Comparison of visual quantities in untrained neural networks.

The ability to compare quantities of visual objects with two distinct measures, proportion and difference, is observed even in newborn animals. However, how this function originates in the brain, even before visual experience, remains unknown. Here, we propose a model in which neuronal tuning for quantity comparisons can arise spontaneously in completely untrained neural circuits. Using a biologically inspired model neural network, we find that single units selective to proportions and differences between visual quantities emerge in randomly initialized feedforward wirings and that they enable the network to perform quantity comparison tasks. Notably, we find that two distinct tunings to proportion and difference originate from a random summation of monotonic, nonlinear neural activities and that a slight difference in the nonlinear response function determines the type of measure. Our results suggest that visual quantity comparisons are primitive types of functions that can emerge spontaneously before learning in young brains. [Display omitted] • The ability to compare visual quantity is observed in naive animals • Units tuned to ratios and differences arise spontaneously in untrained networks • Feedforward wiring of monotonic neural activity induces quantity-comparison tuning • Slightly different nonlinear response functions induce distinct types of tuning Visual quantity comparisons with two distinct measures, ratio and difference, are observed even in newborn animals. Here, Lee et al. find that single units tuned to ratios and differences in visual quantities arise spontaneously in completely untrained neural networks and that they enable the network to perform quantity comparison tasks. [ABSTRACT FROM AUTHOR]