Biology subjects

Schnitzer, M. J.

Publications and source records attributed to Schnitzer, M. J..

At least 19 recordsLinked to original sources

bioRxiv2026

Memory encoding reprograms neuronal transcriptional responses via durable chromatin remodeling

The mammalian brains long-term memory circuits integrate information from prior and new experiences. The medial prefrontal cortex (mPFC) has a crucial role in this process and can reliably store information for weeks to months in rodents and over years in humans. To maintain information over these extended timescales, the neural encoding of remote memories involves persistent synaptic, transcriptional, and epigenetic changes that outlast the more transient forms of molecular activation that occur in the initial minutes to hours of memory storage. However, whether these persistent effects include long-lasting changes to chromatin structure and whether chromatin states mainly reflect prior episodes of neural activation or retune transcriptional responses to future bouts of activation remain unknown. Here we show that mPFC neurons engaged during the initial formation of memory undergo progressive changes in chromatin accessibility over the first four weeks of memory storage, evincing long-lasting modifications to the genetic programs activated during subsequent memory retrieval. Our experiments involved genetic trapping and single-cell multiomic sequencing analyses of mouse mPFC engram neurons activated during contextual fear conditioning. In the absence of subsequent memory recall, memory storage-related changes to chromatin structure were modestly reflected in gene expression patterns at 7 and 28 days after fear conditioning. However, upon memory recall, the genetically trapped engram neurons executed distinct transcriptional programs from those of other neurons of the same genetic types, suggesting that chromatin rearrangements arising during remote memory storage alter the transcriptional control logic by which engram neurons respond to new experiences. These metaplastic changes to transcriptional programs preferentially affect gene-regulatory and post-transcriptional control mechanisms, show substantial enrichment for transcription factor motifs related to neural development and cell-state regulation, and downregulate the neurons transcriptional responses to future excitation. Thus, rather than merely preserving a molecular record of prior learning, chromatin architectural changes in engram neurons occur over timescales of weeks and appear, in part, to repurpose conserved regulatory machinery to dampen the extent to which these neurons will engage in further information storage. Based on these findings, we propose that chromatin structural changes provide a slow-timescale component of neural computation that reduces interference between the representations of different memories.

neuroscience↗

bioRxiv2026

A predictive theory of experimental design for inferring neural population geometry in large-scale recordings

Ongoing technological advances will lead to recordings with progressively increasing numbers of neurons, while trial counts may only increase modestly. The analysis of such large-scale data increasingly relies on extracting collective neural population geometry. These combined recording and analysis trends raise the fundamental need for a predictive theory of experimental design that can tell us how accurately we will be able to infer such geometry in future larger scale recordings with more neurons and trials, by extrapolating from past smaller recordings. We derive such a theory for the simplest and most widely used method for extracting population geometry: principal component analysis. Our theory can predict how the dimensionality of neural data will grow with more neurons and trials and how accurate and reliable neuronal correlations and individual neural modes of the population geometry will be. Importantly, we find a blessing of dimensionality in which recording more neurons allows population geometry to be inferred accurately with fewer trials. The need for fewer trials in larger recordings will allow for the design of new experiments with more diverse trial types. Moreover, we derive scaling laws for the performance of neural prediction, setting the stage for the derivation of scaling laws for foundation models in neuroscience. We successfully test our theory across diverse species and recording modalities.

neuroscience↗

bioRxiv2026

A geometric and dynamical theory of latent computations in biological neural networks

Many neural recordings have revealed low-dimensional sets of behaviorally relevant variables encoded within large-scale neural activity patterns. However, dimensionality reduction analyses alone cannot yield causal explanations for how networks stably implement computations that are resilient to the substantial variability of single neuron dynamics. Further, existing methods for dimensionality reduction often rely on simplifying assumptions about network structure that limit their applicability and explanatory power. To provide a theoretical framework describing the dynamics of low-dimensional computation in high-dimensional neural networks, here we introduce the concept of latent processing units (LPUs), which are architecture-agnostic computational elements operating within biological neural circuitry. Six theorems governing coding and computation by LPUs collectively provide explanations for a range of common biological findings: low-dimensional sets of coding variables can generate high-dimensional neural dynamics; many neurons have activity patterns that represent behaviorally relevant variables but exert little influence on downstream circuits; linear readouts of neural population activity commonly permit near-optimal decoding; the drift of neural representations is often substantial even while network computations remain intact. Overall, our treatment of LPUs, as enacted in network dynamics, unifies the geometric and dynamical views of neural computation under a joint framework and provides systems neuroscience with a causal account of how the brain executes reliable computations.

