  Convolutional neural networks (CNNs) have recently emerged as promising
models of the ventral visual stream, despite their lack of biological
specificity. While current state-of-the-art models of the primary visual cortex
(V1) have surfaced from training with adversarial examples and extensively
augmented data, these models are still unable to explain key neural properties
observed in V1 that arise from biological circuitry. To address this gap, we
systematically incorporated neuroscience-derived architectural components into
CNNs to identify a set of mechanisms and architectures that comprehensively
explain neural activity in V1. We show drastic improvements in model-V1
alignment driven by the integration of architectural components that simulate
center-surround antagonism, local receptive fields, tuned normalization, and
cortical magnification. Upon enhancing task-driven CNNs with a collection of
these specialized components, we uncover models with latent representations
that yield state-of-the-art explanation of V1 neural activity and tuning
properties. Our results highlight an important advancement in the field of
NeuroAI, as we systematically establish a set of architectural components that
contribute to unprecedented explanation of V1. The neuroscience insights that
could be gleaned from increasingly accurate in-silico models of the brain have
the potential to greatly advance the fields of both neuroscience and artificial
intelligence.
