Biology subjects

Häusser, M.

Publications and source records attributed to Häusser, M..

6 recordsLinked to original sources

bioRxiv2026

High-throughput serial two-photon tomography with BakingTray and BrainSaw

Volumetric imaging is increasingly important in fields such as neuroscience, where quantifying structure and mapping features are inherently three-dimensional problems. Block-face serial two-photon tomography (STPT) has emerged as a powerful approach for high-resolution, whole-organ volumetric imaging. STPT avoids complex sample preparation and balances image quality, speed, and scalability. However, STPT remains niche, in part because of the absence of flexible, general-purpose control software. Here we present BakingTray, an open-source software platform for STPT that extends ScanImage. BakingTrays modular framework accommodates diverse hardware configurations and allows users to build systems customised to their needs. Twelve such systems, which we call BrainSaws, have been constructed worldwide since 2016, with more under construction, and have so far contributed to 35 research papers. The three systems at the Sainsbury Wellcome Centre alone have acquired over 7,000 samples in a busy multi-user facility. We demonstrate the platform across its core neuroscience applications: electrode track localisation, brain-wide cell counting, and bulk projection mapping. Beyond these, we map the cell-type-specific synaptic output of inhibitory neurons in the ventral lateral geniculate nucleus and, with whole-mount staining, amyloid plaques brain-wide in a mouse model of Alzheimers disease. BakingTray is a mature platform for groups or facilities needing a customised volumetric imaging solution, whether built new or converted cheaply from an existing two-photon microscope.

neuroscience↗

bioRxiv2026

Logic of brain-wide connections with the cerebellar cortex

The cerebellum governs movement and cognition through multiple lobules that carry distinct signals and connect to distinct long-range brain networks. How these specialized computations are reconciled with the brain-wide convergence required for coherent behaviour is unknown, in part because the input-output connectivity of functionally distinct lobules has not been mapped. We combined anterograde and retrograde transsynaptic viral tracing with whole-brain two-photon tomography to map the monosynaptic and disynaptic inputs and outputs of Lobule V (LV) and Simplex (LS), two lobules preferentially involved in movement and reward, respectively. Monosynaptic connectivity was strongly segregated: LV and LS received climbing fiber and mossy fiber inputs from largely non-overlapping source regions and projected to distinct targets within the cerebellar nuclei. In contrast, disynaptic inputs to LV- and LS-projecting olivary neurons arose from a shared set of forebrain, midbrain, and hindbrain regions, with the segregation re-emerging at the level of fine-scale spatial topography within these regions. Disynaptic outputs through the cerebellar nuclei likewise overlapped more than the monosynaptic projections to the nuclei themselves. These findings reveal that overlapping brain-wide pathways interface with locally specialized monosynaptic cerebellar pathways. This organizational logic could support distinct movement- and reward-related computations in the cerebellum while distributing outputs to shared downstream networks, providing an architectural substrate for both segregating and integrating cerebellar motor and cognitive functions.

neuroscience↗

bioRxiv2025

Fast photostimulus optimization for holographic control of neural ensemble activity in vivo

Determining the intricate structure and function of neural circuits requires the ability to precisely manipulate circuit activity. Two-photon holographic optogenetics has emerged as a powerful tool for achieving this via flexible excitation of user-defined neural ensembles. However, the precision of two-photon optogenetics has been constrained by off-target stimulation, an effect where proximal non-target neurons can be unintentionally activated due to imperfect spatial confinement of light onto target neurons. Here, we introduce a real-time computational method to mitigating off-target stimulation that first empirically samples each neurons sensitivity to stimulation at proximal locations, and then optimizes stimulation sites using a fast, interpretable model based on adaptive non-negative basis function regression (NBFR). NBFR is highly scalable, completing model fitting for hundreds of neurons in just a few seconds and then optimizing stimulation sites in several hundred milliseconds per stimulus - fast enough for most closed-loop behavioral experiments. We characterize the performance of our approach in both simulations and in vivo experiments in mouse hippocampus, showing its efficacy under realistic experimental conditions. Our results thus establish NBFR-based photostimulus optimization as an important addition to an emerging computational toolkit for precise yet scalable holographic optogenetics.

neuroscience↗

bioRxiv2024

A deep-learning strategy to identify cell types across species from high-density extracellular recordings

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but dont reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifiers predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously-recorded cell types during behavior.

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↗

bioRxiv2018

Dendritic NMDA receptors in parvalbumin neurons enable strong and stable neuronal assemblies

Parvalbumin-expressing (PV+) GABAergic interneurons mediate feedforward and feedback inhibition and have a key role in gamma oscillations and information processing. The importance of fast synaptic recruitment, action potential initiation and repolarization, and rapid synchronous GABA release by PV+ cells is well established. In contrast, the functional significance of PV+ cell NMDA receptors (NMDARs), which generate relatively slow postsynaptic currents, is unclear. Underlining their importance, several studies implicate PV+ cell NMDAR disruption in impaired network function and circuit pathologies. Here, we show that dendritic NMDARs underlie supralinear integration of feedback excitation from local pyramidal neurons onto mouse CA1 PV+ cells. Furthermore, by incorporating NMDARs at feedback connections onto PV+ cells in spiking networks, we show that these receptors enable cooperative recruitment of PV+ interneurons, strengthening and stabilising principal cell assemblies. Failure of this phenomenon provides a parsimonious explanation for cognitive and sensory gating deficits in pathologies with impaired PV+ NMDAR signalling.

neuroscience↗