Pedro A. M. Mediano

According to our database, Pedro A. M. Mediano authored at least 35 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamics.

Fernando E. Rosas, Diego Candia-Rivera, Andrea I. Luppi, Yike Guo, Pedro A. M. Mediano

Comput. Biol. Medicine, March, 2024

2023

Not with a "zap" but with a "beep": Measuring the origins of perinatal experience.

Joel Frohlich, Tim Bayne, Julia Sophia Crone, Alessandra DallaVecchia, Asger Kirkeby-Hinrup, Pedro A. M. Mediano, Julia Moser, Karolina Talar, Alireza Gharabaghi, Hubert Preissl

NeuroImage, June, 2023

Reduced emergent character of neural dynamics in patients with a disrupted connectome.

Andrea I. Luppi, Pedro A. M. Mediano, Fernando E. Rosas, Judith Allanson, John D. Pickard, Guy B. Williams, Michael M. Craig, Paola Finoia, Alexander R. D. Peattie, Peter Coppola, David K. Menon, Daniel Bor, Emmanuel A. Stamatakis

NeuroImage, April, 2023

Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness.

Andrea I. Luppi, Joana Cabral, Rodrigo Cofré, Pedro A. M. Mediano, Fernando E. Rosas, Abid Y. Qureshi, Amy Kuceyeski, Enzo Tagliazucchi, Federico Raimondo, Gustavo Deco, James M. Shine, Morten L. Kringelbach, Patricio Orio, ShiNung Ching, Yonatan Sanz Perl, Michael N. Diringer, Robert D. Stevens, Jacobo Diego Sitt

NeuroImage, 2023

Information decomposition reveals hidden high-order contributions to temporal irreversibility.

Andrea I. Luppi, Fernando E. Rosas, Gustavo Deco, Morten L. Kringelbach, Pedro A. M. Mediano

CoRR, 2023

Quantifying synergy and redundancy in multiplex networks.

Andrea I. Luppi, Eckehard Olbrich, Conor Finn, Laura E. Suárez, Fernando E. Rosas, Pedro A. M. Mediano, Jürgen Jost

CoRR, 2023

Dynamical noise can enhance high-order statistical structure in complex systems.

Patricio Orio, Pedro A. M. Mediano, Fernando E. Rosas

CoRR, 2023

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

Zhaolu Liu, Robert L. Peach, Pedro A. M. Mediano, Mauricio Barahona

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Logarithmic Decomposition for Information.

Keenan J. A. Down, Pedro A. M. Mediano

Proceedings of the IEEE International Symposium on Information Theory, 2023

2022

High-order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model.

Marilyn Gatica, Fernando E. Rosas, Pedro A. M. Mediano, Ibai Díez, Stephan P. Swinnen, Patricio Orio, Rodrigo Cofré, Jesús M. Cortés

PLoS Comput. Biol., September, 2022

Psychedelics and schizophrenia: Distinct alterations to Bayesian inference.

Hardik Rajpal, Pedro A. M. Mediano, Fernando E. Rosas, Christopher B. Timmermann, Stefan Brugger, Suresh D. Muthukumaraswamy, Anil K. Seth, Daniel Bor, Robin L. Carhart-Harris, Henrik J. Jensen

NeuroImage, 2022

Metastability, fractal scaling, and synergistic information processing: What phase relationships reveal about intrinsic brain activity.

Fran Hancock, Joana Cabral, Andrea I. Luppi, Fernando E. Rosas, Pedro A. M. Mediano, Ottavia Dipasquale, Federico E. Turkheimer

NeuroImage, 2022

Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks.

Alexandra M. Proca, Fernando E. Rosas, Andrea I. Luppi, Daniel Bor, Matthew Crosby, Pedro A. M. Mediano

CoRR, 2022

Disentangling high-order mechanisms and high-order behaviours in complex systems.

Fernando E. Rosas, Pedro A. M. Mediano, Andrea I. Luppi, Thomas F. Varley, Joseph T. Lizier, Sebastiano Stramaglia, Henrik J. Jensen, Daniele Marinazzo

CoRR, 2022

2021

High-Order Interdependencies in the Aging Brain.

Marilyn Gatica, Rodrigo Cofré, Pedro A. M. Mediano, Fernando E. Rosas, Patricio Orio, Ibai Díez, Stephan P. Swinnen, Jesús M. Cortés

Brain Connect., 2021

2020

Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data.

