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Biology subjects

Macke, J. H.

Publications and source records attributed to Macke, J. H..

At least 19 recordsLinked to original sources
bioRxiv2026

Attractor dynamics underlie sequence working memory in data-constrained recurrent neural networks

Sequences of stimuli can be maintained in working memory by encoding each item, together with its position, as a distinct pattern of neural population activity. Competing hypotheses about the underlying dynamics have been proposed: patterns of activity might be supported either by stable attractor states that persist over time, or by transient sequential dynamics. To investigate the dynamics underlying sequence memory, we developed a framework for fitting low-rank recurrent neural networks (RNNs) to multi-session recordings of spiking activity, and applied it to 2,488 units recorded across 30 sessions in two macaques. Our data-constrained RNNs generated population spiking activity which closely resembled neural data and recovered single-unit tuning properties across sessions. Consistent with prior work, fitted networks represented items presented at different sequence positions in separate low-dimensional subspaces in the neural activity, alongside a dominant temporal component. The RNNs enabled direct investigation of the underlying dynamics: By isolating the temporal and stimulus subspaces, we found a manifold of fixed points emerged during the memory retention period. Targeted perturbation analyses confirmed that activity within this manifold is consistent with attractor-like dynamics, and predict that brief optogenetic perturbations delivered along this attractive manifold can selectively induce persistent shifts in neural activity and decoded behavior. Together, our results indicate that sequence working memory is organized by position-specific coding subspaces, within which attractor states maintain persistent representations, while activity evolves along time-varying dimensions.

neuroscience↗
bioRxiv2026

Multi-channel high-density single-molecule localization

Deep learning has enabled single-molecule localization microscopy (SMLM) at high emitter densities, but only for single channel systems. Here we present DECODE-Plex, a deep-learning-based framework to localize dense single molecules with overlapping point spread functions simultaneously in multiple channels. We showcase DECODE-Plex on experimental ultra-high density dual-color and 3D live-cell data. Packaged for ease of use, it will enable many groups to improve imaging speed and quality of multi-channel SMLM.

biophysics↗
bioRxiv2025

velotest: Statistical assessment of RNA velocity embeddings reveals quality differences for reliable trajectory visualizations

Contemporary single-cell RNA-sequencing studies typically use RNA velocity analyses to investigate cellular dynamics. Commonly, RNA velocities are assessed with two-dimensional projections. Although it is well established that nonlinear embeddings of gene expression data cannot comprehensively preserve the structure of the original high-dimensional data across scales, there is no tool that systematically assesses and quantifies the faithfulness of velocity embeddings. This situation potentially leads to misleading velocity visualizations and putative cell state transitions. To this end, we introduce velotest, a cell-level statistical test that quantifies the faithfulness of a velocity embedding with respect to its high-dimensional counterpart. Applying velotest across multiple datasets and embedding methods, we find strong differences in the embedding qualities between embedding methods, datasets, and regions in individual datasets. velotest thus provides an essential validation step and enables researchers to select a suitable visualization of high-dimensional velocities, ensuring that hypotheses drawn from velocity projections are grounded in accurately represented dynamics.

bioinformatics↗
bioRxiv2025

A data and task-constrained mechanistic model of the mouse outer retina shows robustness to contrast variations

