Biology subjects

Lajoie, G.

Publications and source records attributed to Lajoie, G..

At least 19 recordsLinked to original sources

bioRxiv2026

Evolutionarily conserved neural dynamics across mice, monkeys, and humans

On evolutionary timescales, brain circuits adapt to support survival in each species ecological niche. While some anatomical aspects of neural circuitry are conserved across species with distant evolutionary origins, each species also exhibits specific circuit adaptations that enable its behavioral repertoire. It remains unclear whether homologous brain regions leverage analogous neural computations as different species perform common behaviors such as reaching and manipulating objects. Here, we directly assessed conservation of neural computations using intracortical recordings from mouse, monkey, and human motor cortex--a homologous region across many mammals--during motor behaviors crucial for survival. We hypothesized that, despite their phylogenetic distance, rodents and primates produce movements through conserved neural computations implemented by motor cortical population dynamics. Remarkably, we found that movement-related neural dynamics were highly conserved across species, while variations in behavioral output were uniquely captured in neural trajectory geometries. Strikingly, neural dynamics during movement across species were more conserved than those across brain regions in the same human and between motor preparation and execution in the same monkeys. Lastly, through manipulation of neural network models trained to perform reaching movements, we reinforce that conservation of neural dynamics across species likely stems from shared circuit constraints. We thus assert that evolution maintains neural computations across phylogeny even as behavioral repertoires expand.

neuroscience↗

bioRxiv2025

Training neural networks from scratch in a videogame leads to brittle brain encoding

Recent brain-encoding studies using videogame tasks suggest that the training objective of an artificial neural network plays a central role in how well the networks representations align with brain activity. This study investigates the alignment of artificial neural network activations with brain activity elicited by a video game task using models trained from scratch in controlled settings. We specifically compared three model training objectives: reinforcement learning, imitation learning, and a vision task, while accounting for other potential factors which may impact performance such as training data and model architecture. We tested models on brain encoding, i.e. their ability to predict functional magnetic resonance imaging (fMRI) signals acquired while human subjects played different levels of the video game Super Mario Bros. When tested on new playthroughs from the game levels seen at training, the reinforcement learning objective had a small but significant advantage in brain encoding, followed by the imitation learning and vision models. We hypothesized that brain-aligned representations would emerge only in task-competent models, and that the specific brain regions well encoded by a model would depend on the nature of the task it was trained on. While brain encoding did improve during model training, even an untrained model with matching architecture approached the performance of the best models. Contrary to our hypotheses, no model layers or specific training objectives aligned preferentially with specific brain areas. Large performance gaps also persisted in fully trained models across game levels, both those seen during training and entirely novel ones. Overall, even though reinforcement learning presented a small advantage to train brain encoding models for videogame data, all tested brain encoding models exhibited brittle performance with limited generalization both within- and out-of-distribution. Overall, our results suggest that training small artificial models from scratch is not sufficiently reliable, and that incorporating pretrained models such as foundation vision-action models may ultimately be necessary to support robust inferences about brain representations.

neuroscience↗

bioRxiv2025

Neuromorphic hierarchical modular reservoirs

Modularity is a fundamental principle of brain organization, reflected in the presence of segregated sub-networks that enable specialized information processing. These small, densely connected modules are often nested within larger, higher-order modules, giving rise to a hierarchical modular architecture. This structure is posited to balance information segregation in specialized neuronal communities and global integration via intermodular communication. Yet, how hierarchical modularity shapes network function remains unclear. Here we introduce a simple blockmodeling framework for generating and comparing multi-level hierarchical modular networks and implement them as recurrent neural net-work reservoirs to evaluate their computational capacity. We show that hierarchical modular networks enhance memory capacity, support multitasking, and give rise to a broader range of temporal dynamics compared to strictly modular and random networks. These functional advantages can be traced to topological features enriched in hierarchical modular networks, which include reciprocal and cyclic network motifs. To test whether the computational advantages of hierarchical modularity subsist in empirical human brain structural connectivity patterns, we develop a novel hierarchical modularity-preserving network null model, allowing us to isolate the positive effect of empirical hierarchical modularity patterns on memory capacity. To evaluate the biomimetic validity of connectome-informed reservoir dynamics, we compare reservoir timescales to empirical brain timescales derived from MEG data and find that hierarchical modularity contributes to shaping brain-like neural timescales. Altogether, across multiple benchmarks, these results show that hierarchical modularity endows networks with computationally advantageous properties, providing insight into the relationship between neural network structure and function with potential applications for the design of neuromorphic computing architectures.

