Biology subjects

Harris, K. D.

Publications and source records attributed to Harris, K. D..

At least 19 recordsLinked to original sources

bioRxiv2026

Brainwide representation of navigation

Tracking your position in the environment during navigation requires integrating multiple signals into a spatial representation. This process is thought to occur primarily in the hippocampal formation, but evidence for it has also been observed in other regions. To investigate it across the brain, we recorded from over 20,000 neurons in mice navigating a virtual corridor that decoupled position from correlated signals. Spatial position was encoded in every region, and was more common in neurons tuned to visual landmarks. Most neurons brainwide were also modulated by running speed, likely reflecting arousal, and many by rewards. The hippocampal formation represented spatial position slightly more uniformly than other regions, but less precisely than visual cortex. Spatial position and other navigational signals are thus represented widely across the brain.

neuroscience↗

bioRxiv2026

A causal spatial role for apical dendrites in the cortex

Cortical pyramidal neurons project a long apical dendrite to layer 1 (L1), to receive synaptic inputs that are thought to provide contextual and possibly suppressive modulation. In visual cortex, these inputs are thought to drive or suppress the responses to large stimuli. Here, we demonstrate a causal role for individual apical dendrites in the awake cortex. We performed two-photon assisted dendritic pruning in individual L5 neurons of the mouse primary visual cortex and discovered that apical dendrites do not suppress the responses to large stimuli; rather, they drive the responses of neurons that prefer such stimuli. To establish the synaptic basis of this effect, we imaged the excitatory synaptic inputs targeting the apical dendrite. We found that these inputs cover an extensive region, which is largest in neurons that prefer large stimuli. These results reveal a clear spatial role for apical dendrites in the cortex. HighlightsO_LIOptical dendritic pruning in V1 reveals a causal role for L5 apical dendrites C_LIO_LIApical dendrites drive responses in neurons that prefer large visual stimuli C_LIO_LIImaging of synaptic inputs to apical dendrites reveals extended apical receptive field C_LIO_LIThe apical receptive field is larger in L5 neurons that prefer large visual stimuli C_LI

neuroscience↗

bioRxiv2026

Drivers of immune-related genetic variation across human populations

Pathogen-mediated disease burden imposes some of the strongest selective pressures on human populations, shaping genetic variation in immune-related genomic regions. Because historical, cultural, ecological, and environmental factors can influence disease burden, associations between these factors and immune-related genomic signatures can provide insight into how pathogen-mediated selection varied among populations. To investigate these associations and identify factors potentially contributing to historical disease burden, we analyzed worldwide variation in the Major Histocompatibility Complex (MHC). We evaluated the relationships between global patterns of genetic variation and a comprehensive set of historical-cultural, ecological, and climatic variables compiled in DisECCO (Disease, Ecological, Cultural, and Climatic Origins dataset). This dataset includes factors potentially associated with historical disease burden, including the timing of major cultural transitions, historical climatic conditions, exposure to domesticated animals, and pre-industrial pathogen stress. Contrary to genome-wide expectations, MHC diversity and variation were not well explained by geographic distance from Africa, suggesting that neutral demographic processes play a limited role in shaping MHC variation. Instead, MHC genetic variation was significantly associated with climatic variables, domestication exposure, and the time lag between the onset of the Neolithic and urbanization, with the strongest explanatory models identified for MHC Class II. These results indicate that cultural transitions and environmental conditions have played a central role in shaping immune-related genetic variation, and may have contributed substantially to changes in disease burden. Overall, our findings show that integrating ecological and cultural-historical variables with genomic data can help explain global patterns of genetic variation in humans.

evolutionary biology↗

bioRxiv2026

Contrasting effects of forest fragmentation on the genetics and microbiomes of an endangered arboreal primate

