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Vite, G. R.

Publications and source records attributed to Vite, G. R..

2 recordsLinked to original sources

Coherent thalamic inputs organize head direction signal in the medial entorhinal cortex

Successful navigation relies on signals that remain stable despite environmental changes. This stability can arise by constraining neuronal activity to low-dimensional subspaces. During sleep, when external input is reduced, pairwise coordination persists in the spatial navigation system, as observed for head-direction (HD) cells of the anterodorsal nucleus (ADn) and grid cells of the medial entorhinal cortex (MEC). As ADn is crucial for spatial representation in the MEC, we hypothesized that coherent HD input underlies MEC organization. To test this, we performed simultaneous recordings in the ADn and MEC during wakefulness and sleep. We found that HD cell pairs maintained stable coordination across both states, and that MEC cell coordination was partly driven by common inputs from ADn HD cells. These results suggest that MEC activity is shaped, in part, by coherent thalamic HD signals, supporting stable network organization.

neuroscience↗

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↗