neuroscience↗

bioRxiv2026

Opioid- and NMDA-receptor-dependent neural plasticity mediates long-term analgesia from motor cortical stimulation

Exogenous opioids that activate -opioid receptors (MORs) in nociceptive circuits mediate transient pain relief lasting minutes to hours but have more limited utility for treating chronic pain. By comparison, electrical or magnetic stimulation of the motor cortex can induce pain relief lasting weeks, for which the underlying mechanisms have remained unclear. Here we report an unconventional role for endogenous opioidergic signaling in the rapid induction of long-lasting analgesia from motor cortical stimulation, which triggers opioid-peptide-dependent neural plasticity in the rostral ventromedial medulla (RVM), a key node in the brains descending pain control pathways. To dissect the circuit and cellular bases for these effects, we created a miniaturized, millimeter-sized device allowing focal, non-invasive transcranial magnetic stimulation (TMS) of the mouse motor cortex. In mice with chronic neuropathic pain, reflexive and affective pain behaviors diminished for 1-2 weeks after one session of TMS treatment. Chemogenetic and optogenetic manipulations showed that motor cortical layer 5 pyramidal neurons with axonal projections to the RVM mediated TMS-induced pain relief. High-density electrophysiological recordings revealed that TMS treatment shifted the balance of RVM activity between pain-ON and pain-OFF neurons to a state promoting greater suppression of pain. Genetic and neuropharmacological manipulations revealed that NMDA-receptor-dependent signaling and MOR activation by endogenous opioid peptides in the RVM jointly mediate the long-lasting analgesia induced by a transient bout of TMS. Strikingly, enkephalinase inhibition in the RVM during TMS treatment enhanced the amplitude and duration of analgesia, showing that transiently boosting endogenous opioidergic signaling during TMS increases analgesia-conferring plasticity. In accord, re-analyses of data from human subjects with chronic pain support the idea that opioid administration amplifies analgesia from motor cortical TMS. Overall, our results showcase miniaturized TMS devices as versatile tools for basic and translational neuroscience and detail a hybrid, long-range neural network and NMDA- and opioid-receptor-dependent plasticity mechanism for durable pain relief. These findings point the way to mechanistically grounded, synergistic neurostimulation and drug therapies for brain diseases and disorders that jointly target neural circuit and molecular signaling pathways.

neuroscience↗

bioRxiv2026

Low-dimensional Neural Codes Suppress Neuronal Noise and Extend the Working Memory Duration

Neurons are noisy computational substrates, yet large neural populations achieve reliable computation. What determines the maximal duration that a noisy population can sustain working memory? We study this question with recurrent networks subject to stochastic noise and present three theoretical results. First, networks suppress independent neuronal noise when activity lies on a low-dimensional latent manifold. Second, this structure induces correlated noise across neurons, limiting the downstream information that can be extracted. Third, these effects yield an analytical bound on working memory duration that scales linearly with network size. We test these predictions using large-scale neocortical recordings, and provide a behavioral signature in mice consistent with the theory. Overall, noise suppression constitutes a key functional benefit of low-dimensional neural coding, with which large populations sustain reliable working memory over extended timescales.

neuroscience↗

bioRxiv2026

Ultrasensitive voltage imaging reveals distinct electrical microdomains in neurons

For the brain to compute, electrical signals must propagate over the membranes of individual neurons, connecting synaptic inputs to synaptic outputs1. Complex neuronal morphologies coupled with the spatial organization of synaptic inputs and outputs enable diverse voltage transformations that underlie cell-type specific computations2,3. However, measuring these transformations in vivo has remained challenging, leaving a crucial gap in our mechanistic understanding of single neuron computation. Here, we develop ASAP7y, a genetically encoded voltage indicator with unprecedented subthreshold sensitivity and expanded excitation compatibility in both mice and flies. We leveraged ASAP7y combined with two-photon random-access microscopy to record sensory stimulus-evoked voltage dynamics with millisecond, subcellular, and subthreshold resolution along the neurites of individual neurons in Drosophila. We found remarkable heterogeneity in voltage propagation across cell-types, delineating a fundamental axis of electrical diversity. Leveraging a nanoscale EM reconstruction of the visual system4, we modeled the electrotonic properties of single neurons spanning 717 cell types, revealing how morphology shapes voltage transformations. Finally, we demonstrate that confined voltage propagation creates substrates for local computation, producing subcellular domains with distinct feature selectivity across multiple cell types. These results provide mechanistic insight into how critical single neuron computations arise and reveal parallel processing in single neurons.