Visual processing starts in the outer retina where photoreceptors transform light into electrochemical signals. These signals are modulated by inhibition from horizontal cells and sent to the inner retina via excitatory bipolar cells. The outer retina is thought to play an important role in contrast invariant coding of visual information, but how the different cell types implement this computation together remains incompletely understood. To understand the role of each cell type, we developed a fully-differentiable biophysical model of a circular patch of mouse outer retina. The model includes 200 cone photoreceptors with a realistic phototransduction cascade and ribbon synapses as well as horizontal and bipolar cells, all with cell-type specific ion channels. Going beyond decades of work constraining biophysical models of neurons only by experimental data, we used a dual approach, constraining some parameters of the model with available measurements and others by a visual task: (1) We fit the parameters of the cone models to whole cell patch-clamp measurements of photocurrents and two-photon glutamate imaging measurements of synaptic release. (2) We then trained the spatiotemporal outer retina model with photoreceptors and the other cell types to perform a visual classification task with varying contrast and luminance levels. We found that our outer retina model could learn to solve the classification task despite contrast and luminance variance in the stimuli. Testing different cell type compositions and connectivity patterns, we found that feedback from horizontal cells did not further improve task performance beyond that of excitatory photoreceptors and bipolar cells. This is surprising given that horizontal cells are positioned to mediate communication across cones and that they add to the models number of trainable parameters. Finally, we found that our model generalized better to out of distribution contrast levels than a linear classifier. Our work shows how the nonlinearities found in the outer retina can accomplish contrast invariant classification and teases apart the contributions of different cell types.

neuroscience↗
bioRxiv2025

An ion channel omnimodel for standardized biophysical neuron modelling

Biophysical neuron modeling is an indispensable tool in neuroscience research, with the combination of diverse ion channel kinetics and morphologies being used to explain various single-neuron properties and responses. Despite this, there is no standard way of formulating ion channel models, making it challenging to relate models to one another and experimental data. Here, we revive the idea of a standard model for ion channels based on the Hodgkin-Huxley formulation, and apply it to a recently curated database of ion channel models. We demonstrate that this standard formulation, which we refer to as an omnimodel, accurately fits the majority of voltage-gated models in the database (over 3,000 models). It produces similar, if not identical, responses to voltage-clamp protocols in simulations where the ion channel omnimodels were used. Importantly, the standard formalism enables easy comparison of models based on parameter settings. It can also be used to make new observations about the space of ion channel kinetics found in neurons. Furthermore, it facilitates the inference of ion channel parameters from the responses to standard protocols. We provide an interactive platform to compare and select channel models, and encourage the community to use this standard formulation whenever possible, to facilitate understanding and comparison among models.

neuroscience↗
bioRxiv2025

Personalized real-time inference of momentary excitability from human EEG

The efficacy of transcranial magnetic stimulation (TMS) is often limited by non-adaptive protocols that disregard instantaneous brain states, potentially constraining therapeutic outcomes. Current EEG-guided approaches are hindered by their reliance on motor-evoked potentials (MEPs), which confound cortical and spinal excitability and restrict applications to the motor cortex, and a dependence on static biomarkers that cannot adapt to changing neurophysiological patterns. We introduce PRIME (Personalized Real-time Inference of Momentary Excitability), a deep learning framework that predicts cortical excitability, quantified by TMS-evoked potential (TEP) amplitude, from raw EEG signals. By targeting cortical excitability directly, PRIME enables brain state-dependent stimulation across any cortical region. PRIME incorporates transfer learning and continual adaptation to automatically identify personalized biomarkers, allowing stimulation timing to be adapted across individuals and sessions. PRIME successfully predicts cortical excitability with minimal latency, providing a computational foundation for next-generation, personalized closed-loop TMS interventions.

neuroscience↗
bioRxiv2025

Memory by a thousand rules: Automated discovery of functional multi-type plasticity rules reveals variety and degeneracy at the heart of learning.

Synaptic plasticity is the basis of learning and memory, but the link between synaptic changes and neural function remains elusive. Here, we used automated search algorithms to obtain thousands of strikingly diverse quadruplets of excitatory(E)-to-E, E-to-inhibitory(I), IE, and II plasticity rules, cooperating to stabilize recurrent spiking networks. Despite the fact that quadruplets were selected for homeostasis, more than 90% of them performed well in simple and more difficult memory tasks such as novelty detection, contextual novelty and sequence replay. Co-activity was crucial, i.e., most rules failed in isolation. Our purely local, unsupervised plasticity rules could also help solve computer games such as pong. Our work showcases automated discovery augmenting human intuition to find en masse solutions for high dimensional problems.

