Timescale and Stability in Adaptive Behaviour. - Christopher L. Buckley; Seth Bullock; Netta Cohen (ECAL, 2005)

Spatial Embedding and Complexity: The Small-World Is Not Enough. - Christopher L. Buckley; Seth Bullock (ECAL, 2007)

Dynamical complexity of spatially embedded networks. - Christopher L. Buckley; Lionel C. Barnett; Seth Bullock (ALIFE, 2008)

Monostable Controllers for Adaptive Behaviour. - Christopher L. Buckley; Peter Fine; Seth Bullock; Ezequiel A. Di Paolo (SAB, 2008)

Sensitivity and stability: A signal propagation sweet spot in a sheet of recurrent centre crossing neurons. - Christopher L. Buckley; Seth Bullock (Biosyst., 2008)

Associative Memory in Gene Regulation Networks. - Richard A. Watson; Christopher L. Buckley; Rob Mills; Adam P. Davies (ALIFE, 2010)

Adaptation without Natural Selection. - Richard A. Watson; Rob Mills; Christopher L. Buckley; Simon T. Powers; Alexandra S. Penn; Adam P. Davies; Jason Noble; Seth Bullock (ALIFE, 2010)

Spatially embedded dynamics and complexity. - Christopher L. Buckley; Seth Bullock; Lionel C. Barnett (Complex., 2010)

Spatial, temporal, and modulatory factors affecting GasNet evolvability in a visually guided robotics task. - Philip Husbands; Andrew Philippides; Patrícia Amâncio Vargas; Christopher L. Buckley; Peter Fine; Ezequiel A. Di Paolo; Michael O'Shea (Complex., 2010)

Transformations and multi-scale optimisation in biological adaptive networks. - Richard A. Watson; Rob Mills; Christopher L. Buckley (ECAL, 2011)

Transformations in the scale of behavior and the global optimization of constraints in adaptive networks. - Richard A. Watson; Rob Mills; Christopher L. Buckley (Adapt. Behav., 2011)

"If You Can't Be With the One You Love, Love the One You're With": How Individual Habituation of Agent Interactions Improves Global Utility. - Adam P. Davies; Richard A. Watson; Rob Mills; Christopher L. Buckley; Jason Noble (Artif. Life, 2011)

Global Adaptation in Networks of Selfish Components: Emergent Associative Memory at the System Scale. - Richard A. Watson; Rob Mills; Christopher L. Buckley (Artif. Life, 2011)

Optimization in "self-modeling" complex adaptive systems. - Richard A. Watson; Christopher L. Buckley; Rob Mills (Complex., 2011)

A Minimal Active Inference Agent. - Simon McGregor; Manuel Baltieri; Christopher L. Buckley (CoRR, 2015)

An active inference implementation of phototaxis. - Manuel Baltieri; Christopher L. Buckley (ECAL, 2017)

The modularity of action and perception revisited using control theory and active inference. - Manuel Baltieri; Christopher L. Buckley (ALIFE, 2018)

A Probabilistic Interpretation of PID Controllers Using Active Inference. - Manuel Baltieri; Christopher L. Buckley (SAB, 2018)

A theory of how active behavior stabilises neural activity: Neural gain modulation by closed-loop environmental feedback. - Christopher L. Buckley; Taro Toyoizumi (PLoS Comput. Biol., 2018)

Nonmodular Architectures of Cognitive Systems based on Active Inference. - Manuel Baltieri; Christopher L. Buckley (IJCNN, 2019)

The dark room problem in predictive processing and active inference, a legacy of cognitivism? - Manuel Baltieri; Christopher L. Buckley (ALIFE, 2019)

Generative models as parsimonious descriptions of sensorimotor loops. - Manuel Baltieri; Christopher L. Buckley (CoRR, 2019)

PID Control as a Process of Active Inference with Linear Generative Models. - Manuel Baltieri; Christopher L. Buckley (Entropy, 2019)

Scaling Active Inference. - Alexander Tschantz; Manuel Baltieri; Anil K. Seth; Christopher L. Buckley (IJCNN, 2020)

Predictions in the eye of the beholder: an active inference account of Watt governors. - Manuel Baltieri; Christopher L. Buckley; Jelle P. Bruineberg (ALIFE, 2020)

On the Relationship Between Active Inference and Control as Inference. - Beren Millidge; Alexander Tschantz; Anil K. Seth; Christopher L. Buckley (IWAI, 2020)

Reinforcement Learning through Active Inference. - Alexander Tschantz; Beren Millidge; Anil K. Seth; Christopher L. Buckley (CoRR, 2020)

Reinforcement Learning as Iterative and Amortised Inference. - Beren Millidge; Alexander Tschantz; Anil K. Seth; Christopher L. Buckley (CoRR, 2020)

Control as Hybrid Inference. - Alexander Tschantz; Beren Millidge; Anil K. Seth; Christopher L. Buckley (CoRR, 2020)

Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain. - Beren Millidge; Alexander Tschantz; Anil K. Seth; Christopher L. Buckley (CoRR, 2020)

