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Abstract
Despite great efforts over several decades, our best models of primary visual cortex (V1) still predict spiking activity quite poorly when probed with natural stimuli, highlighting our limited understanding of the nonlinear computations in V1. Recently, two approaches based on deep learning have emerged for modeling these nonlinear computations: transfer learning from artificial neural networks trained on object recognition and data-driven convolutional neural network models trained end-to-end on large populations of neurons. Here, we test the ability of both approaches to predict spiking activity in response to natural images in V1 of awake monkeys. We found that the transfer learning approach performed similarly well to the data-driven approach and both outperformed classical linear-nonlinear and wavelet-based feature representations that build on existing theories of V1. Notably, transfer learning using a pre-trained feature space required substantially less experimental time to achieve the same performance. In conclusion, multi-layer convolutional neural networks (CNNs) set the new state of the art for predicting neural responses to natural images in primate V1 and deep features learned for object recognition are better explanations for V1 computation than all previous filter bank theories. This finding strengthens the necessity of V1 models that are multiple nonlinearities away from the image domain and it supports the idea of explaining early visual cortex based on high-level functional goals.
Highlights
An essential step towards understanding visual processing in the brain is building models that accurately predict neural responses to arbitrary stimuli [1]
We model spiking activity in primary visual cortex (V1) of monkeys using deep convolutional neural networks (CNNs), which have been successful in computer vision
We found that more complex cells are explained better by the Gabor filter bank model than an linear-nonlinear Poisson model (LNP) model (Fig. 9C)
Summary
An essential step towards understanding visual processing in the brain is building models that accurately predict neural responses to arbitrary stimuli [1]. Our current standard model of V1 is based on linear-nonlinear models (LN) [4, 5] and energy models [6] to explain simple and complex cells, respectively While these models work reasonably well to model responses to simple stimuli such as gratings, they fail to account for neural responses to more complex patterns [7] and natural images [8, 9]. There are a number of hypotheses about nonlinear computations in V1, including normative models like overcomplete sparse coding [11, 12] or canonical computations like divisive normalization [13, 14] The latter has been used to explain specific phenomena such as center-surround interactions with carefully designed stimuli [15,16,17,18]. To date, these ideas have not been turned into predictive models of spiking responses that generalize beyond simple stimuli – especially to natural images
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