Biology subjects

Viejo, G.

Publications and source records attributed to Viejo, G..

5 recordsLinked to original sources

bioRxiv2025

Coherent dynamics of thalamic head-direction neurons irrespective of input

The generation of the head-direction (HD) signal, a cornerstone of the brains navigation system, is classically attributed to the lateral mammillary nucleus (LMN) and associated brainstem structures, where attractor-like dynamics are thought to maintain population coherence. Here, using multi-site recordings and optogenetic perturbations along the mammillary-thalamic-cortical pathway, we demonstrate that HD neurons in the anterodorsal nucleus of the thalamus (ADN) maintain coherent population dynamics even when their LMN inputs become decorrelated during non-REM sleep. These findings reveal that thalamic coherence does not strictly depend on structured input from the LMN; instead, it can emerge from local thalamic processes involving shared inhibition and non-linear responses. Together, our findings reveal a previously unrecognized, state-dependent shift in the circuit organizing the HD signal, establishing the thalamus as an active substrate capable of independently sustaining internal representations across brain states.

neuroscience↗

bioRxiv2024

Months-long stability of the head-direction system

Spatial orientation is a universal ability that allows animals to navigate their environment. In mammals, the head-direction (HD) system is an essential component of the brains navigation system, yet the stability of its underlying neuronal code remains unclear. Here, by longitudinally tracking the activity of the same HD cells in freely moving mice, we show that the internal organization of population activity in the HD system was preserved for several months. Furthermore, the HD system developed a unique mapping between its internal organization and spatial orientation in each environment. This was not affected by visits to other environments and was stabilized with experience. These findings demonstrate that stable neuronal code supports the sense of direction and forms long-lasting orientation memories.

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↗

bioRxiv2019

Precise coupling of the thalamic head-direction system to hippocampal ripples.

The anterior thalamus is a key relay of neuronal signals within the limbic system. During sleep, the occurrence of hippocampal sharp wave-ripples (SWRs), believed to mediate consolidation of explicit memories, is modulated by thalamocortical network activity, yet how information is routed around SWRs and how this communication depends on neuronal dynamics remains unclear. Here, by simultaneously recording ensembles of neurons in the anterior thalamus and local field potentials in the CA1 area of the hippocampus, we show that the head-direction (HD) cells of the anterodorsal nucleus are set in stable directions immediately before SWRs. This response contrasts with other thalamic cells that exhibit diverse couplings to the hippocampus related to their intrinsic dynamics but independent of their anatomical location. Thus, our data suggest a specific and homogeneous contribution of the HD signal to hippocampal activity and a diverse and cell-specific coupling of non-HD neurons.

neuroscience↗

bioRxiv2017

Resolving neuronal population code and coordination withgradient boosted trees

Understanding how neurons cooperate to integrate sensory inputs and guide behavior is a fundamental problem of neuroscience. A large body of methods have been developed to study neuronal firing at the single cell and population levels, generally seeking interpretability as well as predictivity. However, these methods are usually confronted with the lack of ground-truth necessary to validate the approach. Here, using neuronal data from the head-direction system, we present evidence how gradient boosted trees, a non-linear and supervised machine learning tool, learns the relationship between behavioral parameters and neuronal responses with high accuracy by optimizing the information rate. Interestingly, and unlike other classes of Machine Learning methods, the intrinsic structure of the trees can be interpreted in relation to behavior (e.g. to recover the tuning curves) or to study how neurons cooperate with their peers in the network. As an example, we show how the method reveals a temporally shifted coordination in a thalamo-cortical circuit during wakefulness and sleep, indicating a brain-state independent feed-forward circuit. Machine learning tools thus open new avenues for benchmarking model-based characterization of spike trains.

neuroscience↗