neuroscience↗

bioRxiv2026

Multimodal alignments of in vivo imaging and spatial biology datasets at cellular resolution

Parallel revolutions in intravital microscopy and spatial biology techniques have respectively enabled large-scale recordings of cellular dynamics in live animals and multi-dimensional molecular profiling at single-cell resolution. However, due to the challenges of aligning data from different modalities at cellular resolution, these two transformational approaches have generally been applied on separate biological samples, stymying the ability to link activity patterns and molecular attributes in the same exact cells. To enable routine, multimodal investigations of cells in vivo dynamics and molecular content, we created TRU-FACT (Total Registration Under Functional Activity, Connectivity, and Transcriptomics), a broadly applicable experimental and computational pipeline for registering large populations of individual cells across intravital imaging and spatial biology datasets. The pipeline combines three key innovations: an optomechanical tissue handling and alignment method to parallelize specimen planes, a graph-theoretic method to register individual cells based on their geometric relationships to neighboring cells, and a statistical framework that provides for each cell an a posteriori probability of correct registration. We validated TRU-FACT with several preparations for imaging neural Ca2+ activity in cortical and deep brain areas in head-fixed and freely behaving mice, RNA-barcode-expressing viruses for labeling neural projections, and low- and high-plex spatial transcriptomic methods. In mice performing a skilled reaching task, TRU-FACT alignments revealed the movement-related signaling patterns of intratelencephalic, extratelencephalic, and striatum-, superior colliculus-, and thalamus-projecting motor cortical neurons. Overall, TRU-FACT constitutes a scalable, multimodal discovery platform that is applicable to diverse tissue-types and spatial biology techniques, thereby enabling multiscale analyses of many complex biological systems.

neuroscience↗

bioRxiv2026

Heterogeneous single-cell dynamics support stable population codes for objects in the mouse anterior cingulate cortex

Remembering object locations is crucial for survival, yet how the anterior cingulate cortex (ACC) encodes spatial features across repeated experiences has not been fully characterized. Using longitudinal calcium imaging in freely moving mice, we tracked excitatory ACC neurons while animals explored objects across multiple days. We demonstrate that the ACC employs a highly dynamic coding strategy: while the overall proportion of object-responsive neurons remains constant across sessions, the specific identities of these cells fluctuate, showing a continuous turnover alongside a small, stable core. This dynamic coding is modulated by behavior, with high-exploring mice exhibiting greater cellular stability. Crucially, population-level analyses reveal that stable spatial representations emerge from collective dynamics rather than fixed single-cell identities. Population decoding demonstrates that information becomes linearly separable and highly efficient at a coarser ensemble scale. Thus, the ACC achieves representational stability through emergent network organization despite persistent single-cell dynamics.

neuroscience↗

bioRxiv2025

Extracting task-relevant preserved dynamics from contrastive aligned neural recordings

Recent work indicates that low-dimensional dynamics of neural and behavioral data are often preserved across days and subjects. However, extracting these preserved dynamics remains challenging: high-dimensional neural population activity and the recorded neuron populations vary across recording sessions. While existing modeling tools can improve alignment between neural and behavioral data, they often operate on a per-subject basis or discretize behavior into categories, disrupting its natural continuity and failing to capture the underlying dynamics. We introduce Contrastive Aligned Neural DYnamics (CANDY), an end-to-end framework that aligns neural and behavioral data using rank-based contrastive learning, adapted for continuous behavioral variables, to project neural activity from different sessions onto a shared low-dimensional embedding space. CANDY fits a shared linear dynamical system to the aligned embeddings, enabling an interpretable model of the conserved temporal structure in the latent space. We validate CANDY on synthetic and real-world datasets spanning multiple species, behaviors, and recording modalities. Our results show that CANDY is able to learn aligned latent embeddings and preserved dynamics across neural recording sessions and subjects, and it achieves improved cross-session behavior decoding performance. We further show that the latent linear dynamical system generalizes to new sessions and subjects, achieving comparable or even superior behavior decoding performance to models trained from scratch. These advances enable robust cross-session behavioral decoding and offer a path towards identifying shared neural dynamics that underlie behavior across individuals and recording conditions. The code and two-photon imaging data of striatal neural activity that we acquired here are available at https://github.com/schnitzer-lab/CANDY-public.git.