neuroscience↗
bioRxiv2024

Dissecting origins of wiring specificity in dense cortical connectomes

What are the origins of the highly specific wiring patterns that are formed by the neurons in the brain? To address this question, we introduce a method to predict the empirically observed wiring diagram - the connectome - at synaptic resolution based on dense electron-microscopic reconstructions of neural tissue. Our method generates the connectome based on the morphological properties of the neurons in combination with synaptic specificity models, whose parameters capture how neurons wire conditional on their subcellular, cellular and cell type properties. We employ simulation-based Bayesian inference to identify all values for these parameters that can generate the observed connectome. Finally, for each synapse in a dense reconstruction, our method provides quantitative measures to reveal which synaptic specificity models are necessary, sufficient and best-suited to generate it. The output of our method are experimentally testable predictions of wiring preferences from subcellular to cell type levels that could account for each synapse in dense reconstructions. We demonstrate our method on dense datasets from mouse primary visual and human temporal cortex. Strikingly, this demonstration shows that just three assumptions, with nearly the same synaptic specificity values, predict the connectivity in both datasets. Our method is openly accessible as a computational framework that includes a comprehensive workflow for the analysis of wiring specificity, and which provides users with full flexibility to define and test their hypotheses. Our method sets the stage to uncover the principles by which neural networks are organized, and to compare these principles across brain areas, species and time points.

neuroscience↗
bioRxiv2024

Loom response in mouse superior colliculus depends on sensorimotor context

Visual motion is produced both by an organisms movement through the world, and by objects moving in the world such as potential predators. Choosing appropriate behaviour therefore requires organisms to distinguish these sources of visual motion. Here we asked how mice integrate self-movement with looming visual motion by combining virtual reality and neural recordings from superior colliculus (SC), a brain area important in visually-guided approach and avoidance behaviours. We first measured locomotion behaviour and neural activity while animals approached an object in virtual reality, and while the same object loomed at them. In both cases, vision dominated activity in superficial layers (SCs), while locomotion had more influence on activity in intermediate layers (SCim). In addition, animals instinctively slowed their locomotion when nearing the object, or when the object neared them. To directly test animals ability to distinguish self-from object motion we replayed the visual images generated during object approach. Locomotion behaviour often changed during replay, showing animals are able to establish if visual motion is matched to their self-movement. Further, decoders trained on locomotion behaviour, or on population activity in SC, particularly in SCim, were able to reliably discriminate epochs of replay and object approach. We conclude that both mouse behaviour and SC activity encode whether looming visual motion arises from self-or object movement, with implications for understanding sensorimotor coordination in dynamic environments. HighlightsO_LIWe recorded from superficial (SCs) and intermediate (SCim) superior colliculus in VR C_LIO_LIVision dominated SCs, while SCim was modulated by both vision and locomotion C_LIO_LIMice altered behaviour when visual experience did not match that expected from their locomotion C_LIO_LIPopulation activity differed between matched and unmatched visual experiences, particularly in SCim C_LI

neuroscience↗
bioRxiv2024

Uncertainty mapping and probabilistic tractography using Simulation-Based Inference in diffusion MRI: A comparison with classical Bayes

Simulation-Based Inference (SBI) has recently emerged as a powerful framework for Bayesian inference: Neural networks are trained on simulations from a forward model, and learn to rapidly estimate posterior distributions. We here present an SBI framework for parametric spherical deconvolution of diffusion MRI data of the brain. We demonstrate its utility for estimating white matter fibre orientations, mapping uncertainty of voxel-based estimates and performing probabilistic tractography by spatially propagating fibre orientation uncertainty. We conduct an extensive comparison against established Bayesian methods based on Markov-Chain Monte-Carlo (MCMC) and find that: a) in-silico training can lead to calibrated SBI networks with accurate parameter estimates and uncertainty mapping for both single- and multi-shell diffusion MRI, b) SBI allows amortised inference of the posterior distribution of model parameters given unseen observations, which is orders of magnitude faster than MCMC, c) SBI-based tractography yields reconstructions that have a high level of agreement with their MCMC-based counterparts, equal to or higher than scan-rescan reproducibility of estimates. We further demonstrate how SBI design considerations (such as dealing with noise, defining priors and handling model selection) can affect performance, allowing us to identify optimal practices. Taken together, our results show that SBI provides a powerful alternative to classical Bayesian inference approaches for fast and accurate model estimation and uncertainty mapping in MRI.