Landscape fragmentation, one of the leading drivers of biodiversity loss, can reshape both the genetics and microbiomes of wild populations. Although fragmentation is generally expected to limit gene flow and erode genetic diversity, and to disrupt host-associated microbial communities, these responses arise via different pathways and may therefore diverge within the same population. To understand how fragmentation simultaneously shapes population genetics and gut microbiomes, we analyzed fecal-derived host genomic and microbiome data from endangered, arboreal black howler monkeys (Alouatta pigra) across a fragmentation gradient. We then integrated these data with measures of ecological connectivity, habitat quality, and demography to identify the drivers of genetic and microbiome variation and structure. Multivariate analyses indicated that genetic patterns were shaped by both connectivity and habitat quality, whereas microbiome variation was driven mainly by habitat quality. Contrary to expectations under reduced realized connectivity with increasing isolation, monkeys showed the strongest gene flow signal in the most fragmented region, and higher genetic diversity and lower inbreeding than monkeys in continuous forest. Relatedness and isolation-by-distance patterns suggested that fragmentation has sex-specific effects on movement, disrupting the usual pattern of short-range male dispersal in the most fragmented region. Gut microbiomes, however, showed predicted negative responses to fragmentation: individuals in highly fragmented habitat had lower microbial diversity and compositional shifts consistent with lower-quality diets and increased exposure to disturbed environments. These results show contrasting biological responses to fragmentation within a single population, with genetic patterns likely resulting from compensatory behavioral flexibility and microbiome patterns reflecting local habitat degradation. Our findings underscore the need for conservation assessments that integrate multiple dimensions of population health rather than relying on any single indicator of fragmentation impact.

ecology↗

bioRxiv2026

The mouse superior colliculus promotes competing actions independently of sensory inputs

In many circumstances the brain makes decisions according to logistic classification: summing weighted factors in favor of each option and choosing an option according to a softmax function. Here we find that the superior colliculus (SC) contributes additively to this computation, promoting competing options independently of stimuli. We trained mice to locate an audiovisual stimulus on their left or right, and found that SC encodes the auditory, visual, and action signals in largely separate neurons in different layers. Unilateral SC inactivation did not affect sensory sensitivity but reduced contralateral actions, mostly in favor of inaction. The effects of bilateral inactivation were additive, with balanced inactivations restoring left-right balance. A simple 3-option logistic classification model captured the results, with sensory and prefrontal cortex processing sensory signals and each side of SC additively promoting the contralateral action while slightly opposing the ipsilateral action. The SC thus simultaneously promotes all competing eligible actions.

neuroscience↗

bioRxiv2026

Spatially resolved transcriptomic identification of thousands of neurons recorded in vivo.

Transcriptomics has transformed our understanding of the brain, but assigning transcriptomic identities to neurons recorded in vivo remains challenging at scale. Existing platforms can pair transcriptomic identity with two-photon calcium imaging in small populations of approximately 100 neurons, but they require recorded cells to be sparse and therefore cannot be applied to large population recordings. Here, we present coppaFISH 3D, a spatially resolved transcriptomics method, and CASTalign, an in silico alignment framework, which together enable transcriptomic identification of thousands of simultaneously recorded cells. coppaFISH 3D detects hundreds of genes in thick 50m fixed sections while preserving tissue integrity, enabling both 3D registration to in vivo imaging and integration with immunofluorescence labelling. The platform is fully powered by open chemistry and open source software, runs on commodity hardware, and can be performed at very low cost per section. It therefore enables transcriptomic identification of recorded neurons at scale, making it possible to study how transcriptomic identity shapes activity in neural populations.

neuroscience↗

bioRxiv2026

Optimal release of gene drives in population connectivity networks

Gene drives, genetic constructs that can spread deleterious alleles in wild populations, have the potential to address some of the major pressing challenges of the Anthropocene such as invasive species, spread of disease vectors, and agricultural pests. However, responsible and effective deployment of gene drive requires taking into account the complex nature of real-world population connectivity networks. In particular, it is unclear how the topological position of the deployment site affects the spread process and its final outcome. Here we develop a framework for modeling gene drive spread in population connectivity networks, and study the eco-evolutionary dynamics of gene drive spread under complex population structures. We investigated the relationship between the position of the deployment site in the topology of the network and whether the gene drive is eventually lost, fixed, or maintained at an intermediate frequency. We identified network centrality measures of deployment sites that are highly correlated with the outcome of deployment for different gene drive designs and across diverse network topologies. We also show that there is a trade-off between the time-to-fixation and the final outcome, implying that multiple centrality measures of the deployment site would need to be considered when aiming to achieve rapid and successful population control using gene drives.

