Object recognition relies on inferior temporal (IT) cortical neural population representations that are themselves computed by a hierarchical network of feedforward and recurrently connected neural population called the ventral visual stream (areas V1, V2, V4 and IT). While recent work has created some 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. For example, current leading ventral stream models do not allow us to ask questions such as: How does the surround suppression behavior of individual V1 neurons ultimately relate to IT neural representation and to behavior?; or How would deactivation of a particular sub-population of V1 neurons specifically alter object recognition behavior? One reason we cannot yet do this is that individual V1 artificial neurons in multi-stage models have not been shown to be functionally similar with individual biological V1 neurons. Here, we took an important first step towards this direction 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 some 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. Crucially, we also observed that hierarchical models with V1-layers that better match macaque V1 at the single neuron level are also more aligned with human object recognition 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. Finally, we here show that an optimized classical neuroscientific model of V1 is still more functionally similar to primate V1 than all of the tested multi-stage models, suggesting that further model improvements are possible, and that those improvements would likely have tangible payoffs in terms of behavioral prediction accuracy and behavioral robustness.Single neurons in some image-computable hierarchical neural network models are functionally similar to single neurons in macaque primate visual cortex (V1)Some hierarchical neural networks models have V1 layers that better match the biological distributions of macaque V1 single neuron response propertiesMulti-stage hierarchical neural network models with V1 stages that better match macaque V1 are also more aligned with human object recognition behavior at their output stageCompeting Interest StatementThe authors have declared no competing interest.