neuroscience↗

bioRxiv2025

Hippocampal reactivation of planned trajectories is required for effective goal choice in an allocentric memory task

Hippocampal activity provides a representation of the world around us, as well as our position within that world. However, it is not known if and to what extent the chosen navigational reference frame can influence hippocampal representations during memory-based tasks, including those focused on future activity. Here, we develop and employ two naturalistic, carefully controlled variants of the everyday memory task to model the use of egocentric and allocentric coordinates in the same arena. By recording hippocampal neural activity through miniature microscopes in male rats performing each of the two tasks, we uncover differences in the representation of space, and in the features of memory-based action planning. By also deploying optogenetic inactivation during navigational decision making, we find that hippocampal representations observed during the planning phase are necessary for solving the allocentric, but not the egocentric version of the task. Overall, our findings reveal a functional link between non-local hippocampal representations and allocentric navigation.

neuroscience↗

bioRxiv2025

Super Recombinator (SuRe): An in vivo recombination system for scalable and efficient transgene assembly at a single genomic locus

The capacity to engineer organisms with multiple transgenic components is crucial to synthetic biology and basic biology research. For the former field, transgenic organisms allow the creation of novel biological functions; for the latter, such organisms provide potent means of dissecting complex biological pathways. However, the size limitations of a single transgenesis event and challenges associated with the assembly of multiple DNA fragments hinder the efficient integration of multiple transgenes. To overcome these hurdles, here we introduce a building block for synthetic design termed an integrated genetic array (IGA), which incorporates all genetic components into a single locus to prevent their separation during genetic manipulations. Since the natural recombination rate for genes located in the same locus is near zero, to construct IGAs we developed the Super Recombinator (SuRe) system, which uses CRISPR/Cas9, alone or in combination with site-specific serine recombinases, for in vivo transgene recombination at a single genomic locus. SuRe effectively doubles the number of elements assembled in each recombination round, exponentially accelerating IGA construction. By preventing the separation of transgenic elements, SuRe greatly reduces screening burdens, as validated here through studies of Drosophila melanogaster and Caenorhabditis elegans. To optimize SuRe, we compared CRISPR/Cas9-induced homology-directed recombination to site-specific recombination using various serine recombinases. Optimized versions of SuRe achieved efficiency and fidelity values near their theoretical maxima and allowed the generation of recombinant products up to 4.2 Mbp in size in Drosophila. Using SuRe, we created fruit flies with 12 transgenic elements for fluorescence voltage imaging of neural activity in precisely defined cell-types. Mathematical modeling of the scalability of SuRe to large transgene assemblies showed that integration times and gene assembly workloads respectively scale logarithmically and linearly with the number of transgenes, both major improvements over conventional approaches. Overall, SuRe enables the efficient integration of multiple genes at individual loci, up to the chromosomal scale.

synthetic biology↗

bioRxiv2025

Striatal neural ensemble codes for voluntary locomotor and involuntary dyskinetic movements

