Incremental Learning and Self-Attention Mechanisms Improve Neural System Identification

Convolutional neural networks (CNNs) have been shown to be the state-of-the-art approach for modeling the transfer functions of visual cortical neurons. Cortical neurons in the primary visual cortex are are sensitive to contextual information mediated by extensive horizontal and feedback connections. Standard CNNs can integrate global spatial image information to model such contextual modulation via two mechanisms: successive rounds of convolutions and a fully connected readout layer. In this paper, we find that non-local networks or self-attention (SA) mechanisms, theoretically related to context-dependent flexible gating mechanisms observed in the primary visual cortex, improve neural response predictions over parameter-matched CNNs in two key metrics: tuning curve correlation and tuning peak. We factorize networks to determine the relative contribution of each context mechanism. This reveals that information in the local receptive field is most important for modeling the overall tuning curve, but surround information is critically necessary for characterizing the tuning peak. We find that self-attention can replace subsequent spatial-integration convolutions when learned in an incremental manner, and is further enhanced in the presence of a fully connected readout layer, suggesting that the two context mechanisms are complementary. Finally, we find that learning a receptive-field-centric model with self-attention, before incrementally learning a fully connected readout, yields a more biologically realistic model in terms of center-surround contributions.

A calcium imaging large dataset reveals novel functional organization in macaque V4

The topological organization and feature preferences of primate visual area V4 have been primarily studied using artificial stimuli. Here, we combined large-scale calcium imaging with deep learning methods to characterize and understand how V4 processes natural images. By fitting a deep learning model to an unprecedentedly large dataset of columnar scale cortical responses to tens of thousands of natural stimuli and using the model to identify the images preferred by each cortical pixel, we obtained a detailed V4 topographical map of natural stimulus preference. The map contains distinct functional domains preferring a variety of natural image features, ranging from surface-related features such as color and texture to shape-related features such as edge, curvature, and facial features. These predicted domains were verified by additional widefield calcium imaging and single-cell resolution two-photon imaging. Our study reveals the systematic topological organization of V4 for encoding image features in natural scenes.