Biology subjects

Svoboda, K.

Publications and source records attributed to Svoboda, K..

At least 19 recordsLinked to original sources

bioRxiv2026

3D-MAESTRO: A scalable, modular, portable pipeline for automated processing of large-scale volumetric brain microscopy data

Light microscopy is routinely used to explore the cellular and molecular structure of tissues, but the scale and complexity of data remains a bottleneck for discovery. We introduce 3D-MAESTRO (3D-Microscopy Automation and Execution with Scalable Tools, Rendering, and Orchestration): an automated image processing workflow for large-scale microscopy data, built for scalable execution across cloud and local computing environments. 3D-MAESTRO orchestrates denoising, stitching of image tiles, atlas registration, and segmentation. Its modular architecture permits the integration and benchmarking of new packages, ensuring that performance evolves as more efficient or accurate algorithms emerge. We introduce a new 3D image template for automated registration of mouse brains cleared with aqueous reagents and an efficient method for detection of fluorescent cells. We apply 3D-MAESTRO to lightsheet images of whole mouse brains in the context of diverse anatomical and functional experiments, illustrating high-throughput and reproducible mapping of microscopic structures across the brain.

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bioRxiv2026

GIAnT: a Glutamate Imaging Analysis Toolbox

Recent advances in fluorescent indicators and optical microscopy now enable in vivo synaptic imaging of glutamate, which transmits the majority of signals between neurons in the brain. Extracting fluorescence signals from these recordings is complicated by the minuscule scale and dense clustering of synapses on dendrites, as well as brain motion in behaving animals. Here we present the Glutamate Imaging Analysis Toolbox (GIAnT), a set of automated tools for glutamate imaging data that corrects sample motion, identifies active synapses with super-resolution precision, and extracts synaptic fluorescence signals. Compared to methods designed for cellular imaging, GIAnT reduces motion artifacts, more accurately identifies active synapses, and improves extracted signal quality by reducing contamination from overlapping synapses. By pairing in vivo glutamate imaging with post hoc expansion microscopy, we find that >70% of the putative synapses extracted using GIAnT matched one-to-one with glutamatergic synapses onto the postsynaptic cell. Our results establish GIAnT as an automated and validated pipeline for processing synaptic glutamate imaging data at scale.

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bioRxiv2026

Functional reorganization of motor cortex connectivity during learning

Learning new tasks requires the brain to reshape the flow of neural activity, but how these changes arise from dynamic neural connectivity remains unclear. Here, we used two-photon photostimulation and calcium imaging to map learning-related changes in connectivity in layer 2/3 of mouse motor cortex, induced by learning of an optical brain-computer interface (BCI) task. Mice rapidly (within minutes) learned to change activity in a conditioned neuron to earn rewards. Activity changes were sparse; the conditioned neuron increased activity more than surrounding neurons. Mapping connectivity before and after learning revealed changes in motor cortex connectivity, enriched in neurons that were active before trial initiation, analogous to motor cortex populations that are active preceding movement. Motor cortex plasticity reroutes preparatory activity to neurons that are active later and control the conditioned neuron. Our findings show how rapid learning can be achieved through structured changes in motor cortex connectivity.

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bioRxiv2025

Neural dynamics outside task-coding dimensions drive decision trajectories through transient amplification

Linking neural activity to behavior typically involves identifying activity subspaces that encode task information, such as stimuli, memory, and choices. However, it is unclear whether activity in these "coding dimensions" drives behavior or merely reflects the underlying computations. Neural activity in other "residual dimensions" is typically ignored. We developed a recurrent neural network that explicitly models interactions between coding and residual subspaces. Applied to multi-regional Neuropixels recordings from mice performing a delayed movement task, the model reveals that perturbations of residual dimensions reliably alter behavior, whereas perturbations of the choice dimension, which encodes the animals decision, are less effective. These effects arise because residual dimensions drive amplification across an intermediate number ([~]10) of dimensions, before the dynamics settle into discrete attractors corresponding to choice. Our findings show that neural activity previously considered task-irrelevant can have critical roles in driving behavior.