neuroscience↗
bioRxiv2024

From Simulations to Inference: Using Machine Learning to Tune Patient-Specific Finite-Element Models of the Middle Ear Towards Objective Diagnosis

Computational models, particularly finite-element (FE) models, are essential for interpreting experimental data and predicting system behavior, especially when direct measurements are limited. A major challenge in tuning these models is the large number of parameters involved. Traditional methods, such as one-by-one sensitivity analyses, are time-consuming, subjective, and often return only a single set of parameter values, focusing on reproducing averaged data rather than capturing the full variability of experimental measurements. In this study, we applied simulation-based inference (SBI) using neural posterior estimation (NPE) to tune an FE model of the human middle ear. The training dataset consisted of 10,000 FE simulations of stapes velocity, ear-canal (EC) input impedance, and absorbance, paired with seven FE parameter values randomly sampled within plausible ranges. The neural network learned the association between parameters and simulation outcomes, returning the probability distribution of parameter values that can reproduce experimental data. Our approach successfully identified parameter sets that reproduced three experimental datasets simultaneously. By accounting for experimental noise and variability during training, the method provided a probability distribution of parameters, representing all valid combinations that could fit the data, rather than tuning to averaged values. The network demonstrated robustness to noise and exhibited an efficient learning curve due to the large training dataset. SBI offers an objective alternative to laborious sensitivity analyses, providing probability distributions for each parameter and uncovering interactions between them. This method can be applied to any biological FE model, and we demonstrated its effectiveness using a middle-ear model. Importantly, it holds promise for objective differential diagnosis of conductive hearing loss by providing insight into the mechanical properties of the middle ear.

bioengineering↗
bioRxiv2024

Deep inverse modeling reveals dynamic-dependent invariances in neural circuit mechanisms

Neural population dynamics are shaped by many cellular, synaptic, and network properties. Not only is it important to understand how coordinated changes in circuit parameters alter neural activity, but also when dynamics are unaffected by--or invariant to--such changes. Computational modeling has revealed invariances in single neurons and small circuits that are thought to reflect their robustness against variability and perturbations. However, generalizing these insights to larger circuits in cortex and other brain areas remains challenging. A key bottleneck lies in inverse modeling of neural circuits with spiking network models, i.e., identifying parameter configurations that quantitatively match dynamics observed in neural recordings. Here, we present Automated Model Inference from Neural Dynamics (AutoMIND) for efficient discovery of invariant circuit model configurations. AutoMIND leverages a mechanistic model with adaptive spiking neurons and clustered connectivity, which displays a rich variety of spatiotemporal dynamics. Probabilistic deep generative models--trained on network simulations only--then returns many parameter configurations consistent with a given target observation of neural activity. Applied to several datasets, AutoMIND discovers circuit models of synchronous network bursting in human brain organoids across early development, as well as models capturing complex frequency profiles of Neuropixels recordings in mouse hippocampus and cortex. In each case, we obtain hundreds of configurations that compose a (nonlinear) parameter subspace in which population dynamics remain unchanged. Surprisingly, global and local geometries of the invariant subspace are not fixed, but differ for different dynamics. Together, our results shed light on dynamic-dependent invariances of circuit parameters underlying diverse population dynamics, while demonstrating the flexibility of AutoMIND for inverse modeling of neural circuits.

