Comparison of visual quantities in untrained deep neural networks

The ability to compare quantities of visual objects with two distinct measures, proportion and difference, is observed in newborn animals. Nevertheless, how this function originates in the brain, even before training, remains unknown. Here, we show that neuronal tuning for quantity comparison can arise spontaneously in completely untrained deep neural networks. Using a biologically inspired model neural network, we found that units selective to proportions and differences between visual quantities emerge in randomly initialized networks and that they enable the network to perform quantity comparison tasks. Further analysis shows that two distinct tunings to proportion and difference both originate from a random summation of monotonic, nonlinear responses to changes in relative quantities. Notably, we found that a slight difference in the nonlinearity profile determines the type of measure. Our results suggest that visual quantity comparisons are primitive types of functions that can emerge spontaneously in random feedforward networks.