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bioRxiv2025

Efficient and reproducible pipelines for spike sorting large-scale electrophysiology data

The scale of in vivo electrophysiology has expanded in recent years, with simultaneous recordings across thousands of electrodes now becoming routine. These advances have enabled a wide range of discoveries, but they also impose substantial computational demands. Spike sorting, the procedure that extracts spikes from extracellular voltage measurements, remains a major bottleneck: a dataset collected in a few hours can take days to spike sort on a single machine, and the field lacks rigorous validation of the many spike sorting algorithms and preprocessing steps that are in use. Advancing the speed and accuracy of spike sorting is essential to fully realize the potential of large-scale electrophysiology. Here, we present an end-to-end spike sorting pipeline that leverages parallelization to scale to large datasets. The same workflow can run reproducibly on individual workstations, high-performance computing clusters, or cloud environments, with computing resources tailored to each processing step to reduce costs and execution times. In addition, we introduce a benchmarking pipeline, also optimized for parallel processing, that enables systematic comparison of multiple sorting pipelines. Using this framework, we show that Kilosort4, a widely used spike sorting algorithm, outperforms Kilosort2.5 (Pachitariu et al. 2024). We also show that 7x lossy compression, which substantially reduces the cost of data storage, has minimal impact on spike sorting performance. Together, these pipelines address the urgent need for scalable and transparent spike sorting of electrophysiology data, preparing the field for the coming flood of multi-thousand-channel experiments.

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bioRxiv2025

Exploring the correspondence between gene expressionand thalamic nuclei using the THALMANAC resource

The thalamus connects the sensory organs and major subcortical brain regions with the neocortex. The thalamus has long been divided into multiple discrete nuclei, based on cytoarchitecture, histochemical stains, and mesoscale connectivity. However, thalamic nuclei do not completely describe thalamic organization. For example, some boundaries between thalamic nuclei are disputed, whereas other nuclei are known to contain subdomains with distinct connectivity and function. Moreover, the correspondence between cellular gene expression and other properties of thalamic projection neurons remains to be established. Spatial analysis of single cell gene expression provides a basis for reevaluating thalamic organization. We present the THALMANAC, (THALamusMERFISH ANalysis and ACcess) a Findable, Accessible, Interoperable, Reusable and Reproducible (FAIRR) resource for exploring and analyzing single-cell transcriptomic variation in the thalamus. The THALMANAC provides streamlined access to thalamic gene expression data registered to the common coordinate framework and tools for quantitative analysis and visualization of these data, all encapsulated in a reproducible, cloud computing platform. Using this resource, we find that gene expression generally supports the parcellation of thalamus into distinct nuclei. Some nuclei, such as the anteromedial nucleus, are additionally composed of discrete subdomains, while other nuclei share patterns of gene expression or are arrayed on a spatial gradient of gene expression. The THALMANAC establishes spatial transcriptomic data as a foundation for delineating thalamic organization. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=124 SRC="FIGDIR/small/679413v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1d517fcorg.highwire.dtl.DTLVardef@119dccdorg.highwire.dtl.DTLVardef@ef2cd6org.highwire.dtl.DTLVardef@68adac_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2025

Glutamate indicators with increased sensitivity and tailored deactivation rates

Identifying the input-output operations of neurons requires measurements of synaptic transmission simultaneously at many of a neurons thousands of inputs in the intact brain. To facilitate this goal, we engineered and screened 3365 variants of the fluorescent protein glutamate indicator iGluSnFR3 in neuron culture, and selected variants in the mouse visual cortex. Two variants have high sensitivity, fast activation (< 2 ms) and deactivation times tailored for recording large populations of synapses (iGluSnFR4s, 153 ms) or rapid dynamics (iGluSnFR4f, 26 ms). By imaging action-potential evoked signals on axons and visually-evoked signals on dendritic spines, we show that iGluSnFR4s/4f primarily detect local synaptic glutamate with single-vesicle sensitivity. The indicators detect a wide range of naturalistic synaptic transmission, including in the vibrissal cortex layer 4 and in hippocampal CA1 dendrites. iGluSnFR4 increases the sensitivity and scale (4s) or speed (4f) of tracking information flow in neural networks in vivo.

