Biology subjects

Bauza, M.

Publications and source records attributed to Bauza, M..

8 recordsLinked to original sources

bioRxiv2026

The medial entorhinal spatial map is built on excitatory-inhibitory network motifs defined by their functional cell type and theta modulation

The medial entorhinal cortex (mEC) contains functionally specific neurons crucial for spatial memory and navigation, including grid, border, spatial, head direction, and cue cells. However, how these neurons interact to build spatial maps remains unclear. Here, using simultaneous recordings from hundreds of functionally defined mEC neurons in mice navigating virtual tracks with varying numbers of visual landmarks, we uncovered connectivity motifs that define the mEC functional network architecture and link it to the principles governing allocentric map formation. We found that connectivity between cells was cell-type-specific and grouped into subnetworks based on their function and theta modulation, with theta-modulated connections dominating over non-theta. Functionally distinct neurons preferentially connected to their own type, and their interactions were coordinated by a shared inhibitory pool of interneurons, with a higher proportion of excitatory-to-inhibitory connections than between excitatory cells. This was accompanied by the number of fields formed across all mEC cell types increasing sublinearly with the number of available cues. Grid cells showed the strongest relative connectivity to interneurons regardless of their theta modulation, linking otherwise largely isolated theta and non-theta streams. Grid cells were also least likely to form a field in response to cues. Together, these findings reveal an mEC network architecture organised by cell type and theta dependency, in which inhibitory interactions, with grid cells as the strongest hub, play a central role in building the mEC allocentric map.

neuroscience↗

bioRxiv2025

Dissecting novel object exploration: a fully automated homecage-based novel object recognition test

The novel object recognition test is a frequently used memory test in rodents. Due to its ethological nature, cross-species relevance, and specificity to testing hippocampal and parahippocampal function, it has been widely applied in basic and translational research. However, its implementation proves challenging due to multiple uncontrolled factors. Here, we describe a fully automated homecage-based novel object recognition test for assessing long-term object memory in mice. We present an empirically guided computational model to show the robustness of this approach despite ambiguity in defining exploratory behaviours. We show that mice preferentially explored novel compared to familiar objects after 24-hour and 7-day retention periods, starting to discern them while still a distance away. The findings were replicated across two facilities. Furthermore, the ability to recognise the novel object depends on the mouses prior interactions with the replaced object after 24 hours, but not after 7 days. Finally, we showed that external factors may introduce undesired exploration biases, which can be addressed using relative instead of absolute discrimination measures. The fully automated homecage-based object recognition test will improve standardisation, rigour, and reproducibility, as well as expand our understanding of the factors influencing object exploratory behaviours and object memory. MotivationRecognition of objects as novel or familiar is an important cognitive memory function with cross-species relevance. Extensive work has provided a good understanding of the brain regions involved. Despite the apparent simplicity of novel object recognition (NOR) tests, they remain challenging due to their sensitivity to various uncontrolled experimental factors and differences in study design. Here, we report a fully automated standardised NOR test carried out in a mouses homecage, which minimises previously reported variability in NOR tests. HighlightsO_LIFully automated novel object recognition test for assessing long-term object memory in the mouses homecage C_LIO_LIA robust analysis pipeline described C_LIO_LITest replicated in two facilities with comparable results C_LIO_LIAn empirically guided computational model pointing to the robustness of this approach introduced C_LIO_LIMajor factors affecting the ability to discriminate novel from familiar objects, including object exploration bias, are described C_LI

neuroscience↗

bioRxiv2024

Repix: reliable, reusable, versatile chronic Neuropixels implants using minimal components

Neuropixels probes represent the state-of-the-art for high-yield electrophysiology in neuroscience: the simultaneous recording of hundreds of neurons is now routinely carried out in head-restrained animals. In contrast, neural recording in unrestrained animals, as well as recording and tracking neurons over days, remains challenging, though it is possible using chronic implants. A major challenge is the availability of simple methods that can be implemented with limited or no prior experience with Neuropixels probes, while achieving reliable, reusable, versatile high-density electrophysiology. Here we developed, deployed, and evaluated the real-world performance of Repix, a chronic implantation system that permits the repeated re-use of Neuropixels probes. The lightweight system allows implanted animals to express a full range of natural behaviors, including social behaviors. We show that Repix allows the recording of hundreds of neurons across many months, up to a year, with implants across cortical and subcortical brain regions. Probes can be reused repeatedly with stable yield. Repix has been used by 16 researchers in 10 laboratories to date, and we evaluated the real-world performance of Repix in a variety of chronic recording paradigms in both mice and rats with a combined 209 implantations. We found that the key advantage of Repix is robustness and simplicity. Adopters of Repix became proficient at five procedures on average, regardless of prior experience with in vivo electrophysiology. With the companion protocol alongside this article, the performance and user-friendliness of Repix should facilitate a wide uptake of chronic Neuropixels recordings.

neuroscience↗

bioRxiv2023

Simultaneous representation of multiple time horizons by entorhinal grid cells and CA1 place cells