Relaxing the Constraints on Predictive Coding Models. - Beren Millidge; Alexander Tschantz; Anil K. Seth; Christopher L. Buckley (CoRR, 2020)

Investigating the Scalability and Biological Plausibility of the Activation Relaxation Algorithm. - Beren Millidge; Alexander Tschantz; Anil K. Seth; Christopher L. Buckley (CoRR, 2020)

Learning action-oriented models through active inference. - Alexander Tschantz; Anil K. Seth; Christopher L. Buckley (PLoS Comput. Biol., 2020)

Habitual and Reflective Control in Hierarchical Predictive Coding. - Paul F. Kinghorn; Beren Millidge; Christopher L. Buckley (PKDD/ECML Workshops (1), 2021)

Neural Kalman Filtering. - Beren Millidge; Alexander Tschantz; Anil K. Seth; Christopher L. Buckley (CoRR, 2021)

Understanding the origin of information-seeking exploration in probabilistic objectives for control. - Beren Millidge; Alexander Tschantz; Anil K. Seth; Christopher L. Buckley (CoRR, 2021)

Predictive Coding: a Theoretical and Experimental Review. - Beren Millidge; Anil K. Seth; Christopher L. Buckley (CoRR, 2021)

A Mathematical Walkthrough and Discussion of the Free Energy Principle. - Beren Millidge; Anil K. Seth; Christopher L. Buckley (CoRR, 2021)

Active Inference in Robotics and Artificial Agents: Survey and Challenges. - Pablo Lanillos; Cristian Meo; Corrado Pezzato; Ajith Anil Meera; Mohamed Baioumy; Wataru Ohata; Alexander Tschantz; Beren Millidge; Martijn Wisse; Christopher L. Buckley; Jun Tani (CoRR, 2021)

Whence the Expected Free Energy? - Beren Millidge; Alexander Tschantz; Christopher L. Buckley (Neural Comput., 2021)

RL with KL penalties is better viewed as Bayesian inference. - Tomasz Korbak; Ethan Perez; Christopher L. Buckley (EMNLP (Findings), 2022)

Knitting a Markov Blanket is Hard When You are Out-of-Equilibrium: Two Examples in Canonical Nonequilibrium Models. - Miguel Aguilera; Ángel Poc-López; Conor Heins; Christopher L. Buckley (IWAI, 2022)

Spin Glass Systems as Collective Active Inference. - Conor Heins; Brennan Klein; Daphne Demekas; Miguel Aguilera; Christopher L. Buckley (IWAI, 2022)

Capsule Networks as Generative Models. - Alex B. Kiefer; Beren Millidge; Alexander Tschantz; Christopher L. Buckley (IWAI, 2022)

Preventing Deterioration of Classification Accuracy in Predictive Coding Networks. - Paul F. Kinghorn; Beren Millidge; Christopher L. Buckley (IWAI, 2022)

A Message Passing Perspective on Planning Under Active Inference. - Magnus T. Koudahl; Christopher L. Buckley; Bert de Vries (IWAI, 2022)

Active Inference Successor Representations. - Beren Millidge; Christopher L. Buckley (IWAI, 2022)

Hybrid Predictive Coding: Inferring, Fast and Slow. - Alexander Tschantz; Beren Millidge; Anil K. Seth; Christopher L. Buckley (CoRR, 2022)

Successor Representation Active Inference. - Beren Millidge; Christopher L. Buckley (CoRR, 2022)

Designing Ecosystems of Intelligence from First Principles. - Karl J. Friston; Maxwell J. D. Ramstead; Alex B. Kiefer; Alexander Tschantz; Christopher L. Buckley; Mahault Albarracin; Riddhi J. Pitliya; Conor Heins; Brennan Klein; Beren Millidge; Dalton A. R. Sakthivadivel; Toby St Clere Smithe; et al. (CoRR, 2022)

Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs. - Beren Millidge; Alexander Tschantz; Christopher L. Buckley (Neural Comput., 2022)

Pretraining Language Models with Human Preferences. - Tomasz Korbak; Kejian Shi; Angelica Chen; Rasika Vinayak Bhalerao; Christopher L. Buckley; Jason Phang; Samuel R. Bowman; Ethan Perez (ICML, 2023)

Understanding Tool Discovery and Tool Innovation Using Active Inference. - Poppy Collis; Paul F. Kinghorn; Christopher L. Buckley (IWAI, 2023)

Relative Representations for Cognitive Graphs. - Alex B. Kiefer; Christopher L. Buckley (IWAI, 2023)

Exploring Action-Centric Representations Through the Lens of Rate-Distortion Theory. - Miguel De Llanza Varona; Christopher L. Buckley; Beren Millidge (IWAI, 2023)

Attention as Implicit Structural Inference. - Ryan Singh; Christopher L. Buckley (NeurIPS, 2023)

Attention: Marginal Probability is All You Need? - Ryan Singh; Christopher L. Buckley (CoRR, 2023)

Understanding Predictive Coding as an Adaptive Trust-Region Method. - Francesco Innocenti; Ryan Singh; Christopher L. Buckley (CoRR, 2023)

