Biology subjects

Harris, K. D.

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

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Adaptation shapes the representational geometry in mouse V1 to efficiently encode the environment

Sensory adaptation dynamically changes neural responses as a function of previous stimuli, profoundly impacting perception. The response changes induced by adaptation have been characterized in detail in individual neurons and at the population level after averaging across trials. However, it is not clear how adaptation modifies the aspects of the representations that relate more directly to the ability to perceive stimuli, such as their geometry and the noise structure in individual trials. To address this question, we recorded from a population of neurons in the mouse visual cortex and presented one stimulus (an oriented grating) more frequently than the others. We then analyzed these data in terms of representational geometry and studied the ability of a linear decoder to discriminate between similar visual stimuli based on the single-trial population responses. Surprisingly, the discriminability of stimuli near the adaptor increased, even though the responses of individual neurons to these stimuli decreased. This improvement arose mainly from how adaptation reshaped the geometry of the mean population responses, with changes in the noise structure playing a minor role. Locomotion modulated the population responses along a direction largely independent of adaptation, allowing the adaptation-induced increase in discriminability to persist during locomotion. Finally, artificial neural networks trained to reconstruct the visual stimulus under metabolic constraints reproduced the increase in discriminability and the decrease in responses. The paradoxical effects of adaptation are thus consistent with the efficient coding framework, allowing the brain to improve the representation of frequent stimuli while limiting the associated metabolic cost.

neuroscience↗

bioRxiv2024

Identification of Recurrent Dynamics in Distributed Neural Populations

Large-scale recordings of neural activity over broad anatomical areas with high spatial and temporal resolution are increasingly common in modern experimental neuroscience. Recently, recurrent switching dynamical systems have been used to tackle the scale and complexity of these data. However, an important challenge remains in providing insights into the existence and structure of recurrent linear dynamics in neural time series data. Here we test a scalable approach to time-varying autoregression with low-rank tensors to recover the recurrent dynamics in stochastic neural mass models with multiple stable attractors. We demonstrate that the sparse representation of time-varying system matrices in terms of temporal modes can recover the attractor structure of simple systems via clustering. We then consider simulations based on a human brain connectivity matrix in high and low global connection strength regimes, and reveal the hierarchical clustering structure of the dynamics. Finally, we explain the impact of the forecast time delay on the estimation of the underlying rank and temporal variability of the time series dynamics. This study illustrates that prediction error minimization is not sufficient to recover meaningful dynamic structure and that it is crucial to account for the three key timescales arising from dynamics, noise processes, and attractor switching.

neuroscience↗

bioRxiv2024

Dorsal prefrontal cortex drives perseverative behavior in mice

Perseveration - repeating one choice when others would generate larger rewards - is a common behavior, but neither its purpose nor neuronal mechanisms are understood. Here we demonstrate a neural correlate and causal role of dorsal prefrontal cortex, specifically anterior supplementary motor cortex (MOs) in perseveration in mice performing a dynamic reward learning task. An auditory go cue signaled mice to turn a wheel either left or right, with the reward probability of each action switching in blocks. Mice perseverated, gaining suboptimal reward, but were faster when making repeated choices. Neuropixels recordings found neurons whose activity correlated with perseveration and predicted rapid reaction times, almost exclusively in anterior MOs. Optogenetically inhibiting this region at choice time reduced perseveration and slowed reactions. In contrast, inactivating medial prefrontal cortex at choice time had no effect, but inactivating it after reward delivery impaired learning. In this task, therefore, anterior MOs reflects a perseverative decision variable, and is necessary for mediating the effect of this decision variable on choice and reaction time.

neuroscience↗

bioRxiv2024

Predicting the direction of phenotypic difference

Predicting phenotypes from genomic data is a key goal in genetics, but for most complex phenotypes, predictions are hampered by incomplete genotype-to-phenotype mapping. Here, we describe a more attainable approach than quantitative predictions, which is aimed at qualitatively predicting phenotypic differences. Despite incomplete genotype-to-phenotype mapping, we show that it is relatively easy to determine which of two individuals has a greater phenotypic value. This question is central in many scenarios, e.g., comparing disease risk between individuals, the yield of crop strains, or the anatomy of extinct vs extant species. To evaluate prediction accuracy, i.e., the probability that the individual with the greater predicted phenotype indeed has a greater phenotypic value, we developed an estimator of the ratio between known and unknown effects on the phenotype. We evaluated prediction accuracy using human data from tens of thousands of individuals from either the same family or the same population, as well as data from different species. We found that, in many cases, even when only a small fraction of the loci affecting a phenotype is known, the individual with the greater phenotypic value can be identified with over 90% accuracy. Our approach also circumvents some of the limitations in transferring genetic association results across populations. Overall, we introduce an approach that enables accurate predictions of key information on phenotypes -- the direction of phenotypic difference -- and suggest that more phenotypic information can be extracted from genomic data than previously appreciated.

