Introducing ActiveInference.jl: A Julia Library for Simulation and Parameter Estimation with Active Inference Models

Entropy, Journal Year: 2025, Volume and Issue: 27(1), P. 62 - 62

Published: Jan. 12, 2025

We introduce a new software package for the Julia programming language, library ActiveInference.jl. To make active inference agents with Partially Observable Markov Decision Process (POMDP) generative models available to growing research community using Julia, we re-implemented pymdp Python. ActiveInference.jl is compatible cutting-edge libraries designed cognitive and behavioural modelling, as it used in computational psychiatry, science neuroscience. This means that POMDP can now be easily fit empirically observed behaviour sampling, well variational methods. In this article, show how makes building straightforward, enables researchers use them simulation, fitting data or performing model comparison.

Language: Английский

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Comparing cooperative geometric puzzle solving in ants versus humans

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 122(1)

Published: Dec. 23, 2024

Biological ensembles use collective intelligence to tackle challenges together, but suboptimal coordination can undermine the effectiveness of group cognition. Testing whether cognition exceeds that individual is often impractical since different organizational scales tend face disjoint problems. One exception problem navigating large loads through complex environments and toward a given target. People ants stand out in their ability efficiently perform this task not just individually also as group. This provides rare opportunity empirically compare problem-solving skills cognitive traits across species sizes. Here, we challenge people with same “piano-movers” load maneuvering puzzle show while more larger groups, opposite true for humans. We find although cannot grasp global nature puzzle, motion translates into emergent skills. They encode short-term memory internally ordered state allows enhanced performance. comprehend way them explore reduced search space and, on average, outperform ants. However, when communication restricted, groups resort most obvious maneuvers facilitate consensus. reminiscent ant behavior, negatively impacts Our results exemplify how simple minds easily enjoy scalability brains require extensive cooperate efficiently.

Language: Английский

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Understanding collective behavior in biological systems through potential field mechanisms

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 29, 2025

Collective behavior in biological systems emerges from local interactions among individuals, enabling groups to adapt dynamic environments. Traditional modeling approaches, such as bottom-up and top-down models, have limitations accurately representing these complex interactions. We propose a novel potential field mechanism that integrates environmental influences explain collective behavior. This study introduces fields, where individuals perceive respond fields generated by cues other individuals. develop mathematical framework combining distributed learning swarm control simulate analyze under varying conditions. Our simulations span variety of conditions, including standard environments organisms interact typical high noise are disrupted random fluctuations, density with increased competition for space, risk featuring areas strong negative field, multiple resource degrees availability. These demonstrate the adaptability resilience changing challenging Results reveal how facilitate emergence stable coordinated behaviors, providing insights into self-organization, cooperation, nature. enhances our understanding has implications bio-robotics, systems, networks.

Language: Английский

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As One and Many: Relating Individual and Emergent Group-Level Generative Models in Active Inference

Entropy, Journal Year: 2025, Volume and Issue: 27(2), P. 143 - 143

Published: Feb. 1, 2025

Active inference under the Free Energy Principle has been proposed as an across-scales compatible framework for understanding and modelling behaviour self-maintenance. Crucially, a collective of active agents can, if they maintain group-level Markov blanket, constitute larger agent with generative model its own. This potential computational scale-free structures speaks to application self-organizing systems across spatiotemporal scales, from cells human collectives. Due difficulty reconstructing that explains emergent agents, there little research on this kind multi-scale inference. Here, we propose data-driven methodology characterising relation between dynamics constituent individual agents. We apply methods cognitive psychiatry, applicable well other types approaches. Using simple Multi-Armed Bandit task example, employ new ActiveInference.jl library Julia simulate who are equipped blanket. use sampling-based parameter estimation make inferences about agent, show is non-trivial relationship models constitute, even in setting. Finally, point number ways which might be applied better understand relations nested scales.

Language: Английский

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Surface-Driven Particle Dynamics: Sequential Synchronization of Colloidal Flow Attempted in a Static Fluidic Environment

ACS Applied Materials & Interfaces, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 7, 2025

The collective behavior of colloids in microsystems is characterized by precise micro-object control, broadening the applications cargo manipulation drug delivery, microfluidics, and nanotechnology. To further investigate this potential, we introduce a cargo-manipulating platform that utilizes micromagnetic patterns fluid flow rather than conventional fluidic components. This platform, called flowless micropump, comprises an encapsulating system within chip, containing both actuation particles (2.8 μm diameter) control targets, thereby eliminating external interactions. enables two distinct modes manipulation: direct nonmagnetic (e.g., MCF-7 THP-1 cells) indirect polymer particles) through secondary localized flow. Direct achieved via coordinated particle collisions, facilitated optimized guiding wall with height 25 μm. Conversely, allows for high-speed mode change individual targets. These events are using patterned structures: railway-track connected half-disk (conductor) patterns. By employing conductor pattern conjunction pattern, agile microcargo (MCF-7 cells bead clusters) was at frequencies 1–3 Hz magnetic field strength 10 mT. study establishes programmable designing micropumps diverse functionalities various experimental purposes. colloidal generated shape semi-three-dimensional (3D) structures, holds significant promise screening, cell–cell interaction studies, organoid-on-chip research.

