Paper List
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SpikGPT: A High-Accuracy and Interpretable Spiking Attention Framework for Single-Cell Annotation
This paper addresses the core challenge of robust single-cell annotation across heterogeneous datasets with batch effects and the critical need to ide...
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Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
This paper addresses the core challenge of efficiently and accurately sampling the conformational landscape of biomolecules from diffusion-based struc...
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Personalized optimization of pediatric HD-tDCS for dose consistency and target engagement
This paper addresses the critical limitation of one-size-fits-all HD-tDCS protocols in pediatric populations by developing a personalized optimization...
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Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
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Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
This paper addresses the core pain point of low sequence-structure alignment in existing synthetic datasets (e.g., AFDB), which severely limits the pe...
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MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
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.