ecology↗

bioRxiv2026

The basal ganglia transform visual identity into behavioral relevance

The basal ganglia are thought to be essential for transforming sensory signals into appropriate actions, yet it remains unclear how this transformation unfolds across their nuclei. We recorded neuronal activity across the striatum, globus pallidus external (GPe), and substantia nigra pars reticulata (SNr) in mice that were either naive or trained in visuomotor associations. In naive mice, visual stimuli drove responses in all three nuclei. These responses were feature-selective in the striatum but less so in GPe and SNr. Task training potentiated visual responses across the circuit, particularly for stimuli associated with movement. Training affected nuclei differently: while the striatum continued to distinguish between stimuli associated with the same behavioral action, GPe and SNr did not, responding similarly to all "Go" cues while distinguishing them from "No-Go" cues. These results demonstrate that the basal ganglia progressively transform high-dimensional sensory representations into low-dimensional signals of behavioral relevance.

neuroscience↗

bioRxiv2026

DeepUnitMatch: tracking neurons across days in electrophysiology using Deep Neural Networks

To understand neural processes such as learning or memory, we need to track the activity of populations of neurons at the level of single spikes and across days. Here, we leverage deep neural networks to build DeepUnitMatch, a software that reliably tracks individual neurons in high-density electrophysiological recordings across weeks. DeepUnitMatch uses only the spike waveforms of the neurons, and not their spiking patterns, and outperforms current solutions.

neuroscience↗

bioRxiv2025

Invariant activity sequences across the mouse brain

Given the variety and plasticity of their circuits, different brain regions could potentially produce profoundly diverse patterns of population activity. Alternatively, the population activity could exhibit canonical patterns that are similar across the brain and stable over time. Here we show that a pattern observed in the cerebral cortex, where neurons fire in 10-100 ms sequences, is stable across weeks and ubiquitous across the brain. In mouse visual cortex, neurons respond to visual stimuli with fixed latency, forming a sequence. Similar sequences appeared during spontaneous activity: each neuron was coupled to the population with a characteristic strength and delay, which remained stable for weeks. Brainwide recordings revealed such sequences in every brain region. These results reveal a stable scaffold for neural activity across the brain.

neuroscience↗

bioRxiv2025

Characterizing neuronal population geometry with AI equation discovery

The activity of visual cortical neurons forms a population code representing image stimuli. There is, however, a discrepancy between our understanding of this code at the single-cell and population levels: direct measurements indicate the population code is high-dimensional, but established models of single-cell tuning give rise to low-dimensional codes. We reconciled this discrepancy by developing an AI science system to find a new parsimonious, interpretable equation for visual cortical orientation tuning. Candidate equations were expressed as short computer programs and evolved by Large Language Models (LLMs) using graphical diagnostics. The resulting equation not only improved single-cell fits, but also accurately modelled the population codes high-dimensional geometry. A novel parameter of the AI-discovered equation, which controls single-cell tuning smoothness, gives rise to high-dimensional population codes. The same parameter drives high-dimensional coding in head-direction cells, suggesting a common coding strategy across brain regions. We used this equation to hypothesize a circuit mechanism generating high-dimensional population codes, and to demonstrate the advantages of these codes in a simulated hyperacuity task. These results show that tuning smoothness has a key role in controlling population code geometry, and demonstrate how AI equation discovery can deliver interpretable models accelerating scientific understanding in neuroscience and beyond.

