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20192026
most citedInterpretable statistical representations of neural population dynamics and geometry

25 citations · 71 across the 15 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

stat.ML2023★ 1 cited

Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactions

Zhaolu Liu, Robert L. Peach, Pedro A. M. Mediano +1

Models that rely solely on pairwise relationships often fail to capture the complete statistical structure of the complex multivariate data found in diverse domains, such as socio-…

nlin.AO2023

A unified framework for Simplicial Kuramoto models

Marco Nurisso, Alexis Arnaudon, Maxime Lucas +4

Simplicial Kuramoto models have emerged as a diverse and intriguing class of models describing oscillators on simplices rather than nodes. In this paper, we present a unified frame…

stat.ME2023

Kernel-based Joint Independence Tests for Multivariate Stationary and Non-stationary Time Series

Zhaolu Liu, Robert L. Peach, Felix Laumann +2

Multivariate time series data that capture the temporal evolution of interconnected systems are ubiquitous in diverse areas. Understanding the complex relationships and potential d…

cs.LG2023★ 25 cited

Interpretable statistical representations of neural population dynamics and geometry

Adam Gosztolai, Robert L. Peach, Alexis Arnaudon +2

The dynamics of neuron populations commonly evolve on low-dimensional manifolds. Thus, we need methods that learn the dynamical processes over neural manifolds to infer interpretab…

cs.SI2023★ 9 cited

PyGenStability: Multiscale community detection with generalized Markov Stability

Alexis Arnaudon, Juni Schindler, Robert L. Peach +4

We present PyGenStability, a general-use Python software package that provides a suite of analysis and visualisation tools for unsupervised multiscale community detection in graphs…