neuroscience↗

bioRxiv2025

Accelerated learning of a noninvasive human brain-computer interface via manifold geometry

Brain-computer interfaces (BCIs) promise to restore and enhance a wide range of human capabilities. However, a barrier to the adoption of BCIs is how long it can take users to learn to control them. We hypothesized that human BCI learning could be accelerated by leveraging the naturally occurring geometric structure of brain activity, or its intrinsic manifold, extracted using a data-diffusion process. We trained participants on a noninvasive BCI that allowed them to gain real-time control of an avatar in a virtual reality game by modulating functional magnetic resonance imaging (fMRI) activity in brain regions that support spatial navigation. We then perturbed the mapping between fMRI activity patterns and the movement of the avatar to test our manifold hypothesis. When the new mapping respected the intrinsic manifold, participants succeeded in regaining control of the BCI by aligning their brain activity within the manifold. When the new mapping sdid not respect the intrinsic manifold, participants could not learn to control the avatar again. These findings show that the manifold geometry of brain activity constrains human learning of a complex cognitive task in higher-order brain regions. Manifold geometry may be a critical ingredient for unlocking the potential of future human neurotechnologies.

neuroscience↗

bioRxiv2024

Brain-like learning with exponentiated gradients

Computational neuroscience relies on gradient descent (GD) for training artificial neural network (ANN) models of the brain. The advantage of GD is that it is effective at learning difficult tasks. However, it produces ANNs that are a poor phenomenological fit to biology, making them less relevant as models of the brain. Specifically, it violates Dales law, by allowing synapses to change from excitatory to inhibitory, and leads to synaptic weights that are not log-normally distributed, contradicting experimental data. Here, starting from first principles of optimisation theory, we present an alternative learning algorithm, exponentiated gradient (EG), that respects Dales Law and produces log-normal weights, without losing the power of learning with gradients. We also show that in biologically relevant settings EG outperforms GD, including learning from sparsely relevant signals and dealing with synaptic pruning. Altogether, our results show that EG is a superior learning algorithm for modelling the brain with ANNs.

neuroscience↗

bioRxiv2024

Syngeneic adipose-derived stromal cells modulate the immune response but have limited persistence within decellularized adipose tissue implants in C57BL/6 mice

The delivery of adipose-derived stromal cells (ASCs) on cell-instructive decellularized adipose tissue (DAT) scaffolds is a promising strategy for stimulating host-derived soft tissue regeneration. However, a better understanding of the mechanisms through which ASCs modulate regeneration in vivo is needed to harness these cells more effectively. In this study, DAT scaffolds, both with and without seeded syngeneic DsRED+ mouse ASCs, were implanted into immunocompetent C57BL/6 mice. Downstream analyses focused on assessing donor ASC persistence and phenotype, as well as the effects of ASC seeding on host macrophage polarization and the perfused host vascular network. Notably, most donor ASCs were cleared from the scaffolds by 2 weeks. Mass spectrometry-based proteomics indicated that the transplanted ASCs maintained their pre-implantation phenotype up to 1 week in vivo, suggesting that the cells were not undergoing programmed cell death. A higher fraction of the infiltrating host macrophages expressed CD68 and Arginase-1 in the ASC-seeded implants up to 1-week post-implantation. Interestingly, a small population of phagocytic macrophages, identified by uptake of DsRED protein, was present in the DAT implants in the first 2 weeks and showed enhanced expression of CD68, Arginase-1, and CD163, along with reduced expression of iNOS. MicroCT angiography revealed a similar perfused vessel network in the seeded and unseeded groups at 4- and 8-weeks post-implantation. Overall, seeding with syngeneic ASCs modulated the host macrophage response to the DAT bioscaffolds at early timepoints, but did not impact long-term regenerative outcomes, potentially due to the rapid clearance of the donor cell population in this model.

cell biology↗

bioRxiv2024

Brain-like neural dynamics for behavioral control develop through reinforcement learning