Grid cells and place cells constitute the basic building blocks of the medial entorhinal-hippocampal spatial cognitive map by representing the spatiotemporal continuum of an animals past, present and future locations. However, the spatiotemporal relationship between these different cell types is unclear. Here we co-recorded grid and place cells in freely foraging rats. We show that average time shifts in grid cells tend to be prospective and are proportional to their spatial scale, providing a nearly instantaneous readout of a spectrum of progressively increasing time horizons ranging hundreds of milliseconds. Average time shifts of place cells are generally larger compared to grid cells and also increase with place field sizes. Moreover, time shifts displayed nonlinear modulation by the animals trajectories in relation to the local boundaries and locomotion cues. Finally, long and short time shifts occurred at different parts of the theta cycle, which may facilitate their readout. Together, these findings suggest that progressively increasing time horizons of grid and place cells may provide a basis for calculating animal trajectories essential for goal-directed navigation and planning.

neuroscience↗

bioRxiv2023

Variational Log-Gaussian Point-Process Methods for Grid Cells

We present practical solutions to applying Gaussian-process methods to calculate spatial statistics for grid cells in large environments. Gaussian processes are a data efficient approach to inferring neural tuning as a function of time, space, and other variables. We discuss how to design appropriate kernels for grid cells, and show that a variational Bayesian approach to log-Gaussian Poisson models can be calculated quickly. This class of models has closed-form expressions for the evidence lower-bound, and can be estimated rapidly for certain parameterizations of the posterior covariance. We provide an implementation that operates in a low-rank spatial frequency subspace for further acceleration, and demonstrate these methods on experimental data.

neuroscience↗

bioRxiv2022

Unsupervised, frequent and remote: a novel platform for personalised digital phenotyping of spatial working memory and image recognition in humans

Spatial working memory and image recognition tests are commonly used to facilitate the diagnosis of hippocampal-related neurological disorders such as Alzheimers disease due to their relatively high specificity and sensitivity to damage to the medial temporal lobes compared to standard commonly used clinical tests. Pathological changes in Alzheimers disease start years before the formal diagnosis is made, partially due to testing too late. To address this challenge, we developed a novel digital platform, hAge ( healthy Age), which integrates double spatial alternation, image recognition and visuospatial tasks for frequent remote unsupervised assessment of spatial and non-spatial working memory. 191 healthy adults (67% females, 18-81 years old) participated in the study. In line with findings using standard laboratory tests, we showed that performance on the spatial alternation task negatively correlated with inter-trial periods and performance levels on image recognition and visuospatial tasks can be controlled by varying image similarity. Importantly, we demonstrated that frequent engagement with the double spatial alternation task leads to a strong practice effect, previously identified as a potential measure of cognitive decline in MCI patients. Finally, we discuss how lifestyle and motivation confounds may present a serious challenge for cognitive assessment in real-world uncontrolled environments.

neuroscience↗

bioRxiv2022

A novel fully-automated system for lifelong continuous phenotyping of mouse cognition and behaviour

Comprehensive ethologically-relevant behavioural phenotyping in rodent experiments is essential for deciphering the neural basis of animal cognition. Automated home-cage monitoring systems present a valuable tool to fulfil this need. However, they often involve complex animal training routines, water or food deprivation, and probe a limited range of behaviours. Here, we present a new fully automated AI-driven home-cage system for cognitive and behavioural phenotyping in mice. The system incorporates spontaneous alternation T-maze, novel-object recognition and object-in-place recognition tests combined with monitoring of an animals position, water consumption, quiescence and locomotion patterns, all carried out continuously and simultaneously in an unsupervised fashion over long periods of time. Mice learnt the tasks rapidly without any need for water or food restrictions. We applied ethomics approach to show that combined statistical properties of multiple behaviours can be used to discriminate between mice with hippocampal, medial entorhinal and sham lesions and accurately predict genotype of Alzheimers disease mouse models on an individual animal level, surpassing the performance of several gold standard cognitive tests. This technology could enable large-scale behavioural screening for genes and neural circuits underlying spatial memory and other cognitive processes.

neuroscience↗

bioRxiv2020

Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings

To study the dynamics of neural processing across timescales, we require the ability to follow the spiking of thousands of individually separable neurons over weeks and months, during unrestrained behavior. To address this need, we introduce the Neuropixels 2.0 probe together with novel analysis algorithms. The new probe has over 5,000 sites and is miniaturized such that two probes plus a headstage, recording 768 sites at once, weigh just over 1 g, suitable for implanting chronically in small mammals. Recordings with high quality signals persisting for at least two months were reliably obtained in two species and six different labs. Improved site density and arrangement combined with new data processing methods enable automatic post-hoc stabilization of data despite brain movements during behavior and across days, allowing recording from the same neurons in the mouse visual cortex for over 2 months. Additionally, an optional configuration allows for recording from multiple sites per available channel, with a penalty to signal-to-noise ratio. These probes and algorithms enable stable recordings from >10,000 sites during free behavior in small animals such as mice.

neuroscience↗