genetics↗

bioRxiv2024

Toward a unified theory of microbially mediated invasion

Biological invasions pose major ecological and economic threats, and extensive research has been dedicated to understanding and predicting their dynamics. Most studies focus on the biological invasion of single species, and only in recent years has it been realized that multi-species interactions that involve native and invasive host species and their microbial symbionts can play important roles in determining invasion outputs. A theoretical framework that treats these interactions and their impact is lacking. Here we offer such a framework and use it to explore possible dynamics that may emerge from the horizontal sharing of native and non-native microbial symbionts among native and non-native host individuals and species. Thus, for example, invasive plants might benefit from native mycorrhizal networks in the soil, or might be particularly successful if they carry with them parasites to which competing native hosts are susceptible. On the other hand, invasion might be hindered by native parasites that spread from native to invasive individuals. The mathematical framework that we lay out in this study provides a new mechanistic, cohesive, and intuition-enhancing tool for theoretically exploring the ways by which the subtleties of the relationships between hosts and microbes may affect invasion dynamics. We identify multiple pathways through which microbes can facilitate (or prevent) host invasion, microbial invasion, and the invasion of both hosts and their co-introduced microbes. We disentangle invasion outcomes and highlight modalities of ecological dynamics that have so far not been considered in invasion biology. Our work sets the foundations for invasion theory that includes a community-level view of invasive and native hosts as well as their microbial symbionts.

ecology↗

bioRxiv2024

DORA: an interactive map for the visualization and analysis of ancient human DNA and associated data

The ability to sequence ancient genomes has revolutionized the way in which we study evolutionary history by providing access to the most important aspect of evolution -- time. Until recently, studying human demography, ecology, biology, and history using population genomic inference relied on contemporary genomic datasets. Over the past decade, the availability of human ancient DNA (aDNA) has increased rapidly, almost doubling every year, opening the way for spatiotemporal studies of ancient human populations. However, the multidimensionality of aDNA, with genotypes having temporal, spatial and genomic coordinates, and the need to integrate multiple sources of data, poses a challenge for developing meta-analyses pipelines. To address this challenge, we developed a publicly-available interactive tool, DORA, which integrates multiple data types, genomic and non-genomic, in a unified interface. This web-based tool allows users to browse sample metadata along with additional layers of information, such as population structure, climatic data, and unpublished samples. Users can then perform analyses on genotypes of these samples, or export sample subsets for external analyses. DORA integrates analyses and visualizations in a single intuitive interface, resolving the technical issues of combining datasets from different sources and formats, and allowing researchers to focus on analysis and the scientific questions that can be addressed through analysis of aDNA datasets.

genomics↗

bioRxiv2023

Tracking neurons across days with high-density probes

Neural activity spans multiple scales from milliseconds to months. Its evolution can be recorded with chronic electrodes, and especially with high-density arrays such as Neuropixels probes, which measure each spike at tens of sites and record hundreds of neurons. These arrays often record units with consistent spike waveforms over time, but produce vast amounts of data that require new approaches for tracking neurons across recordings. To meet this need, we developed UnitMatch, an open-access pipeline that operates after spike sorting, based only on each units average spike waveform. We tested UnitMatch in Neuropixels recordings from the mouse brain, where it tracked neurons across weeks. In visual cortex, a neurons selectivity for visual stimuli and correlation with other neurons remained stable over days. In striatum, neuronal responses changed across days during learning of a task. UnitMatch is thus a promising tool to reveal invariance or plasticity in neural activity across days.

neuroscience↗

bioRxiv2023

A spatially-resolved transcriptional atlas of the murine dorsal pons at single-cell resolution

