Data-driven Inference of Models from Embodied Neural Systems In Vertebrate Experiments https://dimensive.maguilera.net/ Mon, 10 May 2021 10:15:18 +0000 Mon, 10 May 2021 10:15:18 +0000 Jekyll v3.9.0 Simulation data of asymmetric kinetic Ising models near a ferromagnetic phase transition (download link). This data can be used for generating the results in Aguilera, M, Moosavi, SA & Shimazaki H (2021). A unifying framework for mean-field theories of asymmetric kinetic Ising systems. Nature Communications 12:1197, as well ass for exploring ferromagnetic phase transitions in the asymmetric Sherrington-Kirkpatrick model Thu, 04 Nov 2021 00:00:00 +0000 https://dimensive.maguilera.net/datasets/2021/11/04/dataset_unifying_framework/ https://dimensive.maguilera.net/datasets/2021/11/04/dataset_unifying_framework/ dataset datasets In this new paper, published by Miguel Aguilera, Amin Moosavi and Hideaki Shimazaki in Nature Communications, we study and unify different kinetic mean-field methods in Ising models with the objective of developing tools for studying large data-sets from networks of neurons in non-equilibrium conditions and near critical and maximally fluctuating regimes. We propose a framework that integrates previous methods under an information geometric perspective. This framework also also allows us to propose new methods under atypical assuptions for mean-field methods. The University of Sussex’s Media Relations team has been so kind to write this nice piece on our paper, describing the importance developing methods for studying and modelling the activity of neurons in non-equilibrium conditions and near the edge of chaos. Aguilera, M, Moosavi, SA & Shimazaki H (2021). A unifying framework for mean-field theories of asymmetric kinetic Ising systems. Nature Communications 12:1197; https://doi.org/10.1038/s41467-021-20890 Abstract Kinetic Ising models are powerful tools for studying the non-equilibrium dynamics of complex systems. As their behavior is not tractable for large networks, many mean-field methods have been proposed for their analysis, each based on unique assumptions about the system’s temporal evolution. This disparity of approaches makes it challenging to systematically advance mean-field methods beyond previous contributions. Here, we propose a unifying framework for mean-field theories of asymmetric kinetic Ising systems from an information geometry perspective. The framework is built on Plefka expansions of a system around a simplified model obtained by an orthogonal projection to a sub-manifold of tractable probability distributions. This view not only unifies previous methods but also allows us to develop novel methods that, in contrast with traditional approaches, preserve the system’s correlations. We show that these new methods can outperform previous ones in predicting and assessing network properties near maximally fluctuating regimes. Mon, 04 Oct 2021 00:00:00 +0000 https://dimensive.maguilera.net/publications/2021/10/04/Paper-Unifying-framework/ https://dimensive.maguilera.net/publications/2021/10/04/Paper-Unifying-framework/ publication publications In this new paper, published by Miguel Aguilera and Ezequiel Di Paolo in Neuroscience & Biobehavioral Reviews, we review the link of self-organized criticality and integrated information with ideas about soft assembly in neural and cognitive process. Studying the properties of power-law scaling processes as a in indicator of properties like soft-assembly, self-organization or interaction-dominant-dynamics is a suggestive approach for the understanding of cognitive process from a complex systems perspective. However, critics have suggested that these approaches operate mostly at the level of analogy or metaphor between real phenomena and idealized toy models. We suggest that this issue can be resolved by exploring what specific kinds of criticality we should expect from cognitive agents, and exploring a particular type of criticality related with integrated information theory, as a a system’s susceptibility to changes in its own integration (Aguilera & Di Paolo, 2019). We find that identifying this critical integration is more informative than power-law measures about the underlying processes of a system (e.g. agent-environment asymmetries, robust of sensorimotor interaction). Aguilera, M & Di Paolo, EA (2021). Critical integration in neural and cognitive systems: Beyond power-law scaling as the hallmark of soft assembly. Neuroscience & Biobehavioral Reviews 123; https://doi.org/10.1016/j.neubiorev.2021.01.009 Abstract Inspired by models of self-organized criticality, a family of measures quantifies long-range correlations in neural and behavioral activity in the form of self-similar (e.g., power-law scaled) patterns across a range of scales. Long-range correlations are often taken as evidence that a system is near a critical transition, suggesting interaction-dominant, softly assembled relations between its parts. Psychologists and neuroscientists frequently use power-law scaling as evidence of critical regimes and soft assembly in neural and cognitive activity. Critics, however, argue that this methodology operates at most at the level of an analogy between cognitive and other natural phenomena. This is because power-laws do not provide information about a particular system’s organization or what makes it specifically cognitive. We respond to this criticism using recent work in Integrated Information Theory. We propose a more principled understanding of criticality as a system’s susceptibility to changes in its own integration, a property cognitive agents are expected to manifest. We contrast critical integration with power-law measures and find the former more informative about the underlying processes. Tue, 04 May 2021 00:00:00 +0000 https://dimensive.maguilera.net/publications/2021/05/04/Paper-Critical-integration/ https://dimensive.maguilera.net/publications/2021/05/04/Paper-Critical-integration/ publication publications