Dissecting origins of wiring specificity in dense cortical connectomes DOI Open Access
Philipp Harth, Daniel Udvary, Jan Boelts

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Дек. 15, 2024

Wiring specificity in the cortex is observed across scales from subcellular to network level. It describes deviations of connectivity patterns those expected randomly connected networks. Understanding origins wiring neural networks remains difficult as a variety generative mechanisms could have contributed connectome. To take step forward, we propose modeling framework that operates directly on dense connectome data provided by saturated reconstructions tissue. The computational allows testing different assumptions synaptic while accounting for anatomical constraints posed neuron morphology, which known confounding source specificity. We evaluated mouse visual and human temporal cortex. Our template model incorporates based cell type, single-cell identity, compartment. Combinations these were sufficient various are indicative Moreover, identified parameters showed interesting similarities between both datasets, motivating further analysis species.

Язык: Английский

A new approach for estimating effective connectivity from activity in neural networks DOI Creative Commons

Niklas Laasch,

Wilhelm Braun,

Lisa Knoff

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Фев. 6, 2024

Abstract Inferring and understanding the underlying connectivity structure of a system solely from observed activity its constituent components is challenge in many areas science. In neuroscience, techniques for estimating are paramount when attempting to understand network neural systems their recorded patterns. To date, no universally accepted method exists inference effective connectivity, which describes how node mechanistically affects other nodes. Here, focussing on purely excitatory networks small intermediate size continuous dynamics, we provide systematic comparison different approaches connectivity. Starting with Hopf neuron model conjunction known ground truth structural reconstruct system’s matrix using variety algorithms. We show that, sparse non-linear delays, combining lagged-cross-correlation (LCC) approach recently published derivative-based covariance analysis provides most reliable estimation matrix. also that linear networks, LCC has comparable performance based transfer entropy, at drastically lower computational cost. highlight works best decreases larger less networks. Applying dynamics without time find it does not outperform methods. Employing model, then use estimated as basis forward simulation order recreate under certain conditions, method, LCC, results higher trace-to-trace correlations than methods noise-driven systems. Finally, apply empirical biological data. subset nervous nematode C. Elegans . computationally simple performs better another published, more expensive reservoir computing-based method. Our comparatively can be used reliably estimate directed presence spatio-temporal delays noise. concrete suggestions scenario common research, where only neuronal set neurons known.

Язык: Английский

Процитировано

6
Comparison of derivative-based and correlation-based methods to estimate effective connectivity in neural networks DOI Creative Commons

Niklas Laasch,

Wilhelm Braun,

Lisa Knoff

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 13, 2025

Язык: Английский

Процитировано

0
Controlling morpho-electrophysiological variability of neurons with detailed biophysical models DOI Creative Commons
Alexis Arnaudon, Maria Reva, Mickaël Zbili

и другие.

iScience, Год журнала: 2023, Номер 26(11), С. 108222 - 108222

Опубликована: Окт. 17, 2023

Variability, which is known to be a universal feature among biological units such as neuronal cells, holds significant importance, as, for example, it enables robust encoding of high volume information in circuits and prevents hypersynchronizations. While most computational studies on electrophysiological variability were done with single-compartment neuron models, we instead focus the detailed biophysical models multi-compartmental morphologies. We leverage Markov chain Monte Carlo method generate populations electrical reproducing experimental recordings while being compatible set morphologies faithfully represent specifi morpho-electrical type. demonstrate our approach layer 5 pyramidal cells study particular, find that morphological alone insufficient reproduce variability. Overall, this provides strong statistical basis create neurons controlled

Язык: Английский

Процитировано

6
Pathological cell assembly dynamics in a striatal MSN network model DOI Creative Commons
Astrid Correa, Adam Ponzi, Vladimir Calderón

и другие.

Frontiers in Computational Neuroscience, Год журнала: 2024, Номер 18

Опубликована: Июнь 6, 2024

Under normal conditions the principal cells of striatum, medium spiny neurons (MSNs), show structured cell assembly activity patterns which alternate sequentially over exceedingly long timescales many minutes. It is important to understand this since it characteristically disrupted in multiple pathologies, such as Parkinson's disease and dyskinesia, thought be caused by alterations MSN lateral inhibitory connections strength distribution cortical excitation MSNs. To how these arise we extended a previous network model include synapses with short-term plasticity, parameters taken from recent detailed striatal connectome study. We first confirmed presence switching clusters using non-linear dimensionality reduction technique, Uniform Manifold Approximation Projection (UMAP). found that could generate non-stationary varying extremely slowly on order minutes under biologically realistic conditions. Next used Simulation Based Inference (SBI) train deep net map features generated parameters. trained SBI estimate ex-vivo brain slice calcium imaging data. best fit were very close their physiologically observed values. On other hand estimated Parkinsonian, decorticated dyskinetic preparations different. Our work may provide pipeline for diagnosis basal ganglia pathology spiking data well design pharmacological treatments.

Язык: Английский

Процитировано

0
Building virtual patients using simulation-based inference DOI Creative Commons

Nathalie Paul,

Venetia Karamitsou,

Clemens Giegerich

и другие.

Frontiers in Systems Biology, Год журнала: 2024, Номер 4

Опубликована: Сен. 12, 2024

In the context of in silico clinical trials, mechanistic computer models for pathophysiology and pharmacology (here Quantitative Systems Pharmacology models, QSP) can greatly support decision making drug candidates elucidate (potential) response patients to existing novel treatments. These are built on disease mechanisms then parametrized using (clinical study) data. Clinical variability among is represented by alternative model parameterizations, called virtual patients. Despite complexity modeling itself, individual patient data build these particularly challenging given high-dimensional, potentially sparse noisy trial this work, we investigate applicability simulation-based inference (SBI), an advanced probabilistic machine learning approach, generation from develop evaluate concept nearest fits (SBI NPF), which further enhances fitting performance. At example rheumatoid arthritis where prediction treatment notoriously difficult, our experiments demonstrate that SBI approaches capture large inter-patient compete with standard methods field. Moreover, since learns a probability distribution over parametrization, it naturally provides parametrizations. The learned distributions allow us generate highly probable populations arthritis, could enhance assessment if used trials.

Язык: Английский

Процитировано

0
Dissecting origins of wiring specificity in dense cortical connectomes DOI Open Access
Philipp Harth, Daniel Udvary, Jan Boelts

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Дек. 15, 2024

Wiring specificity in the cortex is observed across scales from subcellular to network level. It describes deviations of connectivity patterns those expected randomly connected networks. Understanding origins wiring neural networks remains difficult as a variety generative mechanisms could have contributed connectome. To take step forward, we propose modeling framework that operates directly on dense connectome data provided by saturated reconstructions tissue. The computational allows testing different assumptions synaptic while accounting for anatomical constraints posed neuron morphology, which known confounding source specificity. We evaluated mouse visual and human temporal cortex. Our template model incorporates based cell type, single-cell identity, compartment. Combinations these were sufficient various are indicative Moreover, identified parameters showed interesting similarities between both datasets, motivating further analysis species.

Язык: Английский

Процитировано

0