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bioRxiv2025

Spike inference from calcium imaging data acquired with GCaMP8 indicators

For neuroscience to be reproducible, its key methods must be quantitative and interpretable. Calcium imaging is such a key method, but it records neuronal activity only indirectly and is therefore difficult to interpret. These difficulties arise from the kinetics, nonlinearity, and sensitivity of calcium indicators, but also from the methods used for signal analysis. Here, we evaluate how methods for spike inference can be optimized to interpret data with the recently developed calcium indicator GCaMP8. We find that the indicator linearity of GCaMP8 enables more accurate recovery of high-frequency spiking events and the detection of single action potentials. Ground truth recordings from mouse neocortex show that the most linear calcium variants, GCaMP8s and GCaMP8m - but not GCaMP6, GCaMP7f, or GCaMP8f - robustly detect isolated spikes in pyramidal neurons under realistic noise conditions. In addition, we fine-tune and benchmark existing algorithms for spike inference (CASCADE, OASIS, MLSpike) with GCaMP8 data, investigate spike inference for interneurons, and demonstrate how the fast rise time of GCaMP8 enables low-latency real-time detection of neuronal activity. Together, our study provides tools and guidelines to optimally process calcium signals with GCaMP8 and highlights the key role of linearity in improving the interpretability of calcium imaging.

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bioRxiv2025

Neuropixels Opto: Combining high-resolution electrophysiology and optogenetics

High-resolution extracellular electrophysiology is the gold standard for recording spikes from distributed neural populations, and is especially powerful when combined with optogenetics for manipulation of specific cell types with high temporal resolution. We integrated these approaches into prototype Neuropixels Opto probes, which combine electronic and photonic circuits. These devices pack 960 electrical recording sites and two sets of 14 light emitters onto a 70 m wide, 1 cm long shank, allowing spatially addressable optogenetic stimulation with blue and red light. In mouse cortex, Neuropixels Opto probes delivered high-quality recordings together with spatially addressable optogenetics, differentially activating or silencing neurons at distinct cortical depths. In mouse striatum and other deep structures, Neuropixels Opto probes delivered efficient optotagging, facilitating the identification of two cell types in parallel. Neuropixels Opto probes represent an unprecedented tool for recording, identifying, and manipulating neuronal populations.

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bioRxiv2025

A brainstem map of orofacial rhythms

Rhythmic orofacial movements, such as eating, drinking, or vocalization, are controlled by distinct premotor oscillator networks in the brainstem. Orofacial movements must be coordinated with rhythmic breathing to avoid aspiration and because they share muscles. Understanding how brainstem circuits coordinate rhythmic motor programs requires neurophysiological measurements in behaving animals. We used Neuropixels probe recordings to map brainstem neural activity related to breathing, licking, and swallowing in mice drinking water. Breathing and licking rhythms were tightly coordinated and phase-locked, whereas intermittent swallowing paused breathing and licking. Multiple clusters of neurons, each recruited during different orofacial rhythms, delineated a lingual premotor network in the intermediate nucleus of the reticular formation (IRN). Local optogenetic perturbation experiments identified a region in the IRN where constant stimulation can drive sustained rhythmic licking, consistent with a central pattern generator for licking. Stimulation to artificially induce licking showed that coupled brainstem oscillators autonomously coordinated licking and breathing. The brainstem oscillators were further patterned by descending inputs at moments of licking initiation. Our results reveal the logic governing interactions of orofacial rhythms during behavior and outline their neural circuit dynamics, providing a model for dissecting multi-oscillator systems controlling rhythmic motor programs.

