to generate invariance. We applied a “filter-then-pool” model to recordings from neurons in cat primary visual cortex (V1) to investigate visual feature selectivity and invariance in the brain. Many neurons pooled the outputs of multiple filters, resulting in selectivity for feature characteristics that were preserved across filters, and invariance to feature characteristics that differed across filters. We found cells corresponding to the “energy model” of V1 complex cells that were invariant to spatial phase but selective to a combination of other feature characteristics. We also frequently found cells that showed only partial invariance to spatial phase, while exhibiting invariance to perturbations in peak orientation and spatial frequency. For each of these feature characteristics, some cells were more selective for the characteristic and others that were more invariant. To quantify the “selective-to-invariant” spectrum we used a bandwidth measuring the range of a characteristic over which a cell responded equally allowing for modest changes in contrast (< 2x). Peak orientation had the greatest portion of selective cells, followed by peak spatial frequency and then spatial phase, the latter showing the greatest portion of invariance. The bandwidth measure also allowed us to quantify how much the nonlinear pooling operation contributed to invariance by comparing it to the bandwidth expected from a linear operation. This showed that spatial phase invariance benefited the most from nonlinear pooling over multiple features, with the bandwidth frequently much greater than expected in the linear case, and sometimes reaching the maximum possible (360 deg). In contrast, orientation and spatial frequency had bandwidths that did not increase much with nonlinear pooling over that of linear pooling. Thus, in V1 there is a diversity of cells that combine selectivity for some feature characteristics with invariance to perturbations in others. This diversity encompasses a variety of feature characteristics beyond spatial phase.



Ali completed his PhD at the university of Melbourne in 2017 and is currently a research fellow at the National Vision Research Institute of Australia. His research interests include understanding how visual information is processed in the brain using experimental and theoretical approaches. During his PhD, Ali investigated the applicability of computational models based on efficient coding to complex cells in primary visual cortex, which is challenging topic due to the inherent nonlinearities found in their receptive field properties.