Multi scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior
Primate visual object recognition relies on the representations in cortical areas at the top of the ventral stream that are computed by a complex, hierarchical network of neural populations. While recent work has created reasonably accurate image computable hierarchical neural network models of those neural stages, those models do not yet bridge between the properties of individual neurons and the overall emergent behavior of the ventral stream. One reason we cannot yet do this is that individual artificial neurons in multi stage models have not been shown to be functionally similar to individual biological neurons. Here, we took an important first step by building and evaluating hundreds of hierarchical neural network models in how well their artificial single neurons approximate macaque primary visual cortical (V1) neurons. We found that single neurons in certain models are surprisingly similar to their biological counterparts and that the distributions of single neuron properties, such as those related to orientation and spatial frequency tuning, approximately match those in macaque V1. Critically, we observed that hierarchical models with V1 stages that better match macaque V1 at the single neuron level are also more aligned with human object recognition behavior. Finally, we show that an optimized classical neuroscientific model of V1 is more functionally similar to primate V1 than all of the tested multi stage models, suggesting room for further model improvements with tangible payoffs in closer alignment to human behavior. These results provide the first multi stage, multi scale models that allow our field to ask precisely how the specific properties of individual V1 neurons relate to recognition behavior. Highlights Image computable hierarchical neural network models can be naturally extended to create hierarchical "brain models" that allow direct comparison with biological neural networks at multiple scales from single neurons, to population of neurons, to behavior. Single neurons in some of these hierarchical brain models are functionally similar to single neurons in macaque primate visual cortex (V1) Some hierarchical brain models have processing stages in which the entire distribution of artificial neuron properties closely matches the biological distributions of those same properties in macaque V1 Hierarchical brain models whose V1 processing stages better match the macaque V1 stage also tend to be more aligned with human object recognition behavior at their output stage