The "dorsal pons", or "dorsal pontine tegmentum" (dPnTg), is part of the brainstem. It is a complex, densely packed region whose nuclei are involved in regulating many vital functions. Notable among them are the parabrachial nucleus, the Kolliker Fuse, the Barrington nucleus, the locus coeruleus, and the dorsal, laterodorsal, and ventral tegmental nuclei. In this study, we applied single-nucleus RNA-seq (snRNA-seq) to resolve neuronal subtypes based on their unique transcriptional profiles and then used multiplexed error robust fluorescence in situ hybridization (MERFISH) to map them spatially. We sampled [~]1 million cells across the dPnTg and defined the spatial distribution of over 120 neuronal subtypes. Our analysis identified an unpredicted high transcriptional diversity in this region and pinpointed many neuronal subtypes unique marker genes. We also demonstrated that many neuronal subtypes are transcriptionally similar between humans and mice, enhancing this studys translational value. Finally, we developed a freely accessible, GPU and CPU-powered dashboard (http://harvard.heavy.ai:6273/) that combines interactive visual analytics and hardware-accelerated SQL into a data science framework to allow the scientific community to query and gain insights into the data.

neuroscience↗

bioRxiv2023

Modeling effects of variable preBötzinger Complex network topology and cellular properties on opioid-induced respiratory depression and recovery

The pre-Botzinger complex (preBotC), located in the medulla, is the essential rhythm-generating neural network for breathing. The actions of opioids on this network impair its ability to generate robust, rhythmic output, contributing to life-threatening opioid-induced respiratory depression (OIRD). The occurrence of OIRD varies across individuals and internal and external states, increasing the risk of opioid use, yet the mechanisms of this variability are largely unknown. In this study, we utilize a computational model of the preBotC to perform several in silico experiments exploring how differences in network topology and the intrinsic properties of preBotC neurons influence the sensitivity of the network rhythm to opioids. We find that rhythms produced by preBotC networks in silico exhibit variable responses to simulated opioids, similar to the preBotC network in vitro. This variability is primarily due to random differences in network topology and can be manipulated by imposed changes in network connectivity and intrinsic neuronal properties. Our results identify features of the preBotC network that may regulate its susceptibility to opioids. Significance StatementThe neural network in the brainstem that generates the breathing rhythm is disrupted by opioid drugs. However, this response can be surprisingly unpredictable. By constructing computational models of this rhythm-generating network, we illustrate how random differences in the distribution of biophysical properties and connectivity patterns within individual networks can predict their response to opioids, and we show how modulation of these network features can make breathing more susceptible or resistant to the effects of opioids.

neuroscience↗

bioRxiv2023

Reusable, flexible, and lightweight chronic implants for Neuropixels probes

Electrophysiology has proven invaluable to record neural activity, and the development of Neuropixels probes dramatically increased the number of recorded neurons. These probes are often implanted acutely, but acute recordings cannot be performed in freely moving animals and the recorded neurons cannot be tracked across days. To study key behaviors such as navigation, learning, and memory formation, the probes must be implanted chronically. An ideal chronic implant should (1) allow stable recordings of neurons for weeks; (2) allow reuse of the probes after explantation; (3) be light enough for use in mice. Here, we present the "Apollo Implant", an open-source and editable device that meets these criteria and accommodates up to two Neuropixels 1.0 or 2.0 probes. The implant comprises a "payload" module which is attached to the probe and is recoverable, and a "docking" module which is cemented to the skull. The design is adjustable, making it easy to change the distance between probes, the angle of insertion, and the depth of insertion. We tested the implant across eight labs in head-fixed mice, freely moving mice, and freely moving rats. The number of neurons recorded across days was stable, even after repeated implantations of the same probe. The Apollo implant provides an inexpensive, lightweight, and flexible solution for reusable chronic Neuropixels recordings.

neuroscience↗

bioRxiv2023

A Brain-Wide Map of Neural Activity during Complex Behaviour

A key challenge in neuroscience is understanding how neurons in hundreds of interconnected brain regions integrate sensory inputs with prior expectations to initiate movements and thereby make decisions. It is difficult to meet this challenge if different laboratories apply different analyses to different recordings in different regions during different behaviours. Here, we report a comprehensive set of recordings from 621733 neurons across 139 mice in 12 labs performing a decision-making task with sensory, motor, and cognitive components, obtained with 699 Neuropixels probe insertions covering 279 brain areas in the left forebrain and midbrain and the right hindbrain and cerebellum. We provide an initial appraisal of this brain-wide map, assessing how neural activity encoded key task variables. Representations of visual stimuli appeared transiently in classical visual areas after stimulus onset and then spread to ramp-like activity in a collection of mid- and hindbrain regions that also encoded choices. Neural responses correlated with motor action almost everywhere in the brain. Responses to reward delivery and consumption versus negative feedback were also widespread. This publicly available dataset represents an unprecedented resource for understanding how computations distributed across and within brain areas drive behaviour.

