Topic: natural movies

Much of sensory neuroscience focuses on presenting stimuli that are chosen by the experimenter because they are parametric and easy to sample and are thought to be behaviorally relevant to the organism. However, it is not generally known what the relevant features are in complex, natural scenes. This work focuses on using the retinal encoding of natural movies to determine the presumably behaviorally-relevant features that the brain represents. It is prohibitive to parameterize a natural movie and its respective retinal encoding fully. We use time within a natural movie as a proxy for the whol

The processing of natural visual environments rich with motion is central to animal survival. Many aspects of perceiving and detecting visual motion are already mature before the onset of visual experience. How the visual system develops to enable these functions in the absence of external stimuli remains unclear. A prominent transient feature of developing circuits is the ability to generate complex spatiotemporal patterns of spontaneous activity, even before sensory organs mature. For example, before vision onset in the mammalian retina, activity propagates in the form of spatiotemporally st

Efficient coding theory, which stipulates that the nervous system attempts to minimize redundancy in stimulus coding, has proven widely successful in predicting observed features in early sensory systems, including center-surround receptive fields (RFs), ON and OFF parallel pathways, and the relative arrangement of ON and OFF RF mosaics. However, relatively limited work has addressed the encoding of spatiotemporal stimuli. Ocko et al. (2018) examined spatiotemporal encoding in the retina using an approach based on a convolutional autoencoder, replicating known features of retinal ganglion cell

Sensory neurons can integrate their inputs nonlinearly, and this nonlinear pooling allows the extraction of complex features from the natural environment. Such nonlinear computations are ubiquitous in the visual system, and can be captured with encoding models that partition receptive fields into subunits whose outputs are nonlinearly combined. In the retina, the nonlinearities that transform subunit signals before integration by retinal ganglion cells affect the cells’ sensitivity to the spatial structure of natural scenes. Fitting subunit models to neural responses remains a challenge, as av

During the course of evolution, a species’ environment shapes its sensory abilities, as individuals with more optimized sensory abilities are more likely survive and procreate. Adaptations to the statistics of the natural environment can be observed along the early visual pathway and across species. Therefore, characterising the properties of natural environments and studying the representation of natural scenes along the visual pathway is crucial for advancing our understanding of the structure and function of the visual system. In the past 20 years, mice have become an important model in vis

Characterizing feature selectivities of sensory visual neurons is crucial for understanding how the brain forms representations of the external world. In vivo neurophysiological experiments aimed at identifying such tuning properties are constrained by time, making exhaustive searches in stimulus space infeasible. In recent years, deep neural networks (DNNs) have set new standards in predicting neuronal responses of the visual system to arbitrary stimuli, making in silico exploration of the stimulus-response space possible. As visual input is dynamic, there is a need for models that can faithf

Information in the nervous system is encoded by the spiking patterns of large populations of neurons. The analysis of such high-dimensional data is typically restricted to simple, arbitrarily defined features like spike rates, which discards information in the temporal structure of spike trains. Here, we use a recently developed method called SpikeShip based on optimal transport theory, which captures information from all the relative spike-timing relations among neurons. We compared spike-rate and spike-timing codes in neural ensembles from six visual areas during natural video presentations.

It is appealing to disclose the state of an animal as well as the functional connectivity of its neuronal circuit by calculating pairwise and higher-order interactions (HOI) among neuronal activity. Here, we present a systematic approach to achieve these goals by analysing the spiking activity of simultaneously recorded neurons. To this end, we first developed a method to extract significant pairwise and triple-wise interactions among neurons. Using statistically significant data, we then analysed pairwise and HOI from large-scale spiking activities in visual areas of mice during natural movie

Visual cortical neurons are richly inter-connected by within-area, and feedforward and feedback across-area circuits. How these different circuits contribute to visual function remains poorly understood. We performed simultaneous recordings from hundreds of neurons in V1 and V2 in anesthetized macaque monkeys. Using a latent variable model, we identified the feedforward, feedback, and within-area signals that contributed to the measured responses. The identified feedforward and feedback signals contributed equally to explaining activity in the two areas, consistent with their equal anatomical

The ability to reconstruct imagery represented by the brain has the potential to give us an intuitive understanding of what the brain “sees”. Reconstruction of visual input from human fMRI data has garnered significant attention in recent years. Comparatively less focus has been directed towards vision reconstruction from single-cell recordings, despite its potential to provide a more direct measure of the information represented by the brain. Here, we achieve high-quality reconstructions of videos presented to mice, from the activity of neurons in their visual cortex. With our method of video