Language: Английский

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Collective decision making by embodied neural agents

PNAS Nexus, Journal Year: 2025, Volume and Issue: 4(4)

Published: March 25, 2025

Abstract Collective decision making using simple social interactions has been studied in many types of multiagent systems, including robot swarms and human networks. However, existing studies have rarely modeled the neural dynamics that underlie sensorimotor coordination embodied biological agents. In this study, we investigated collective decisions resulted from among agents with dynamics. We equipped our a model minimal based on framework, embedded them an environment stimulus gradient. single-agent setup, between two sources depends solely agent’s its environment. same also agents, via their interactions. Our results show success depended balance intra-agent, interagent, agent–environment coupling, use these to identify influences environmental factors difficulty. More generally, illustrate how behaviors can be analyzed terms participating This contribute ongoing developments neuro-AI self-organized systems.

Language: Английский

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Sustainability under Active Inference

Systems, Journal Year: 2024, Volume and Issue: 12(5), P. 163 - 163

Published: May 4, 2024

In this paper, we explore the known connection among sustainability, resilience, and well-being within framework of active inference. Initially, revisit how notions resilience intersect inference before defining sustainability. We adopt a holistic concept sustainability denoting enduring capacity to meet needs over time without depleting crucial resources. It extends beyond material wealth encompass community networks, labor, knowledge. Using free energy principle, can emphasize role fostering resource renewal, harmonious system–entity exchanges, practices that encourage self-organization as pathways achieving both an agent part collective. start by connecting with well-being, building on existing work. then attempt link asserting alone is insufficient for sustainable outcomes. While absorbing shocks stresses, must be intrinsically linked ensure adaptive capacities do not merely perpetuate vulnerabilities. Rather, it should facilitate transformative processes address root causes unsustainability. Sustainability, therefore, manifest across extended timescales all system strata, from individual components broader system, uphold ecological integrity, economic stability, social well-being. explain manifests at level collectives systems. To model quantify interdependencies between resources their impact overall introduce application network theory dynamical systems theory. optimization precision or learning rates through framework, advocating approach fosters elastic plastic necessary long-term abundance.

Language: Английский

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Sustainability under Active Inference

Published: May 2, 2024

In this paper we explore the known connection among sustainability, resilience, and well-being within framework of active inference. Initially, revisit how notions resilience intersect inference before defining sustainability. We adopt a holistic concept sustainability denoting enduring capacity to meet needs over time without depleting crucial resources. It extends beyond material wealth encompass community networks, labor, knowledge. Using Free Energy Principle, can emphasize role fostering resource renewal, harmonious system-entity exchanges, practices that encourage self-organization as pathways achieving both in an agent collectives. start by connecting Active Inference with well-being, building on exsiting work. then attempt link asserting alone is insufficient for sustainable outcomes. While absorbing shocks stresses, must be intrinsically linked ensure adaptive capacities do not merely perpetuate existing vulnerabilities. Rather, it should facilitate transformative processes address root causes unsustainability. Sustainability, therefore, manifest across extended timescales all system strata, from individual components broader system, uphold ecological integrity, economic stability, social well-being. explain manifests at level agent, collectives systems. To model quantify interdependencies between resources their impact overall introduce application network theory dynamical systems theory. optimization precision or learning rates through framework, advocating approach fosters elastic plastic necessary long-term abundance.

Language: Английский

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Bayesian brain theory: Computational neuroscience of belief

Neuroscience, Journal Year: 2024, Volume and Issue: 566, P. 198 - 204

Published: Dec. 4, 2024

Language: Английский

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Computational joint action: dynamical models to understand the development of joint coordination

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 28, 2024

Abstract Coordinating with others is part of our everyday experience. Previous studies using sensorimotor coordination games suggest that human dyads develop strategies can be interpreted as Nash equilibria. However, if the players are uncertain about what their partner doing, they which robust to actual partner’s actions. This has suggested humans select actions based on an explicit prediction will doing – a model probabilistic by nature. mechanisms underlying development joint over repeated trials remain unknown. Very much like adaptation individuals external perturbations (eg force fields or visual rotations), dynamical models may help understand how develops trials. Here we present general computational game theory and Bayesian estimation designed Joint tasks modeled quadratic games, where each participant’s task expressed cost function. Each participant predicts next move (partner model) optimally combining predictions sensory observations, selects through stochastic optimization its expected cost, given model. The parameters include perceptual uncertainty (sensory noise), representation (retention rate process in action selection decay (which action’s learning rate). used two ways: (i) simulate interactive behaviors, thus helping make specific context scenario; (ii) analyze time series experiments, providing quantitative metrics describe individual behaviors during action. We demonstrate variety scenarios. In version Stag Hunt game, different representations lead via-point (2-VP) reaching task, consist complex trajectories, captures well observed temporal evolution performance. For this also estimated from experimental provided comprehensive characterization dyad participants. Computational identifying factors preventing facilitating coordination. They clinical settings, interpret impaired interaction capabilities. provide theoretical basis devise artificial agents establish forms facilitate neuromotor recovery. Author summary Acting together (joint action) But, do learn coordinate collaborate? Using combination experiments show multiple repetitions same represents ‘best response’ believe opponent do. Such belief developed gradually, prior assumptions (how repeatable erratic behaves) information opponent’s past Rooted estimation, accounts for mutual ‘trust’ among partners essential establishing mutually advantageous collaboration, explains combine decisions movements generative tool, scenario, analytic tool characterize traits defects ability collaborations.

Language: Английский

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