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bioRxiv2023

Connectivity underlying motor cortex activity during naturalistic goal-directed behavior

Neural representations of information are shaped by local network interactions. Previous studies linking neural coding and cortical connectivity focused on stimulus selectivity in the sensory cortex 1-4. Here we study neural activity in the motor cortex during naturalistic behavior in which mice gathered rewards with multidirectional tongue reaching. This behavior does not require training and thus allowed us to probe neural coding and connectivity in motor cortex before its activity is shaped by learning a specific task. Neurons typically responded during and after reaching movements and exhibited conjunctive tuning to target location and reward outcome. We used an all-optical 5,4,6,7 method for large-scale causal functional connectivity mapping in vivo. Mapping connectivity between > 20,000,000 excitatory neuronal pairs revealed fine-scale columnar architecture in layer 2/3 of the motor cortex. Neurons displayed local (< 100 {micro}m) like-to-like connectivity according to target-location tuning, and inhibition over longer spatial scales. Connectivity patterns comprised a continuum, with abundant weakly connected neurons and sparse strongly connected neurons that function as network hubs. Hub neurons were weakly tuned to target-location and reward-outcome but strongly influenced neighboring neurons. This network of neurons, encoding location and outcome of movements to different motor goals, may be a general substrate for rapid learning of complex, goal-directed behaviors.

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bioRxiv2023

Feedforward amplification in recurrent networks underliesparadoxical neural coding

The activity of single neurons encodes behavioral variables, such as sensory stimuli (Hubel & Wiesel 1959) and behavioral choice (Britten et al. 1992; Guo et al. 2014), but their influence on behavior is often mysterious. We estimated the influence of a unit of neural activity on behavioral choice from recordings in anterior lateral motor cortex (ALM) in mice performing a memory-guided movement task (H. K. Inagaki et al. 2018). Choice selectivity grew as it flowed through a sequence of directions in activity space. Early directions carried little selectivity but were predicted to have a large behavioral influence, while late directions carried large selectivity and little behavioral influence. Consequently, estimated behavioral influence was only weakly correlated with choice selectivity; a large proportion of neurons selective for one choice were predicted to influence choice in the opposite direction. These results were consistent with models in which recurrent circuits produce feedforward amplification (Goldman 2009; Ganguli et al. 2008; Murphy & Miller 2009) so that small amplitude signals along early directions are amplified to produce low-dimensional choice selectivity along the late directions, and behavior. Targeted photostimulation experiments (Daie et al. 2021b) revealed that activity along the early directions triggered sequential activity along the later directions and caused predictable behavioral biases. These results demonstrate the existence of an amplifying feedforward dynamical motif in the motor cortex, explain paradoxical responses to perturbation experiments (Chettih & Harvey 2019; Daie et al. 2021b; Russell et al. 2019), and reveal behavioral relevance of small amplitude neural dynamics.

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bioRxiv2023

Not everything, not everywhere, not all at once: a study of brain-wide encoding of movement

Activity related to movement is found throughout sensory and motor regions of the brain. However, it remains unclear how movement-related activity is distributed across the brain and whether systematic differences exist between brain areas. Here, we analyzed movement related activity in brain-wide recordings containing more than 50,000 neurons in mice performing a decision-making task. Using multiple techniques, from markers to deep neural networks, we find that movement-related signals were pervasive across the brain, but systematically differed across areas. Movement-related activity was stronger in areas closer to the motor or sensory periphery. Delineating activity in terms of sensory- and motor-related components revealed finer scale structures of their encodings within brain areas. We further identified activity modulation that correlates with decision-making and uninstructed movement. Our work charts out a largescale map of movement encoding and provides a roadmap for dissecting different forms of movement and decision-making related encoding across multi-regional neural circuits.

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bioRxiv2023

Expansion-assisted selective plane illumination microscopy for nanoscale imaging ofcentimeter-scale tissues

Recent advances in tissue processing, labeling, and fluorescence microscopy are providing unprecedented views of the structure of cells and tissues at sub-diffraction resolutions and near single molecule sensitivity, driving discoveries in diverse fields of biology, including neuroscience. Biological tissue is organized over scales of nanometers to centimeters. Harnessing molecular imaging across intact, three-dimensional samples on this scale requires new types of microscopes with larger fields of view and working distance, as well as higher throughput. We present a new expansion-assisted selective plane illumination microscope (ExA-SPIM) with aberration-free 1.5 {micro}mx1.5 {micro}mx3 {micro}m optical resolution over a large field of view (10.6x8.0 mm2) and working distance (35 mm) at speeds up to 946 megavoxels/sec. Combined with new tissue clearing and expansion methods, the microscope allows imaging centimeter-scale samples with 375 nm lateral and 750 nm axial resolution (4x expansion), including entire mouse brains, with high contrast and without sectioning. We illustrate ExA-SPIM by reconstructing individual neurons across the mouse brain, imaging cortico-spinal neurons in the macaque motor cortex, and visualizing axons in human white matter.

