33 The mammalian visual system, from retina to neocortex, has been extensively studied at both 34 anatomical and functional levels. Anatomy indicates the cortico-thalamic system is hierarchical, 35 but characterization of cellular-level functional interactions across multiple levels of this 36 hierarchy is lacking, partially due to the challenge of simultaneously recording activity across 37 numerous regions. Here, we describe a large, open dataset (part of the Allen Brain Observatory) 38 that surveys spiking from units in six cortical and two thalamic regions responding to a battery of 39 visual stimuli. Using spike cross-correlation analysis, we find that inter-area functional 40 connectivity mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas. 41Classical functional measures of hierarchy, including visual response latency, receptive field 42 size, phase-locking to a drifting grating stimulus, and autocorrelation timescale are all correlated 43 with the anatomical hierarchy. Moreover, recordings during a visual task support the behavioral 44 relevance of hierarchical processing. Overall, this dataset and the hierarchy we describe provide 45 a foundation for understanding coding and dynamics in the mouse cortico-thalamic visual 46 system. 47
Although self-motion perception is believed to rely heavily on visual cues, the inertial system also provides valuable information about movement through space. How the brain integrates inertial signals to update position can be better understood through a detailed characterization of self-motion perception during passive transport. In this study, we employed an intuitive method for measuring the perception of self-motion in real-world coordinates. Participants were passively translated by a robotic wheelchair in the absence of visual and auditory cues. The traveled trajectories consisted of twelve straight paths, five to six meters in length, each with a unique velocity profile. As participants moved, they pointed continuously toward a stationary target viewed at the beginning of each trial. By using an optical tracking system to measure the position of a hand-held pointing device, we were able to calculate participants' perceived locations with a high degree of spatial and temporal precision. Differentiating perceived location yielded absolute instantaneous perceived velocity (in units of meters per second), a variable that, to the best of our knowledge, has not previously been measured. Results indicate that pointing behavior is updated as a function of changes in wheelchair velocity, and that this behavior reflects differences in starting position relative to the target. During periods of constant, nonzero velocity, the perceived velocity of all participants decreases systematically over the course of the trajectory. This suggests that the inertial signal is integrated in a leaky fashion, even during the relatively short paths used in this experiment. This methodology allows us to characterize such nonveridical aspects of self-motion perception with more precision than has been achieved in the past. The continuous-pointing paradigm used here can also be effectively adapted for use in other research domains, including spatial updating, vection, and visual-vestibular integration
The response of a set of neurons in an area is the result of the sensory input, the interaction of the neurons within the area as well as the long range interactions between areas. We aimed to study the relation between interactions among multiple areas, and if they are fixed or dynamic. The structural connectivity provides a substrate for these interactions, but anatomical connectivity is not known in sufficient detail and it only gives us a static picture. Using the Allen Brain Observatory Visual Coding Neuropixels dataset, which includes simultaneous recordings of spiking activity from up to 6 hierarchically organized mouse cortical visual areas, we estimate the functional connectivity between neurons using a linear model of responses to flashed static grating stimuli. We characterize functional connectivity between populations via interaction subspaces. We find that distinct subspaces of a source area mediate interactions with distinct target areas, supporting the notion that cortical areas use distinct channels to communicate. Most importantly, using a piecewise linear model for activity within each trial, we find that these interactions evolve dynamically over tens of milliseconds following a stimulus presentation. Inter-areal subspaces become more aligned with the intra-areal subspaces during epochs in which a feedforward wave of activity propagates through visual cortical areas. When the short-term dynamics are averaged over, we find that the interaction subspaces are stable over multiple stimulus blocks. These findings have important implications for understanding how information flows through biological neural networks composed of interconnected modules, each of which may have a distinct functional specialization.
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