BackgroundIn the classical model of basal ganglia circuitry, striatal spiny projection neurons of the direct and indirect pathways (dSPNs, iSPNs) promote and suppress movement, respectively, and exhibit unbalanced activity levels during hypokinetic or hyperkinetic conditions. Most therapies for these conditions are thought to work by rebalancing the relative activity of dSPNs and iSPNs towards normal levels. However, the mechanism of amantadine, which uniquely improves both hypokinetic and hyperkinetic conditions, is poorly understood. ObjectiveTo determine whether amantadine restores motor function by normalizing the balance of dSPN and iSPN activity or through a distinct mechanism. MethodsWe used dual-color two-photon Ca2+ imaging in the 6-OHDA mouse model of Parkinsons disease to concurrently monitor dSPN and iSPN dynamics across healthy, hypokinetic (parkinsonian), and hyperkinetic (dyskinetic) conditions. ResultsWe evaluated both dSPN/iSPN activity balance and action-specific neural ensemble activity in the dorsolateral striatum. In hypokinetic conditions, L-DOPA rescued the dSPN/iSPN imbalance but failed to restore the disrupted activity of the locomotion-specific ensemble. Conversely, amantadine improved locomotion-specific ensemble activity without normalizing the dSPN/iSPN imbalance. In hyperkinetic conditions, forelimb dyskinesias were characterized by neural activity patterns distinct from those encoding locomotion. Amantadine selectively suppressed the resting activity of forelimb dyskinesia ensembles without affecting locomotion-coding ensembles or restoring pathway balance. ConclusionsL-DOPA and amantadine may act through distinct mechanisms, with L-DOPA normalizing pathway balance and amantadine modulating action-specific neural ensembles. These findings support the importance of action-coding disruptions during hypokinetic and hyperkinetic conditions and suggest that correcting them can restore motor function.

neuroscience↗

bioRxiv2024

An autonomous robotic system for high-throughput phenotyping and behavioral studies of individual fruit flies

The fruit fly, Drosophila melanogaster, is a widely used model species in biomedical research. Despite its importance, conducting manual experiments with individual fruit flies can be challenging and time-consuming, especially for studies of individual fly behaviors. Such studies often involve cumbersome preparatory steps, such as manually tethering a fly and then positioning it within an experimental setup1,2. These procedures commonly require the fly to be anesthetized, and, before behavioral assessments begin, the fly must recover from anesthesia. Hence, the introduction of automated phenotyping and behavioral assays would expedite important aspects of fly research, by minimizing manual handling of flies and decreasing the net time needed for experiments. Here, we introduce FlyMAX (Fly Manipulation and Autonomous eXperimentation), an autonomous robotic system for manipulating adult flies without use of anesthesia. FlyMAX collects individual flies from a standard vial, analyzes them with computer vision, and achieves a throughput of >1,000 flies per day for high-throughput inspection and characterization assays. Robotic handling had no detectable adverse effects on fly longevity or our assessments of fly health. Moreover, the behavioral performance of flies, especially of males, was better and less variable than of flies handled manually. Our system employs deep learning-based machine vision for real-time assessments of picking quality and fly phenotypes. This enables fully pipelined, autonomous experimentation for behavioral assays with individual flies in controlled environments, which was previously infeasible. Overall, FlyMAX constitutes a promising technology to enhance the efficiency and reproducibility of research with flies and other insects in fields such as genetics, neuroscience, and drug screening.

neuroscience↗

bioRxiv2024

ActSort: An active-learning accelerated cell sorting algorithm for large-scale calcium imaging datasets

Recent advances in calcium imaging enable simultaneous recordings of up to a million neurons in behaving animals, producing datasets of unprecedented scales. Although individual neurons and their activity traces can be extracted from these videos with automated algorithms, the results often require human curation to remove false positives, a laborious process called cell sorting. To address this challenge, we introduce ActSort, an active-learning algorithm for sorting large-scale datasets that integrates features engineered by domain experts together with data formats with minimal memory requirements. By strategically bringing outlier cell candidates near the decision boundary up for annotation, ActSort reduces human labor to about 1-3% of cell candidates and improves curation accuracy by mitigating annotator bias. To facilitate the algorithms widespread adoption among experimental neuroscientists, we created a user-friendly software and conducted a first-of-its-kind benchmarking study involving about 160,000 annotations. Our tests validated ActSorts performance across different experimental conditions and datasets from multiple animals. Overall, ActSort addresses a crucial bottleneck in processing large-scale calcium videos of neural activity and thereby facilitates systems neuroscience experiments at previously inaccessible scales.

neuroscience↗

bioRxiv2024

Imaging high-frequency voltage dynamics in multiple neuron classes of behaving mammals

