Sparse convolutional coding for neuronal assembly detection. - Sven Peter; Elke Kirschbaum; Martin Both; Lee Campbell; Brandon Harvey; Conor Heins; Daniel Durstewitz; Ferran Diego; Fred A. Hamprecht (NIPS, 2017)

Deep Active Inference and Scene Construction. - Conor Heins; M. Berk Mirza; Thomas Parr; Karl J. Friston; Igor Kagan; Arezoo Pooresmaeili (Frontiers Artif. Intell., 2020)

Stochastic Chaos and Markov Blankets. - Karl J. Friston; Conor Heins; Kai Ueltzhöffer; Lancelot Da Costa; Thomas Parr (Entropy, 2021)

Memory and Markov Blankets. - Thomas Parr; Lancelot Da Costa; Conor Heins; Maxwell James D. Ramstead; Karl J. Friston (Entropy, 2021)

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)

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)

Epistemic Communities under Active Inference. - Mahault Albarracin; Daphne Demekas; Maxwell J. D. Ramstead; Conor Heins (Entropy, 2022)

pymdp: A Python library for active inference in discrete state spaces. - Conor Heins; Beren Millidge; Daphne Demekas; Brennan Klein; Karl J. Friston; Iain D. Couzin; Alexander Tschantz (J. Open Source Softw., 2022)

An Analytical Model of Active Inference in the Iterated Prisoner's Dilemma. - Daphne Demekas; Conor Heins; Brennan Klein (IWAI, 2023)

Collective behavior from surprise minimization. - Conor Heins; Beren Millidge; Lancelot Da Costa; Richard Mann; Karl J. Friston; Iain D. Couzin (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)

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)

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

Bayesian Predictive Coding. - Alexander Tschantz; Magnus T. Koudahl; Hampus Linander; Lancelot Da Costa; Conor Heins; Jeff Beck; 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)