neuroscience↗

bioRxiv2023

Brain-wide representations of prior information in mouse decision-making

The neural representations of prior information about the state of the world are poorly understood. To investigate them, we examined brain-wide Neuropixels recordings and widefield calcium imaging collected by the International Brain Laboratory. Mice were trained to indicate the location of a visual grating stimulus, which appeared on the left or right with prior probability alternating between 0.2 and 0.8 in blocks of variable length. We found that mice estimate this prior probability and thereby improve their decision accuracy. Furthermore, we report that this subjective prior is encoded in at least 20% to 30% of brain regions which, remarkably, span all levels of processing, from early sensory areas (LGd, VISp) to motor regions (MOs, MOp, GRN) and high level cortical regions (ACAd, ORBvl). This widespread representation of the prior is consistent with a neural model of Bayesian inference involving loops between areas, as opposed to a model in which the prior is incorporated only in decision-making areas. This study offers the first brain-wide perspective on prior encoding at cellular resolution, underscoring the importance of using large scale recordings on a single standardized task.

neuroscience↗

bioRxiv2023

Mouse frontal cortex nonlinearly encodes sensory, choice and outcome signals

Frontal area MOs (secondary motor area) is a key brain structure in rodents for making decisions based on sensory evidence and on reward value. In behavioral tasks, its neurons can encode sensory stimuli, upcoming choices, expected rewards, ongoing actions, and recent outcomes. However, the information encoded, and the nature of the resulting code, may depend on the task being performed. We recorded MOs population activity using two-photon calcium imaging, in a task requiring mice to integrate sensory evidence with reward value. Mice turned a wheel to report the location of a visual stimulus following a delay period, to receive a reward whose size varied over trial blocks. MOs neurons encoded multiple task variables, but not all of those seen in other tasks. In the delay period, the MOs population strongly encoded the stimulus side but did not significantly encode the reward-size block. A correlation of MOs activity with upcoming choice could be explained by a common effect of stimulus on those two correlates. After the wheel turn and the feedback, the MOs population encoded choice side and choice outcome jointly and nonlinearly according to an exclusive-or (XOR) operation. This nonlinear operation would allow a downstream linear decoder to infer the correct choice side (i.e., the side that would have been rewarded) even on zero contrast trials, when there had been no visible stimulus. These results indicate that MOs neurons flexibly encode some but not all variables that determine behavior, depending on task. Moreover, they reveal that MOs activity can reflect a nonlinear combination of these behavioral variables, allowing simple linear inference of task events that would not have been directly observable.

neuroscience↗

bioRxiv2022

Task-dependent optimal representations for cerebellarlearning

The cerebellar granule cell layer has inspired numerous theoretical models of neural representations that support learned behaviors, beginning with the work of Marr and Albus. In these models, granule cells form a sparse, combinatorial encoding of diverse sensorimotor inputs. Such sparse representations are optimal for learning to discriminate random stimuli. However, recent observations of dense, low-dimensional activity across granule cells have called into question the role of sparse coding in these neurons. Here, we generalize theories of cerebellar learning to determine the optimal granule cell representation for tasks beyond random stimulus discrimination, including continuous input-output transformations as required for smooth motor control. We show that for such tasks, the optimal granule cell representation is substantially denser than predicted by classic theories. Our results provide a general theory of learning in cerebellum-like systems and suggest that optimal cerebellar representations are task-dependent.

neuroscience↗

bioRxiv2022

Visuomotor learning promotes visually evoked activity in the medial prefrontal cortex

The medial prefrontal cortex (mPFC) is necessary for executing many learned associations between stimuli and movement. It is unclear, however, whether activity in the mPFC reflects sensory or motor aspects of sensorimotor associations and whether it evolves gradually during learning. To address these questions, we recorded cortical activity with widefield calcium imaging while mice learned a visuomotor task. The task involved associating a visual stimulus with a forelimb movement. After learning, the mPFC showed stimulus-evoked activity both during task performance and during passive viewing, when the stimulus evoked no action. This stimulus-evoked activity closely tracked behavioral performance across training, exhibiting jumps between training days. Electrophysiological recordings localized this activity to the secondary motor and anterior cingulate cortex. We conclude that learning a visuomotor task promotes a route for visual information to reach the prefrontal cortex, which develops responses to the relevant visual stimuli even outside the context of the task.

