Denny, H. | bioRxiv Science

bioRxiv2024

Visual objects refine head direction coding

Animals use visual objects to guide navigation-related behaviors, from hunting prey, to escaping predators, to exploring the world. However, little is known about where visual objects are encoded in the mouse brain or how they impact processing in the spatial navigation system. Using functional ultrasound imaging in mice, we conducted a brain-wide screen and identified brain areas that were preferentially activated by images of objects compared to scrambled versions of the same images. While visual cortical areas did not show a significant preference, regions associated with spatial navigation were preferentially activated by visual objects. Electrophysiological recordings in postsubiculum, the primary cortical area of the head direction (HD) system, further confirmed a preference for visual objects, which was present in both HD cells and fast-spiking interneurons. Finally, we found that visual objects dynamically modulated HD cells, selectively increasing firing rates of HD cells aligned with a visual landmarks direction, while decreasing activity in HD cells coding for other directions. These results reveal that visual objects refine population-level coding of head direction.

neuroscience↗

bioRxiv2022

Pynapple: a toolbox for data analysis in neuroscience.

Datasets collected in neuroscientific studies are of ever-growing complexity, often combining high dimensional time series data from multiple data acquisition modalities. Handling and manipulating these various data streams in an adequate programming environment is crucial to ensure reliable analysis, and to facilitate sharing of reproducible analysis pipelines. Here, we present Pynapple, the PYthon Neural Analysis Package, a lightweight python package designed to process a broad range of time-resolved data in systems neuroscience. The core feature of this package is a small number of versatile objects that support the manipulation of any data streams and task parameters. The package includes a set of methods to read common data formats and allows users to easily write their own. The resulting code is easy to read and write, avoids low-level data processing and other error-prone steps, and is open source. Libraries for higher-level analyses are developed within the Pynapple framework but are contained within in a collaborative repository of specialized and continuously updated analysis routines. This provides flexibility while ensuring long-term stability of the core package. In conclusion, Pynapple provides a common framework for data analysis in neuroscience. HighlightsO_LIAn open-source framework for data analysis in systems neuroscience. C_LIO_LIEasy-to-use object-oriented programming for data manipulation. C_LIO_LIA lightweight and standalone package ensuring long-term backward compatibility. C_LI

neuroscience↗