neuroscience↗

bioRxiv2025

Genomic signatures of increasing disease burden in recent prehistory

One of the strongest selection pressures experienced by human populations is that driven by diseases on immune-related genomic regions. While it has been hypothesized for some time that disease burdens increased with the shift to agricultural and urbanized lifestyles, direct evidence for this hypothesis is lacking. Here, we capitalize on the accumulation of ancient genomic data to study changes in disease burden in human populations over the past 12,000 years. We investigated changes in genetic diversity and balancing selection in two distinct geographical and cultural centers in Southwest Eurasia and Eastern Asia, and found that not only is the major histocompatibility complex (MHC) the genomic region with the most substantial increases in diversity in terms of enrichment of genes and rates of increase, but that the rates of changes for individual genes are highly correlated between the two cultural centers, indicating similar selection pressures. We identify periods of time with substantial peaks in MHC diversity increase that primarily correspond to periods with settlement intensification, increased connectivity, and the expansion of animal domestication, which suggest that the most intensive disease burden occurred following the transition to sedentary lifestyle but prior to urbanization. These findings demonstrate the potential of our approach in uncovering the interplay between cultural shifts and selection, and provide strong support to the hypothesis that the levels of disease burden have substantially increased in recent prehistory following changes in lifestyle, connectivity and the introduction of domesticated animals.

evolutionary biology↗

bioRxiv2025

Mapping the visual cortex with Zebra noise and wavelets

Studies of the early visual system often require characterizing the visual preferences of large populations of neurons. This task typically requires multiple stimuli such as sparse noise and drifting gratings, each of which probe only a limited set of visual features. Here we introduce a new dynamic stimulus with sharp-edged stripes called Zebra noise and a new analysis model based on wavelets, and show that in combination they are highly efficient for mapping multiple aspects of the visual preferences of thousands of neurons. We used two-photon calcium imaging to record the activity of neurons in the mouse visual cortex. Zebra noise elicited strong responses that were more repeatable than those evoked by traditional stimuli. The wavelet-based model captured the repeatable aspects of the resulting responses, providing measures of neuronal tuning for multiple stimulus features: position, orientation, size, spatial frequency, drift rate, and direction. The method proved efficient, requiring only 5 minutes of stimulus (repeated 3 times) to characterize the tuning of thousands of neurons across visual areas. In combination, the Zebra noise stimulus and the wavelet-based model provide a broadly applicable toolkit for the rapid characterization of visual representations, promising to accelerate future studies of visual function.

neuroscience↗

bioRxiv2025

Different representations in layer 2/3 and layer 5 excitatory neurons of the primary visual cortex

The cortex contains multiple types of excitatory neuron, differentiated primarily by their layer of residence. We recorded from neuronal populations in the mouse visual cortex using 2-photon calcium imaging or Neuropixels probes, and found that excitatory neurons in layer 2/3 (L2/3) and layer 5 (L5) differed in their encoding of visual vs. nonvisual signals, with L2/3 more strongly modulated by visual stimuli and L5 more strongly modulated by movement. Movement had opposite effects on population synchrony in the two layers: it desynchronizes L2/3, by abolishing spontaneous synchronous fluctuations that entrain that population, and synchronizes L5, where excitatory cells are less entrained by spontaneous synchronous fluctuations and more strongly correlated with movement itself. Spontaneous activity was lower-dimensional in L2/3 than L5, with L2/3 population activity dominated by a single dimension of overlap between spontaneous and stimulus-evoked subspaces. We conclude that excitatory neurons in different layers of the visual cortex carry different representations of visual and non-visual signals.