neuroscience↗
bioRxiv2024

Differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics

Biophysiscal neuron models provide insights into cellular mechanisms underlying neural computations. However, a central challenge has been the question of how to identify the parameters of detailed biophysical models such that they match physiological measurements at scale or such that they perform computational tasks. Here, we describe a framework for simulation of detailed biophysical models in neuroscience--JO_SCPLOWAXLEYC_SCPLOW--which addresses this challenge. By making use of automatic differentiation and GPU acceleration, JO_SCPLOWAXLEYC_SCPLOW opens up the possibility to efficiently optimize large-scale biophysical models with gradient descent. We show that JO_SCPLOWAXLEYC_SCPLOW can learn parameters of biophysical neuron models with several hundreds of parameters to match voltage or two photon calcium recordings, sometimes orders of magnitude more efficiently than previous methods. We then demonstrate that JO_SCPLOWAXLEYC_SCPLOW makes it possible to train biophysical neuron models to perform computational tasks. We train a recurrent neural network to perform working memory tasks, and a feedforward network of morphologically detailed neurons with 100,000 parameters to solve a computer vision task. Our analyses show that JO_SCPLOWAXLEYC_SCPLOW dramatically improves the ability to build large-scale data- or task-constrained biophysical models, creating unprecedented opportunities for investigating the mechanisms underlying neural computations across multiple scales.

neuroscience↗
bioRxiv2024

Decoding movie content from neuronal population activity in the human medial temporal lobe

The human medial temporal lobe (MTL), a region implicated in memory and high-level cognition, contains neurons that respond selectively to stimuli belonging to specific categories, such as individual people, landmarks, or objects. However, these neurons have been largely studied via static, isolated presentations of stimuli. Therefore, it is unclear how neurons in the MTL respond to rich stimuli such as movies, and which dynamical stimulus features can be retrieved from neuronal population spiking activity. We studied single-unit responses from 2286 neurons recorded from the amygdala, hippocampus, entorhinal cortex, and parahippocampal cortex of 29 intracranially implanted patients during the presentation of an 83-minute movie. We found only a few individual neurons that exhibited a classic selective response to semantic features. However, we successfully decoded the presence of characters, settings, and visual transitions from neuronal population activity. The information relevant for decoding varies across regions depending on the feature category, as visual transitions could be decoded from subsets of neurons with selective responses, whereas character and location features relied on distributed representations. Our results demonstrate an approach for reliably decoding movie features in the human MTL, and suggest that the brain uses a population code when representing character and location features.

neuroscience↗
bioRxiv2024

Modeling conditional distributions of neural and behavioral data with masked variational autoencoders

Extracting the relationship between high-dimensional recordings of neural activity and complex behav- ior is a ubiquitous problem in systems neuroscience. Toward this goal, encoding and decoding models attempt to infer the conditional distribution of neural activity given behavior and vice versa, while dimensionality reduc- tion techniques aim to extract interpretable low-dimensional representations. Variational autoencoders (VAEs) are flexible deep-learning models commonly used to infer low-dimensional embeddings of neural or behavioral data. However, it is challenging for VAEs to accurately model arbitrary conditional distributions, such as those encountered in neural encoding and decoding, and even more so simultaneously. Here, we present a VAE-based approach for accurately calculating such conditional distributions. We validate our approach on a task with known ground truth and demonstrate the applicability to high-dimensional behavioral time series by retrieving the condi- tional distributions over masked body parts of walking flies. Finally, we probabilistically decode motor trajectories from neural population activity in a monkey reach task and query the same VAE for the encoding distribution of neural activity given behavior. Our approach provides a unifying perspective on joint dimensionality reduction and learning conditional distributions of neural and behavioral data, which will allow for scaling common analyses in neuroscience to todays high-dimensional multi-modal datasets.

neuroscience↗
bioRxiv2024

Decoding Motor Excitability in TMS using EEG-Features:An Exploratory Machine Learning Approach