During development, neural circuits are shaped continuously as we learn to control our bodies. The ultimate goal of this process is to produce neural dynamics that enable the rich repertoire of behaviors we perform. What begins as a series of "babbles" coalesces into skilled motor output as the brain rapidly learns to control the body. However, the nature of the teaching signal underlying this normative learning process remains elusive. Here, we test two well-established and biologically plausible theories--supervised learning (SL) and reinforcement learning (RL)--that could explain how neural circuits develop the capacity for skilled movements. We trained recurrent neural networks to control a biomechanical model of a primate arm using either SL or RL and compared the resulting neural dynamics to populations of neurons recorded from the motor cortex of monkeys performing the same movements. Intriguingly, only RL-trained networks produced neural activity that matched their biological counterparts in terms of both the geometry and dynamics of population activity. We show that this similarity with biological brains depends critically on matching biomechanical properties of the limb. Dynamical analysis on network activity revealed that our RL-trained networks operate at the "edge of chaos", a dynamical regime known for its computational richness, greater memory capacity, and robust plasticity properties. We then demonstrated that monkeys and RL-trained networks, but not SL-trained networks, show a strikingly similar capacity for robust short-term behavioral adaptation to a movement perturbation, indicating a fundamental and general commonality in the neural control policy. Together, our results support the hypothesis that neural dynamics for behavioral control emerge through a process akin to reinforcement learning. The resulting neural circuits offer numerous advantages for adaptable behavioral control over simpler and more efficient learning rules and expand our understanding of how developmental processes shape neural dynamics.

neuroscience↗

bioRxiv2024

The oneirogen hypothesis: modeling the hallucinatory effects of classical psychedelics in terms of replay-dependent plasticity mechanisms

Classical psychedelics induce complex visual hallucinations in humans, generating percepts that are coherent at a low level, but which have surreal, dream-like qualities at a high level. While there are many hypotheses as to how classical psychedelics could induce these effects, there are no concrete mechanistic models that capture the variety of observed effects in humans, while remaining consistent with the known pharmacological effects of classical psychedelics on neural circuits. In this work, we propose the "oneirogen hypothesis", which posits that the perceptual effects of classical psychedelics are a result of their pharmacological actions inducing neural activity states that truly are more similar to dream-like states. We simulate classical psychedelics effects via manipulating neural network models trained on perceptual tasks with the Wake-Sleep algorithm. This established machine learning algorithm leverages two activity phases: a perceptual phase (wake) where sensory inputs are encoded, and a generative phase (dream) where the network internally generates activity consistent with stimulus-evoked responses. We simulate the action of psychedelics by partially shifting the model to the Sleep state, which entails a greater influence of top-down connections, in line with the impact of psychedelics on apical dendrites. The effects resulting from this manipulation capture a number of experimentally observed phenomena including the emergence of hallucinations, increases in stimulus-conditioned variability, and large increases in synaptic plasticity. We further provide a number of testable predictions which could be used to validate or invalidate our oneirogen hypothesis.

neuroscience↗

bioRxiv2024

HR3/RORα-mediated cholesterol sensing regulates TOR signaling

Cells and organisms adjust their growth based on the availability of cholesterol, which is essential for cellular functions. However, the mechanisms by which cells sense cholesterol levels and translate these into growth signals are not fully understood. We report that cholesterol rapidly activates the master growth-regulatory TOR pathway in Drosophila tissues. We identify the nuclear receptor HR3, an ortholog of mammalian ROR, as an essential factor in cholesterol-induced TOR activation. We demonstrate that HR3 binds cholesterol and promotes TOR pathway activation through a non-genomic mechanism acting upstream of the Rag GTPases. Similarly, we find that ROR is necessary for cholesterol-mediated TOR activation in human cells, suggesting that HR3/ROR represents a conserved mechanism for cholesterol sensing that couples cholesterol availability to TOR-pathway activity. These findings advance our understanding of how cholesterol influences cell growth, with implications for cholesterol-related diseases and cancer. HighlightsO_LICholesterol leads to dynamic TOR pathway activation, driving systemic growth C_LIO_LIHR3 in Drosophila binds cholesterol and couples its availability to TOR activation C_LIO_LIHR3 acts upstream of Rag GTPases to activate TOR in response to lysosomal cholesterol C_LIO_LIMammalian HR3 ortholog ROR is required for cholesterol-induced TOR activation C_LI