neuroscience↗

bioRxiv2022

Rescue by gene swamping as a gene drive deployment strategy

Gene drives are genetic constructs that can spread deleterious alleles with potential application to population suppression of harmful species. Given that a gene drive can potentially spill over to other populations or even other species, control measures and fail-safes strategies must be considered. Gene drives are designed to generate a rapid demographic decline, while at the same time generating a dynamic change in the populations genetics. Since these evolutionary and demographic processes are linked and are expected to occur at a similar time-scale during gene drive spread, feedback between these processes may significantly affect the outcome of deployment. To study this feedback and to understand how it affects gene drive spillovers, we developed a gene drive model that combines evolutionary and demographic dynamics in a two-population setting. The model demonstrates how feedback between evolutionary and demographic dynamics can generate additional outcomes to those generated by the evolutionary dynamics alone. We identify an outcome of particular interest, where the short-term suppression of the target population is followed by gene swamping and loss of the gene drive. This outcome could be useful for designing gene drive deployments that temporarily suppress the population, but ultimately do not remain in the population. Using our model, we demonstrate the robustness of this outcome to spillover and to the evolution of resistance, and suggest that it could be used as a fail-safe strategy for gene drive deployment.

evolutionary biology↗

bioRxiv2022

modelRxiv: A platform for the distribution, computation and interactive display of models

Modeling the dynamics of biological processes is ubiquitous across the ecological and evolutionary disciplines. However, the increasing complexity of these models poses a significant challenge to the dissemination of model-derived results. With the existing requirements of scientific publishing, most often only a small subset of model results are generated, presented in static figures or tables, and made available to the scientific community. Further exploration of the parameter space of a model, investigation of possible variations of a model, and validation of the results in relation to model assumptions commonly rely on local deployment of code supplied by the authors. This can pose a technical challenge due to the diversity of frameworks and environments in which models are developed, and preclude model validation and exploration by readers and reviewers. To address this issue, we developed a platform that serves as an interactive repository of biological models, called modelRxiv. The platform provides a unified interface for the analysis of models that does not require any technical understanding of the model implementation. To facilitate adding models to modelRxiv, we utilize OpenAI large-language models (LLMs) to make code written in different programming languages compatible with modelRxiv, making the platform language-agnostic. modelRxiv is designed to serve as an interactive extension of published models, allowing users to regenerate model results under user-defined parameterizations of the model. By making published models accessible, this platform promises to significantly improve the accessibility, reproducibility, and validation of ecological and evolutionary models.

ecology↗

bioRxiv2021

A transcriptomic axis predicts state modulation of cortical interneurons

Transcriptomics has revealed the exquisite diversity of cortical inhibitory neurons1-7, but it is not known whether these fine molecular subtypes have correspondingly diverse activity patterns in the living brain. Here, we show that inhibitory subtypes in primary visual cortex (V1) have diverse correlates with brain state, but that this diversity is organized by a single factor: position along their main axis of transcriptomic variation. We combined in vivo 2-photon calcium imaging of mouse V1 with a novel transcriptomic method to identify mRNAs for 72 selected genes in ex vivo slices. We used transcriptomic clusters (t-types)4 to classify inhibitory neurons imaged in layers 1-3 using a three-level hierarchy of 5 Families, 11 Classes, and 35 t-types. Visual responses differed significantly only across Families, but modulation by brain state differed at all three hierarchical levels. Nevertheless, this diversity could be predicted from the first transcriptomic principal component, which predicted a cell types brain state modulation and correlations with simultaneously recorded cells. Inhibitory t-types with narrower spikes, lower input resistance, weaker adaptation, and less axon in layer 1 as determined in vitro8 fired more in resting, oscillatory brain states. Transcriptomic types with the opposite properties fired more during arousal. The former cells had more inhibitory cholinergic receptors, and the latter more excitatory receptors. Thus, despite the diversity of V1 inhibitory neurons, a simple principle determines how their joint activity shapes state-dependent cortical processing.

neuroscience↗