Fluorescent genetically encoded voltage indicators report transmembrane potentials of targeted cell-types. However, voltage-imaging instrumentation has lacked the sensitivity to track spontaneous or evoked high-frequency voltage oscillations in neural populations. Here we describe two complementary TEMPO voltage-sensing technologies that capture neural oscillations up to [~]100 Hz. Fiber-optic TEMPO achieves [~]10-fold greater sensitivity than prior photometry systems, allows hour-long recordings, and monitors two neuron-classes per fiber-optic probe in freely moving mice. With it, we uncovered cross-frequency-coupled theta- and gamma-range oscillations and characterized excitatory-inhibitory neural dynamics during hippocampal ripples and visual cortical processing. The TEMPO mesoscope images voltage activity in two cell-classes across a [~]8-mm-wide field-of-view in head-fixed animals. In awake mice, it revealed sensory-evoked excitatory-inhibitory neural interactions and traveling gamma and 3-7 Hz waves in the visual cortex, and previously unreported propagation directions for hippocampal theta and beta waves. These technologies have widespread applications probing diverse oscillations and neuron-type interactions in healthy and diseased brains.

neuroscience↗

bioRxiv2021

Dual polarity voltage imaging reveals subthreshold dynamics and concurrent spiking patterns of multiple neuron-types

Genetically encoded fluorescent voltage indicators are ideally suited to reveal the millisecond-scale interactions among and between distinct, targeted cell populations. However, current indicator families lack the requisite sensitivity for in vivo multipopulation imaging. We describe high-performance green and red sensors, Ace-mNeon2 and VARNAM2, and their reverse response-polarity variants, pAce and pAceR. Our indicators enable 0.4-1 kHz voltage recordings from >50 neurons per field-of-view in awake mice and [~]30-min continuous imaging in flies. Using dual-polarity multiplexed imaging, we uncovered behavioral state-dependent interactions between distinct neocortical subclasses, as well as contributions to hippocampal field potentials from non-overlapping projection neuronal ensembles. By combining three mutually compatible indicators, we demonstrate concurrent triple-population voltage imaging. Our approach will empower investigations of the dynamic interplay between neuronal subclasses at single-spike resolution. One Sentence SummaryA new suite of voltage sensors enables simultaneous cellular-resolution activity imaging from multiple, targeted neuron-types in awake animals.

neuroscience↗

bioRxiv2021

Fast and statistically robust cell extraction from large-scale neural calcium imaging datasets

State-of-the-art Ca2+ imaging studies that monitor large-scale neural dynamics can produce video datasets that tally up to [~]100 TB in size ([~]10 days transfer over 1 Gbit/s ethernet). Processing such data volumes requires automated, general-purpose and fast computational methods for cell identification that are robust to a wide variety of noise sources. We present EXTRACT, an algorithm that is based on robust estimation theory and uses graphical processing units (GPUs) to extract neural dynamics from a typical Ca2+ video in computing times up to [~]10-times faster than imaging durations. We extensively validated EXTRACT on simulated and experimental data and processed 199 public datasets ([~]12 TB) from the Allen Institute in a day. Showcasing its superiority over past cell extraction methods at removing noise contaminants, neural activity traces from EXTRACT allow more accurate decoding of animal behavior. Overall, EXTRACT is a powerful computational tool matched to the present challenges of neural Ca2+ imaging studies in behaving animals.

neuroscience↗

bioRxiv2019

Interhemispheric gamma synchrony between parvalbumin interneurons supports behavioral adaptation

Organisms must learn novel strategies to adapt to changing environments. Synchrony, which enhances neuronal communication, might create dynamic brain states, facilitating such adaptation. Although synchronization is common in neural systems, its functional significance remains controversial. We studied the role of gamma-frequency (~40 Hz) synchronization, promoted by parvalbumin interneurons, in mice learning multiple new cue-reward associations. Voltage imaging revealed cell type-specific increases of interhemispheric gamma synchrony within prefrontal parvalbumin interneurons, when mice received feedback that previously-learned associations were no longer valid. Disrupting this synchronization by delivering out-of-phase optogenetic stimulation caused mice to perseverate on outdated associations, an effect not reproduced by stimulating in-phase or out-of-phase at other frequencies. Gamma synchrony was specifically required when new associations utilized familiar cues that were previously irrelevant to behavioral outcomes, not when associations involved novel cues, or for reversing previously learned associations. Thus, gamma synchrony is indispensable for reappraising the behavioral salience of external cues.

neuroscience↗