cell biology↗

bioRxiv2024

Assistive sensory-motor perturbations influence learned neural representations

Task errors are used to learn and refine motor skills. We investigated how task assistance influences learned neural representations using Brain-Computer Interfaces (BCIs), which map neural activity into movement via a decoder. We analyzed motor cortex activity as monkeys practiced BCI with a decoder that adapted to improve or maintain performance over days. Over time, task-relevant information became concentrated in fewer neurons, unlike with fixed decoders. At the population level, task information also became largely confined to a few neural modes that accounted for an unexpectedly small fraction of the population variance. A neural network model suggests the adaptive decoders directly contribute to forming these more compact neural representations. Our findings show that assistive decoders manipulate error information used for long-term learning computations like credit assignment, which informs our understanding of motor learning and has implications for designing real-world BCIs.

neuroscience↗

bioRxiv2023

Experimental exposure to winter thaws reveals tipping point in yellow birch bud mortality and phenology in the northern temperate forest of Quebec, Canada

Climate change is expected to increase the frequency and intensity of winter thaws, which could have two contrasting effects on leaf phenology. Phenology could either be advanced through the acceleration of forcing accumulation or chilling completion, or be postponed through a reduction in chilling associated with warming air temperature. We tested the influence of winter thaws on budburst phenology by exposing 300 tree cuttings of sugar maple and yellow birch trees to five different frequencies and durations of winter thaws in the lab. In spring, half of the cuttings were exposed to air temperature in two cities representing an air temperature gradient of + 2.0 {degrees}C to mimic the ongoing climate warming and bud phenology was monitored three times a week. Irrespective of thaw treatment, yellow birch phenology occurred earlier in the warmer city, showing the importance of spring temperature in triggering budburst. The treatment with the highest frequency and duration of thawing increased bud mortality and delayed the onset of spring phenology whereas low frequency treatments did not, thereby identifying a tipping point in the impact of winter thaws on bud phenology. Past this point, winter thaws could slow the acceleration of bud phenology induced by warmer spring temperature and limit carbon uptake by delaying the closure of the canopy. Climate change simulations projected by the CMIP6 Canadian downscaled climate scenario show that winter thaws will increase in frequency. Hence the expected advance in the spring phenology associated with warmer spring is not necessarily as straightforward as previously thought. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/563331v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@68374borg.highwire.dtl.DTLVardef@631a6org.highwire.dtl.DTLVardef@a5b35corg.highwire.dtl.DTLVardef@a2679b_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗

bioRxiv2023

Online Bayesian Optimization of Nerve Stimulation

ObjectiveIn bioelectronic medicine, neuromodulation therapies induce neural signals to the brain or organs modifying their function. Stimulation devices, capable of triggering exogenous neural signals using electrical wave forms, require a complex and multi-dimensional parameter space in order to control such wave forms. Determining the best combination of parameters (wave form optimization, or dosing) for treating a particular patients illness is therefore challenging. Comprehensive parameter searching for an optimal stimulation effect is often infeasible in a clinical setting, due to the size of the parameter space. Restricting this space, however, may lead to sub-optimal therapeutic results, reduced responder rates, and adverse effects. ApproachAs an alternative to a full parameter search, we present a flexible machine learning, data acquisition and processing framework for optimizing neural stimulation parameters requiring as few steps as possible using Bayesian optimization. Such optimization builds a model of the neural and physiological responses to stimulations enabling it to optimize stimulation parameters and to provide estimates of the accuracy of the response model. The vagus nerve innervates, among other thoracic and visceral organs, the heart, thus controlling heart rate and is therefore ideal for demonstrating the effectiveness of our approach. Main results. The efficacy of our optimization approach was first evaluated on simulated neural responses, then applied to vagus nerve stimulation intraoperatively in porcine subjects. Optimization converged quickly on parameters achieving target heart rates and optimizing neural B-fibre activations despite high intersubject variability. SignificanceAn optimized stimulation waveform was achieved in real time with far fewer stimulations than required by alternative optimization strategies, thus minimizing exposure to side effects. Uncertainty estimates helped avoiding stimulations outside a safe range. Our approach shows that a complex set of neural stimulation parameters can be optimized in real-time for a patient to achieve a personalized precision dosing.

