Understanding whole-brain-scale electrophysiological recordings will rely on the collective work of multiple labs. Because two labs recording from the same brain area often reach different conclusions, it is critical to quantify and control for features that decrease reproducibility. To address these issues, we formed a multi-lab collaboration using a shared, open-source behavioral task and experimental apparatus. We repeatedly inserted Neuropixels multi-electrode probes targeting the same brain locations (including posterior parietal cortex, hippocampus, and thalamus) in mice performing the behavioral task. We gathered data across 9 labs and developed a common histological and data processing pipeline to analyze the resulting large datasets. After applying stringent behavioral, histological, and electrophysiological quality-control criteria, we found that neuronal yield, firing rates, spike amplitudes, and task-modulated neuronal activity were reproducible across laboratories. To quantify variance in neural activity explained by task variables (e.g., stimulus onset time), behavioral variables (timing of licks/paw movements), and other variables (e.g., spatial location in the brain or the lab ID), we developed a multi-task neural network encoding model that extends common, simpler regression approaches by allowing nonlinear interactions between variables. We found that within-lab random effects captured by this model were comparable to between-lab random effects. Taken together, these results demonstrate that across-lab standardization of electrophysiological procedures can lead to reproducible results across labs. Moreover, our protocols to achieve reproducibility, along with our analyses to evaluate it are openly accessible to the scientific community, along with our extensive electrophysiological dataset with corresponding behavior and open-source analysis code.

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One of the most important tasks for humans is the attribution of causes and effects in all wakes of life. The first systematical study of visual perception of causality—often referred to as phenomenal causality—was done by Albert Michotte using his now well-known launching events paradigm. Launching events are the seeming collision and seeming transfer of movement between two objects—abstract, featureless stimuli (“objects”) in Michotte’s original experiments. Here, we study the relation between causal ratings for launching events in Michotte’s setting and launching collisions in a photorealistically computer-rendered setting. We presented launching events with differing temporal gaps, the same launching processes with photorealistic billiard balls, as well as photorealistic billiard balls with realistic motion dynamics, that is, an initial rebound of the first ball after collision and a short sliding phase of the second ball due to momentum and friction. We found that providing the normal launching stimulus with realistic visuals led to lower causal ratings, but realistic visuals together with realistic motion dynamics evoked higher ratings. Two-dimensional versus three-dimensional presentation, on the other hand, did not affect phenomenal causality. We discuss our results in terms of intuitive physics as well as cue conflict.

Zebrafish pretectal neurons exhibit specificities for large-field optic flow patterns associated with rotatory or translatory body motion. We investigate the hypothesis that these specificities reflect the input statistics of natural optic flow. Realistic motion sequences were generated using computer graphics simulating self-motion in an underwater scene. Local retinal motion was estimated with a motion detector and encoded in four populations of directionally tuned retinal ganglion cells, represented as two signed input variables. This activity was then used as input into one of two learning networks: a sparse coding network (competitive learning) and backpropagation network (supervised learning). Both simulations develop specificities for optic flow which are comparable to those found in a neurophysiological study [8], and relative frequencies of the various neuronal responses are best modeled by the sparse coding approach. We conclude that the optic flow neurons in the zebrafish pretectum do reflect the optic flow statistics. The predicted vectorial receptive fields show typical optic flow fields but also "Gabor" and dipole-shaped patterns that likely reflect difference fields needed for reconstruction by linear superposition.

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