Paper List
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GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
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Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
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Simulation and inference methods for non-Markovian stochastic biochemical reaction networks
This paper addresses the computational bottleneck of simulating and performing Bayesian inference for non-Markovian biochemical systems with history-d...
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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
Pulse desynchronization of neural populations by targeting the centroid of the limit cycle in phase space
University of Padua | Abdus Salam International Center for Theoretical Physics | Université Paris Dauphine-PSL
30秒速读
IN SHORT: This work addresses the core challenge of determining optimal pulse timing and intensity for desynchronizing pathological neural oscillations when the underlying dynamical system is unknown, by leveraging a robust geometric feature in phase space.
核心创新
- Methodology Introduces a pulse desynchronization control strategy based on targeting the geometric centroid of the limit cycle in phase space, a point shown to be robust to changes in the coupling constant (ε).
- Methodology Utilizes bivariate neural activity signals (e.g., X and Y averages) as feedback input, moving beyond traditional univariate approaches (like local field potential alone) to extract richer phase-space information.
- Theory Demonstrates analytically and numerically that the centroid lies within a region of maximal return times to the limit cycle after perturbation, making it an effective target for prolonging desynchronized states with minimal pulses.
主要结论
- Numerical simulations of a coupled FitzHugh-Nagumo system (N=1000) show the centroid's location is nearly independent of the coupling parameter ε (tested for ε ∈ {0.1, 0.2, 0.3, 0.4}), providing a robust target.
- The centroid is strategically located near the dx/dt=0 nullcline within the region of maximal return times (visualized via interpolated heatmaps), delaying the system's return to the synchronized limit cycle.
- The proposed control strategy, exploiting bivariate input and the centroid target, aims to achieve desynchronization with a significantly lower number of pulses compared to previous adaptive search methods, potentially reducing clinical side effects.
摘要: The synchronized activity of neuronal populations can lead to pathological over-synchronization in conditions such as epilepsy and Parkinson disease. Such states can be desynchronized by brief electrical pulses. But when the underlying oscillating system is not known, as in most practical applications, to determine the specific times and intensities of pulses used for desynchronizaton is a difficult inverse problem. Here we propose a desynchronization scheme for neuronal models of bi-variate neural activity, with possible applications in the medical setting. Our main argument is the existence of a peculiar point in the phase space of the system, the centroid, that is both easy to calculate and robust under changes in the coupling constant. This important target point can be used in a control procedure because it lies in the region of minimal return times of the system.