neuroscience↗

bioRxiv2023

Using neural biomarkers to personalize dosing of vagus nerve stimulation

BackgroundVagus nerve stimulation (VNS) is an established therapy for treating a variety of chronic diseases, such as epilepsy, depression, obesity, and for stroke rehabilitation. However, lack of precision and side-effects have hindered its efficacy and extension to new conditions. ObjectiveTo achieve a better understanding of the relationship between VNS parameters and neural and physiological responses to enable the design of personalized dosing procedures to improve precision and efficacy of VNS therapies. MethodsWe used biomarkers from recorded evoked neural activity and short-term physiological responses (throat muscle, cardiac and respiratory activity) to understand the response to a wide range of VNS parameters in anaesthetised pigs. Using signal processing, Gaussian processes (GP) and parametric regression models we analyse the relationship between VNS parameters and neural and physiological responses. ResultsFirstly, we observe inter-subject variability for both neural and physiological responses. Secondly, we illustrate how considering multiple stimulation parameters in VNS dosing can improve the efficacy and precision of VNS therapies. Thirdly, we describe the relationship between different VNS parameters and the evoked neural activity and show how spatially selective electrodes can be used to improve fibre recruitment. Fourthly, we provide a detailed exploration of the relationship between the activations of neural fibre types and different physiological effects, and show that recordings of evoked neural activity are powerful biomarkers for predicting the short-term physiological effects of VNS. Finally, based on these results, we discuss how recordings of evoked neural activity can help design VNS dosing procedures that optimize short-term physiological effects safely and efficiently. ConclusionUnderstanding of evoked neural activity during VNS provide powerful biomarkers that could improve the precision, safety and efficacy of VNS therapies.

neuroscience↗

bioRxiv2023

conn2res: A toolbox for connectome-based reservoir computing

The connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial intelligence open fundamentally new opportunities to understand how connection patterns shape computational capacity in biological brain networks. Reservoir computing is a versatile paradigm that uses nonlinear dynamics of high-dimensional dynamical systems to perform computations and approximate cognitive functions. Here we present conn2res: an open-source Python toolbox for implementing biological neural networks as artificial neural networks. conn2res is modular, allowing arbitrary architectures and arbitrary dynamics to be imposed. The toolbox allows researchers to input connectomes reconstructed using multiple techniques, from tract tracing to noninvasive diffusion imaging, and to impose multiple dynamical systems, from simple spiking neurons to memristive dynamics. The versatility of the conn2res toolbox allows us to ask new questions at the confluence of neuroscience and artificial intelligence. By reconceptualizing function as computation, conn2res sets the stage for a more mechanistic understanding of structure-function relationships in brain networks.

neuroscience↗

bioRxiv2023

Neural manifolds and gradient-based adaptation in neural-interface tasks

During well-trained behaviors, neural population activity in motor cortex lies on a low-dimensional manifold. This raises the question of how such structure constrains subsequent learning. In brain-computer interface experiments in nonhuman primates, perturbations aligned with this subspace induced rapid adaptation, whereas misaligned perturbations induced slower adaptation. Several theoretical accounts have been proposed to explain this differential adaptation, differing in the locus of plasticity. We compare these hypotheses using a minimal linear recurrent network operating at its fixed point and trained by gradient descent. All candidate plasticity sites are able to produce some degree of differential adaptation, whose strength depends on the variance of recurrent weights, with different sensitivities across sites. Hessian analysis reveals how misaligned perturbations reshape the loss landscape by introducing directions of shallow curvature along which gradient descent proceeds slowly. We further propose an experimental test to help distinguish the contributions of different plasticity sites during adaptation. Overall, our results identify the variance of recurrent weights as a key control parameter governing differential adaptation, alongside the site of plasticity.