Brain-Inspired Computational Intelligence via Predictive Coding. - Tommaso Salvatori; Ankur Arjun Mali; Christopher L. Buckley; Thomas Lukasiewicz; Rajesh P. N. Rao; Karl J. Friston; Alexander Ororbia (CoRR, 2023)

Supervised structure learning. - Karl J. Friston; Lancelot Da Costa; Alexander Tschantz; Alex B. Kiefer; Tommaso Salvatori; Victorita Neacsu; Magnus T. Koudahl; Conor Heins; Noor Sajid; Dimitrije Markovic; Thomas Parr; Tim Verbelen; et al. (CoRR, 2023)

Active Inference and Intentional Behaviour. - Karl J. Friston; Tommaso Salvatori; Takuya Isomura; Alexander Tschantz; Alex B. Kiefer; Tim Verbelen; Magnus T. Koudahl; Aswin Paul; Thomas Parr; Adeel Razi; Brett J. Kagan; Christopher L. Buckley; et al. (CoRR, 2023)

Hybrid predictive coding: Inferring, fast and slow. - Alexander Tscshantz; Beren Millidge; Anil K. Seth; Christopher L. Buckley (PLoS Comput. Biol., 2023)

A behavioral and modeling study of control algorithms underlying the translational optomotor response in larval zebrafish with implications for neural circuit function. - John G. Holman; Winnie W. K. Lai; Paul Pichler; Daniel Saska; Leon Lagnado; Christopher L. Buckley (PLoS Comput. Biol., 2023)

Learning in Hybrid Active Inference Models. - Poppy Collis; Ryan Singh; Paul F. Kinghorn; Christopher L. Buckley (IWAI, 2024)

Only Strict Saddles in the Energy Landscape of Predictive Coding Networks? - Francesco Innocenti; El Mehdi Achour; Ryan Singh; Christopher L. Buckley (NeurIPS, 2024)

From pixels to planning: scale-free active inference. - Karl J. Friston; Conor Heins; Tim Verbelen; Lancelot Da Costa; Tommaso Salvatori; Dimitrije Markovic; Alexander Tschantz; Magnus T. Koudahl; Christopher Buckley 001; Thomas Parr (CoRR, 2024)

Hybrid Recurrent Models Support Emergent Descriptions for Hierarchical Planning and Control. - Poppy Collis; Ryan Singh; Paul F. Kinghorn; Christopher L. Buckley (CoRR, 2024)

Gradient-free variational learning with conditional mixture networks. - Conor Heins; Hao Wu; Dimitrije Markovic; Alexander Tschantz; Jeff Beck; Christopher Buckley 001 (CoRR, 2024)

R-AIF: Solving Sparse-Reward Robotic Tasks from Pixels with Active Inference and World Models. - Viet Dung Nguyen; Zhizhuo Yang; Christopher L. Buckley; Alexander Ororbia (CoRR, 2024)

Variational Bayes Gaussian Splatting. - Toon Van de Maele; Ozan Çatal; Alexander Tschantz; Christopher L. Buckley; Tim Verbelen (CoRR, 2024)

Navigation under uncertainty: Trajectory prediction and occlusion reasoning with switching dynamical systems. - Ran Wei; Joseph Lee; Shohei Wakayama; Alexander Tschantz; Conor Heins; Christopher Buckley 001; John Carenbauer; Hari Thiruvengada; Mahault Albarracin; Miguel de Prado; Petter Horling; Peter Winzell; et al. (CoRR, 2024)

JPC: Flexible Inference for Predictive Coding Networks in JAX. - Francesco Innocenti; Paul F. Kinghorn; Will Yun-Farmbrough; Miguel De Llanza Varona; Ryan Singh; Christopher L. Buckley (CoRR, 2024)

Natural Induction: Spontaneous Adaptive Organisation without Natural Selection. - Christopher L. Buckley; Tim Lewens; Michael Levin 001; Beren Millidge; Alexander Tschantz; Richard A. Watson (Entropy, 2024)

Active Inference and Intentional Behavior. - Karl J. Friston; Tommaso Salvatori; Takuya Isomura; Alexander Tschantz; Alex B. Kiefer; Tim Verbelen; Magnus T. Koudahl; Aswin Paul; Thomas Parr; Adeel Razi; Brett J. Kagan; Christopher L. Buckley; et al. (Neural Comput., 2025)

Bayesian Predictive Coding. - Alexander Tschantz; Magnus T. Koudahl; Hampus Linander; Lancelot Da Costa; Conor Heins; Jeff Beck; Christopher L. Buckley (CoRR, 2025)

μPC: Scaling Predictive Coding to 100+ Layer Networks. - Francesco Innocenti; El Mehdi Achour; Christopher L. Buckley (CoRR, 2025)

AXIOM: Learning to Play Games in Minutes with Expanding Object-Centric Models. - Conor Heins; Toon Van de Maele; Alexander Tschantz; Hampus Linander; Dimitrije Markovic; Tommaso Salvatori; Corrado Pezzato; Ozan Çatal; Ran Wei; Magnus T. Koudahl; Marco Perin; Karl J. Friston; et al. (CoRR, 2025)