Can TNNs expand to self-supervised objectives? Yes, to a degree. We show that training All-TNNs with SimCLR yields smooth topography and category-independent spatial biases. However, SimCLR training fails to reproduce the structure of human-like category-specific spatial biases. 10/12

We show that these behavioural accuracy maps are structured and exhibit category-specific effects. Importantly, All-TNNs better reproduce these spatial structures of human visual biases than CNNs and other control models. 9/12

To study the impact of topography on behaviour, we conducted a human psychophysical experiment to quantify object recognition performance across spatial locations. This provided us with category-specific spatial accuracy maps for humans. 8/12

Similarly, All-TNNs allocate energy expenditure to task-relevant input regions, using an order of magnitude less “metabolic” cost than CNNs! And the smoother the topography, the greater the energy efficiency of the network! Energy efficiency was not explicitly optimised for. 7/12

Upon training, topographical features reminiscent of the ventral stream emerge in All-TNNs, including smooth orientation selectivity maps in the first layer, and category-based selectivity clusters for tools, scenes, and faces in the last layer. 6/12

All-TNNs overcome this limitation. In All-TNNs 1) each unit has its own local RF, 2) units in each layer are arranged on a 2D “cortical sheet” without weight sharing, and 3) feature selectivity varies smoothly across space by encouraging similar selectivity in neighboring units. 5/12

Now out in Nature Human Behaviour @nathumbehav.nature.com : “End-to-end topographic networks as models of cortical map formation and human visual behaviour”. Please check our NHB link: www.nature.com/articles/s41...