neuroscience↗

bioRxiv2023

Data-driven identification of major axes of functional variation in bacteria

The discovery of major axes of correlated functional variation among species and habitats has revealed the fundamental trade-offs structuring both functional and taxonomic diversity in eukaryotes such as plants. Whether such functional axes exist in the bacterial realm and whether they could explain bacterial taxonomic turnover among ecosystems remains unknown. Here we use a data-driven approach to leverage global genomic and metagenomic datasets to reveal the existence of major axes of functional variation explaining both evolutionary differentiation within Bacteria and their ecological sorting across diverse habitats. We show that metagenomic variation among bacterial communities from various ecosystems is structured along a few axes of correlated functional pathways. Similar clusters of traits explained phylogenetic trait variation among >16,000 bacterial genomes, suggesting that functional turnover among bacterial communities from distinct habitats does not only result from the differential filtering of similar functions among communities, but also from phylogenetic correlations among these functions. Concordantly, functional pathways associated with trait clusters that were most important for defining functional turnover among bacterial communities were also those that had the highest phylogenetic signal in the bacterial genomic phylogeny. This study overall underlines the important role of evolutionary history in shaping contemporary distributions of bacteria across ecosystems. Originality-Significance StatementIn this article, we use a trait screening approach based on genomic and metagenomic data to identify the key functional strategies of bacteria across ecosystems but also across the bacterial tree of life. This novel approach allows us to quantify the role of evolutionary processes in structuring microbial ecological differences among ecosystems. By reducing the high-dimensionality of trait variation observed among microorganisms around a small number of fundamental axes of trait covariation, we make a significant step towards generalization of the drivers of biological diversity in microbes but also across study systems. This research provide a major advance in our understanding of the origin and maintenance of bacterial biological diversity, expanding on related findings for plants and animals.

ecology↗

bioRxiv2022

Rapidly Inferring Personalized Neurostimulation Parameters with Meta-Learning: A Case Study of Individualized Fiber Recruitment in Vagus Nerve Stimulation

ObjectiveNeurostimulation is emerging as treatment for several diseases of the brain and peripheral organs. Due to variability arising from placement of stimulation devices, underlying neuroanatomy and physiological responses to stimulation, it is essential that neurostimulation protocols are personalized to maximize efficacy and safety. Building such personalized protocols would benefit from accumulated information in increasingly large datasets of other individuals responses. ApproachTo address that need, we propose a meta-learning family of algorithms to conduct few-shot optimization of key fitting parameters of physiological and neural responses in new individuals. While our method is agnostic to neurostimulation setting, here we demonstrate its effectiveness on the problem of physiological modeling of fiber recruitment during vagus nerve stimulation (VNS). Using data from acute VNS experiments, the mapping between amplitudes of stimulus-evoked compound action potentials (eCAPs) and physiological responses, such as heart rate and breathing interval modulation, is inferred. Main resultsUsing additional synthetic data sets to complement experimental results, we demonstrate that our meta-learning framework is capable of directly modeling the physiology-eCAP relationship for individual subjects with much fewer individually queried data points than standard methods. SignificanceOur meta-learning framework is general and can be adapted to many input-response neurostimulation mapping problems. Moreover, this method leverages information from growing data sets of past patients, as a treatment is deployed. It can also be combined with several model types, including regression, Gaussian processes with Bayesian optimization, and beyond.

neuroscience↗

bioRxiv2022

Temporal PHATE: A multi-view manifold learningmethod for brain state trajectories

1The complexity and intelligence of the brain give the illusion that measurements of brain activity will have intractably high dimensionality, rife with collection and biological noise. Nonlinear dimensionality reduction methods like UMAP and t-SNE have proven useful for high-throughput biomedical data. However, they have not been used extensively for brain imaging data such as from functional magnetic resonance imaging (fMRI), a noninvasive, secondary measure of neural activity over time containing redundancy and co-modulation from neural population activity. Here we introduce a nonlinear manifold learning algorithm for timeseries data like fMRI, called temporal potential of heat-diffusion for affinity-based transition embedding (T-PHATE). In addition to recovering a lower intrinsic dimensionality from timeseries data, T-PHATE exploits autocorrelative structure within the data to faithfully denoise dynamic signals and learn activation manifolds. We empirically validate T-PHATE on three human fMRI datasets, showing that T-PHATE significantly improves data visualization, classification, and segmentation of the data relative to several other state-of-the-art dimensionality reduction benchmarks.These notable improvements suggest many potential applications of T-PHATE to other high-dimensional datasets of temporally-diffuse processes.

neuroscience↗