Fernando E. Rosas, Pedro A. M. Mediano, Henrik J. Jensen, Anil K. Seth, Adam B. Barrett, Robin L. Carhart-Harris, Daniel Bor

PLoS Comput. Biol., 2020

Learning, compression, and leakage: Minimizing classification error via meta-universal compression principles.

Fernando E. Rosas, Pedro A. M. Mediano, Michael Gastpar

CoRR, 2020

Building Proactive Voice Assistants: When and How (not) to Interact.

Ondrej Miksik, I. Munasinghe, J. Asensio-Cubero, S. Reddy Bethi, S.-T. Huang, S. Zylfo, Xuechen Liu, T. Nica, A. Mitrocsak, S. Mezza, Rory Beard, Ruibo Shi, Raymond W. M. Ng, Pedro A. M. Mediano, Zafeirios Fountas, S.-H. Lee, J. Medvesek, H. Zhuang, Yvonne Rogers, Pawel Swietojanski

CoRR, 2020

An operational information decomposition via synergistic disclosure.

Fernando Rosas, Pedro A. M. Mediano, Borzoo Rassouli, Adam B. Barrett

CoRR, 2020

Deep active inference agents using Monte-Carlo methods.

Zafeirios Fountas, Noor Sajid, Pedro A. M. Mediano, Karl J. Friston

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Causal Blankets: Theory and Algorithmic Framework.

Fernando E. Rosas, Pedro A. M. Mediano, Martin Biehl, Shamil Chandaria, Daniel Polani

Proceedings of the Active Inference - First International Workshop, 2020

Learning, compression, and leakage: Minimising classification error via meta-universal compression principles.

Fernando E. Rosas, Pedro A. M. Mediano, Michael Gastpar

Proceedings of the IEEE Information Theory Workshop, 2020

2019

IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks.

Patricia Wollstadt, Joseph T. Lizier, Raul Vicente, Conor Finn, Mario Martínez-Zarzuela, Pedro A. M. Mediano, Leonardo Novelli, Michael Wibral

J. Open Source Softw., 2019

Measuring Integrated Information: Comparison of Candidate Measures in Theory and Simulation.

Pedro A. M. Mediano, Anil K. Seth, Adam B. Barrett

Entropy, 2019

Quantifying High-order Interdependencies via Multivariate Extensions of the Mutual Information.

Fernando Rosas, Pedro A. M. Mediano, Michael Gastpar, Henrik J. Jensen

CoRR, 2019

Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing.

Leonardo Novelli, Patricia Wollstadt, Pedro A. M. Mediano, Michael Wibral, Joseph T. Lizier

CoRR, 2019

Relational Forward Models for Multi-Agent Learning.

Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinícius Flores Zambaldi, János Kramár, Neil C. Rabinowitz, Thore Graepel, Matthew M. Botvinick, Peter W. Battaglia

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

An Information-Theoretic Approach to Self-Organisation: Emergence of Complex Interdependencies in Coupled Dynamical Systems.

Fernando Rosas, Pedro A. M. Mediano, Martín Ugarte, Henrik J. Jensen

Entropy, 2018

Relational Forward Models for Multi-Agent Learning.

Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinícius Flores Zambaldi, Neil C. Rabinowitz, Thore Graepel, Matthew M. Botvinick, Peter W. Battaglia

CoRR, 2018

Measuring Fitness Effects of Agent-Environment Interactions.

Simon McGregor, Pedro A. M. Mediano

Artif. Life, 2018

Adaptation Is Not Just Improvement over Time.

Simon McGregor, Pedro A. M. Mediano

Artif. Life, 2018

2017

The Partial Information Decomposition of Generative Neural Network Models.

Tycho M. S. Tax, Pedro A. M. Mediano, Murray Shanahan

Entropy, 2017

An Investigation of the Dynamical Transitions in Harmonically Driven Random Networks of Firing-Rate Neurons.

Kyriacos Nikiforou, Pedro A. M. Mediano, Murray Shanahan

Cogn. Comput., 2017

Spectral Modes of Network Dynamics Reveal Increased Informational Complexity Near Criticality.

Xerxes D. Arsiwalla, Pedro A. M. Mediano, Paul F. M. J. Verschure

Proceedings of the International Conference on Computational Science, 2017

2016

Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders.

Nat Dilokthanakul, Pedro A. M. Mediano, Marta Garnelo, Matthew C. H. Lee, Hugh Salimbeni, Kai Arulkumaran, Murray Shanahan

CoRR, 2016