neuroscience↗

bioRxiv2025

Brainwide blood volume reflects opposing neural populations

The supply of blood to brain tissue is thought to depend on the overall neural activity in that tissue, and this dependence is thought to differ across brain regions and across brain states. Studies supporting these views, however, measured neural activity as a bulk quantity, and related it to blood supply following disparate events in different regions. Here we measure fluctuations in neuronal activity and blood volume across the mouse brain, and find that their relationship is consistent across brain states and brain regions but differs in two opposing brainwide neural populations. Functional Ultrasound Imaging (fUSI) revealed that whisking, a marker of arousal, is associated with brainwide fluctuations in blood volume. Simultaneous fUSI and Neuropixels recordings showed that neurons that increase vs. decrease activity with whisking have distinct hemodynamic response functions. Their summed contributions predicted blood volume across states. Brainwide Neuropixels recordings revealed that these two opposing populations coexist in the entire brain. Their differing contributions to blood volume largely explain the apparent differences in blood volume fluctuations across regions. The mouse brain thus contains two neural populations with opposite relation to brain state and distinct relationships to blood supply, which together account for brainwide fluctuations in blood volume.

neuroscience↗

bioRxiv2025

High-dimensional neuronal activity from low-dimensional latent dynamics: a solvable model

Computation in recurrent networks of neurons has been hypothesized to occur at the level of low-dimensional latent dynamics, both in artificial systems and in the brain. This hypothesis seems at odds with evidence from large-scale neuronal recordings in mice showing that neuronal population activity is high-dimensional. To demonstrate that low-dimensional latent dynamics and high-dimensional activity can be two sides of the same coin, we present an analytically solvable recurrent neural network (RNN) model whose dynamics can be exactly reduced to a low-dimensional dynamical system, but generates an activity manifold that has a high linear embedding dimension. This raises the question: Do low-dimensional latents explain the high-dimensional activity observed in mouse visual cortex? Spectral theory tells us that the covariance eigenspectrum alone does not allow us to recover the dimensionality of the latents, which can be low or high, when neurons are nonlinear. To address this indeterminacy, we develop Neural Cross-Encoder (NCE), an interpretable, nonlinear latent variable modeling method for neuronal recordings, and find that high-dimensional neuronal responses to drifting gratings and spontaneous activity in visual cortex can be reduced to low-dimensional latents, while the responses to natural images cannot. We conclude that the high-dimensional activity measured in certain conditions, such as in the absence of a stimulus, is explained by low-dimensional latents that are nonlinearly processed by individual neurons.

neuroscience↗

bioRxiv2025

Suite3D: Volumetric cell detection for two-photon microscopy

In two-photon imaging of neuronal activity it is increasingly common to acquire 3-dimensional volumes. However, these volumes are typically processed plane by plane, leading to uncorrected axial movement, duplicated cells across planes, reduced signal-to-noise ratio per cell, and missed cells. To overcome these limitations, we introduce Suite3D, a volumetric cell detection pipeline. Suite3D corrects for rigid and non-rigid 3D brain motion, both lateral and axial. It detects neurons using 3D correlation, improving detectability. Finally, it performs 3D segmentation, identifying cells across imaging planes. We validated Suite3D with data from conventional multi-plane microscopes and advanced volumetric microscopes, at multiple resolutions and in multiple brain regions, and with ground truth anatomical labelling. Suite3D successfully detected cells appearing on multiple imaging planes, improving cell detectability and signal quality, avoiding duplications, and running faster than a prior volumetric pipeline. Suite3D offers a powerful solution for analyzing volumetric two-photon data.

neuroscience↗

bioRxiv2025

AIRUS: a simple workflow for AI-assisted exploration of scientific data

The development "reasoning" large language models (LLMs) seems poised to transform data analysis in all fields of science. This note describes a simple, iterative workflow termed AIRUS (AI Research Under Supervision), that allows working scientists, including those who lack extensive AI or programming expertise, to immediately start taking advantage of these capabilities. In the proposed workflow, a researcher uses an LLM to generate hypotheses, produce code, interpret results, and refine its approach in repeated cycles, only intervening when necessary. This can be run by simple cut-and-paste of code and results between web-based LLMs and an online Jupyter notebook. We demonstrate the workflow with two examples: one analysis of synthetic data, and one analysis of data from the International Brain Lab.

neuroscience↗