BackgroundWith the burgeoning interest in personalized treatments for brain network disorders, closed-loop transcranial magnetic stimulation (TMS) represents a promising frontier. Relying on the real-time adjustment of stimulation parameters based on brain signal decoding, the success of this approach depends on the identification of precise biomarkers for timing the stimulation optimally. ObjectiveWe aimed to develop and validate a supervised machine learning framework for the individualized prediction of motor excitability states, leveraging a broad spectrum of sensor and source space EEG features. MethodsOur approach integrates multi-scale EEG feature extraction and selection within a nested cross-validation scheme, tested on a cohort of 20 healthy participants. We assessed the frameworks performance across different classifiers, feature sets, and experimental protocols to ensure robustness and generalizability. ResultsPersonalized classifiers demonstrated a statistically significant mean predictive accuracy of 72 {+/-} 11%. Consistent performance across various testing conditions highlighted the sufficiency of sensor-derived features for accurate excitability state predictions. Subtype analysis revealed distinct clusters linked to specific brain regions and oscillatory features as well as the need for a more extensive feature set for effective biomarker identification than conventionally considered. ConclusionsOur machine learning framework effectively identifies predictive biomarkers for motor excitability, holding potential to enhance the efficacy of personalized closed-loop TMS interventions. While the clinical applicability of our findings remains to be validated, the consistent performance across diverse testing conditions and the efficacy of sensor-only features suggest promising avenues for clinical research and wider applications in brain signal classification.

neuroscience↗
bioRxiv2023

Indistinguishable network dynamics can emerge from unalike plasticity rules

Synaptic plasticity is thought to be critical for building and maintaining brain circuits. Models of plasticity, or plasticity rules, are typically designed by hand, and evaluated based on their ability to elicit similar neuron or circuit properties to ground truth. While this approach has provided crucial insights into plasticity mechanisms, it is limited in its scope by human intuition and cannot identify all plasticity mechanisms that are consistent with the empirical data of interest. In other words, focusing on individual hand-crafted rules ignores the potential degeneracy of plasticity mechanisms that explain the same empirical data, and may thus lead to inaccurate experimental predictions. Here, we use an unsupervised, adversarial approach to infer plasticity rules directly from neural activity recordings. We show that even in a simple, idealised network model, many mechanistically different plasticity rules are equally compatible with empirical data. Our results suggest the need for a shift in the study of plasticity rules, considering as many degenerate plasticity mechanisms consistent with data as possible, before formulating experimental predictions.

neuroscience↗
bioRxiv2023

Generating realistic neurophysiological time series with denoising diffusion probabilistic models

In recent years, deep generative models have had a profound impact in engineering and sciences, revolutionizing domains such as image and audio generation, as well as advancing our ability to model scientific data. In particular, Denoising Diffusion Probabilistic Models (DDPMs) have been shown to accurately model time series as complex high-dimensional probability distributions. Experimental and clinical neuroscience also stand to benefit from this progress, since accurate modeling of neurophysiological time series, such as electroencephalography (EEG), electrocorticography (ECoG), and local field potential (LFP) recordings, and their synthetic generation can enable or improve a variety of neuroscientific applications. Here, we present a method for modeling multi-channel and densely sampled neurophysiological recordings using DDPMs, which can be flexibly applied to different recording modalities and experimental configurations. First, we show that DDPMs can generate realistic synthetic data for a variety of datasets including different recording techniques (LFP, ECoG, EEG) and species (rat, macaque, human). DDPM-generated time series accurately capture single- and multi-channel statistics such as frequency spectra and phase-amplitude coupling, as well as fine-grained and dataset-specific features such as sharp wave-ripples. In addition, synthetic time series can be generated based on additional information like experimental conditions or brain states. We demonstrate the utility and flexibility of DDPMs in several neuroscience-specific analyses, such as brain-state classification and imputation of missing channels to improve neural decoding. In summary, DDPMs can serve as accurate generative models of neurophysiological recordings, and have a broad utility in the probabilistic generation of synthetic time series for neuroscientific applications.

neuroscience↗