Biology subjects

Carrasco, S. S.

Publications and source records attributed to Carrasco, S. S..

2 recordsLinked to original sources

bioRxiv2023

Hyperpolarization-Activated Currents Drive Neuronal Activation Sequences in Sleep

Sequential neuronal patterns are believed to support information processing in the cortex, yet their origin is still a matter of debate. We report that neuronal activity in the mouse head-direction cortex (HDC, i.e., the post-subiculum) was sequentially activated along the dorso-ventral axis during sleep at the transition from hyperpolarized "DOWN" to activated "UP" states, while representing a stable direction. Computational modelling suggested that these dynamics could be attributed to a spatial gradient of hyperpolarization-activated current (Ih), which we confirmed in ex vivo slice experiments and corroborated in other cortical structures. These findings open up the possibility that varying amounts of Ih across cortical neurons could result in sequential neuronal patterns, and that travelling activity upstream of the entorhinal-hippocampal circuit organises large-scale neuronal activity supporting learning and memory during sleep. HighlightsO_LINeuronal Activation Sequence in HDC: neuronal activity was sequentially reinstated along the dorsoventral axis of the HDC at UP state but not DOWN state onset. C_LIO_LIRole of Ih in Sequence Generation: Incorporating the hyperpolarization-activated current (Ih) into computational models, we identified its pivotal role in UP/DOWN dynamics and neuronal activity sequences. C_LIO_LIEx Vivo Verification: slice physiology revealed a dorsoventral gradient of Ih in the HDC. C_LIO_LIImplications Beyond HDC: the gradient of Ih could account for the sequential organization of neuronal activity across various cortical areas. C_LI

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↗