1Deep convolutional neural networks (CNNs) have emerged as the state of the art 2 for predicting neural activity in visual cortex. While such models outperform classical 3 linear-nonlinear and wavelet-based representations, we currently do not know what 4 computations they approximate. Here, we tested divisive normalization (DN) for 5 its ability to predict spiking responses to natural images. We developed a model 6 that learns the pool of normalizing neurons and the magnitude of their contribution 7 end-to-end from data. In macaque primary visual cortex (V1), we found that 8 our interpretable model outperformed linear-nonlinear and wavelet-based feature 9 representations and almost closed the gap to high-performing black-box models.10 Surprisingly, within the classical receptive field, oriented features were normalized 11 preferentially by features with similar orientations rather than non-specifically as 12 currently assumed. Our work provides a new, quantitatively interpretable and 13 high-performing model of V1 applicable to arbitrary images, refining our view on 14 gain control within the classical receptive field. 15 53 some original experimental studies suggest that this assumption may not be correct for some 54 neurons (Bonds, 1989; DeAngelis et al., 1992), and normative models of normalization predict 55 that the magnitude with which a given neuron contributes to another neuron's normalization 56 depends on the relationship of their response properties (Schwartz and Simoncelli, 2001). 57 In this paper, we address two main questions raised above: (1) can an interpretable model based 58 on divisive normalization match the superior performance of black-box CNNs over simpler, 59 interpretable subunit or energy models when predicting spiking responses to natural images 60 and (2) how are V1 neurons normalized? We focus on responses to stimuli mostly restricted 61 to the classical receptive field and on models that account only for normalization by neurons 62 with overlapping receptive field locations. We developed an end-to-end trainable divisive 63 normalization model to predict V1 spike counts from natural stimuli. Our model learns the 64 filter coefficients of all neurons as well as their normalization weights directly from the data. 65 We applied our model to natural image responses in monkey V1 and found that it outperforms 66 linear-nonlinear and subunit models, and is competitive with that of state-of-the-art CNNs 67 while requiring much fewer parameters and being directly interpretable. This result implies 68 that divisive normalization is an important computation under stimulation with natural images. 69Importantly, we found that oriented features were normalized preferentially by features with 70 similar orientation, in contrast to the current standard model of nonspecific normalization 71 (Heeger, 1992; Busse et al., 2009). Our work thus advances our understanding of V1 function 72 by establishing a new state-of-the-art interpretable model and predicting an orientation-specific 73 divisive nor...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.