ABSTRACT
While visual responses to familiar and novel stimuli have been extensively studied, it is unknown how neuronal representations of familiar stimuli are affected when they are interleaved with novel images. We examined a large-scale dataset from mice performing a visual go/no-go change detection task. After training with eight images, six novel images were interleaved with two familiar ones. Unexpectedly, we found that the behavioral performance in response to familiar images was impaired when they were mixed with novel images. When familiar images were interleaved with novel ones, the dimensionality of their representation increased, indicating a perturbation of their neuronal responses. Furthermore, responses to familiar images in the primary visual cortex were less predictive of responses in higher-order areas, indicating less efficient communication. Spontaneous correlations between neurons were predictive of responses to novel images, but less so to familiar ones. Our study demonstrates the modification of representations of familiar images by novelty.
ABSTRACT
To understand the neural basis of behavior, it is essential to measure spiking dynamics across many interacting brain regions. Although new technologies, such as Neuropixels probes, facilitate multi-regional recordings, significant surgical and procedural hurdles remain for these experiments to achieve their full potential. Here, we describe skull-shaped hemispheric implants enabling large-scale electrophysiology datasets (SHIELD). These 3D-printed skull-replacement implants feature customizable insertion holes, allowing dozens of cortical and subcortical structures to be recorded in a single mouse using repeated multi-probe insertions over many days. We demonstrate the procedure's high success rate, biocompatibility, lack of adverse effects on behavior, and compatibility with imaging and optogenetics. To showcase SHIELD's scientific utility, we use multi-probe recordings to reveal novel insights into how alpha rhythms organize spiking activity across visual and sensorimotor networks. Overall, this method enables powerful, large-scale electrophysiological experiments for the study of distributed neural computation.