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bioRxiv2023

Compression strategies for large-scale electrophysiology data

AbstractWith the rapid adoption of high-density electrode arrays for recording neural activity, electrophysiology data volumes within labs and across the field are growing at unprecedented rates. For example, a one-hour recording with a 384-channel Neuropixels probe generates over 80 GB of raw data. These large data volumes carry a high cost, especially if researchers plan to store and analyze their data in the cloud. Thus, there is a pressing need for strategies that can reduce the data footprint of each experiment. Here, we establish a set of benchmarks for comparing the performance of various compression algorithms on experimental and simulated recordings from Neuropixels 1.0 (NP1) and 2.0 (NP2) probes. For lossless compression, audio codecs (FLAC and WavPack) achieve compression ratios 6% higher for NP1 and 10% higher for NP2 than the best general-purpose codecs, at the expense of a slower decompression speed. For lossy compression, the WavPack algorithm in "hybrid mode" increases the compression ratio from 3.59 to 7.08 for NP1 and from 2.27 to 7.04 for NP2 (compressed file size of[~] 14% for both types of probes), without adverse effects on spike sorting accuracy or spike waveforms. Along with the tools we have developed to make compression easier to deploy, these results should encourage all electrophysiologists to apply compression as part of their standard analysis workflows.

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bioRxiv2023

Brain-wide neural activity underlying memory-guided movement

Behavior requires neural activity across the brain, but most experiments probe neurons in a single area at a time. Here we used multiple Neuropixels probes to record neural activity simultaneously in brain-wide circuits, in mice performing a memory-guided directional licking task. We targeted brain areas that form multi-regional loops with anterior lateral motor cortex (ALM), a key circuit node mediating the behavior. Neurons encoding sensory stimuli, choice, and actions were distributed across the brain. However, in addition to ALM, coding of choice was concentrated in subcortical areas receiving input from ALM, in an ALM-dependent manner. Choice signals were first detected in ALM and the midbrain, followed by the thalamus, and other brain areas. At the time of movement initiation, choice-selective activity collapsed across the brain, followed by new activity patterns driving specific actions. Our experiments provide the foundation for neural circuit models of decision-making and movement initiation.

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bioRxiv2022

BCI learning phenomena can be explained by gradient-based optimization

Brain-computer interface (BCI) experiments have shown that animals are able to adapt their recorded neural activity in order to receive reward. Recent studies have highlighted two phenomena. First, the speed at which a BCI task can be learned is dependent on how closely the required neural activity aligns with pre-existing activity patterns: learning "out-of-manifold" tasks is slower than "in-manifold" tasks. Second, learning happens by "re-association": the overall distribution of neural activity patterns does not change significantly during task learning. These phenomena have been presented as distinctive aspects of BCI learning. Here we show, using simulations and theoretical analysis, that both phenomena result from the simple assumption that behaviour and representations are improved via gradient-based algorithms. We invoke Occams Razor to suggest that this straightforward explanation should be preferred when accounting for these experimental observations.

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bioRxiv2022

Statistically unbiased prediction enables accurate denoising of voltage imaging data

Here we report SUPPORT (Statistically Unbiased Prediction utilizing sPatiOtempoRal information in imaging daTa), a self-supervised learning method for removing Poisson-Gaussian noise in voltage imaging data. SUPPORT is based on the insight that a pixel value in voltage imaging data is highly dependent on its spatially neighboring pixels in the same time frame, even when its temporally adjacent frames do not provide useful information for statistical prediction. Such spatiotemporal dependency is captured and utilized to accurately denoise voltage imaging data in which the existence of the action potential in a time frame cannot be inferred by the information in other frames. Through simulation and experiments, we show that SUPPORT enables precise denoising of voltage imaging data while preserving the underlying dynamics in the scene.

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