Willeke, K. F. | bioRxiv Science

bioRxiv2021

Behavioral state tunes mouse vision to ethological features through pupil dilation

Sensory processing changes with behavioral context to increase computational flexibility. In the visual system, active behavioral states enhance sensory responses but typically leave the preferred stimuli of neurons unchanged. Here we find that behavioral state does modulate stimulus selectivity in mouse visual cortex in the context of colored natural scenes. Using population imaging, behavior, pharmacology, and deep neural networks, we identified a shift of color selectivity towards ultraviolet stimuli exclusively caused by pupil dilation, resulting in a dynamic switch from rod to cone photoreceptors, extending their role beyond night and day vision. This facilitated the detection of ethological stimuli, such as aerial predators against the twilight sky. In contrast to previous studies that have used pupil dilation as an indirect measure of brain state, our results suggest that the brain uses pupil dilation itself to differentially recruit rods and cones on short timescales to tune visual representations to behavioral demands.

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bioRxiv2020

Generalization in data-driven models of primary visual cortex

AO_SCPLOWBSTRACTC_SCPLOWDeep neural networks (DNN) have set new standards at predicting responses of neural populations to visual input. Most such DNNs consist of a convolutional network (core) shared across all neurons which learns a representation of neural computation in visual cortex and a neuron-specific readout that linearly combines the relevant features in this representation. The goal of this paper is to test whether such a representation is indeed generally characteristic for visual cortex, i.e. gener-alizes between animals of a species, and what factors contribute to obtaining such a generalizing core. To push all non-linear computations into the core where the generalizing cortical features should be learned, we devise a novel readout that reduces the number of parameters per neuron in the readout by up to two orders of magnitude compared to the previous state-of-the-art. It does so by taking advantage of retinotopy and learns a Gaussian distribution over the neurons receptive field po-sition. With this new readout we train our network on neural responses from mouse primary visual cortex (V1) and obtain a gain in performance of 7% compared to the previous state-of-the-art network. We then investigate whether the convolutional core indeed captures general cortical features by using the core in transfer learning to a different animal. When transferring a core trained on thousands of neurons from various animals and scans we exceed the performance of training directly on that animal by 12%, and outperform a commonly used VGG16 core pre-trained on imagenet by 33%. In addition, transfer learning with our data-driven core is more data-efficient than direct training, achieving the same performance with only 40% of the data. Our model with its novel readout thus sets a new state-of-the-art for neural response prediction in mouse visual cortex from natural images, generalizes between animals, and captures better characteristic cortical features than current task-driven pre-training approaches such as VGG16.

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