FEP_Active_Inference_Papers/bibtex.bib

@article{gershman2019does,
  title={What does the free energy principle tell us about the brain?},
  author={Gershman, Samuel J},
  journal={arXiv preprint arXiv:1901.07945},
  year={2019},
  keywords={survey},
  url={https://arxiv.org/abs/1901.07945}
}
@String(gershman2019does="This provides a great high level introduction to the basic ideas and intuitions of the FEP, with a small amount of crucial mathematical background.")

@article{friston2010free,
  title={The free-energy principle: a unified brain theory?},
  author={Friston, Karl},
  journal={Nature reviews neuroscience},
  volume={11},
  number={2},
  pages={127--138},
  year={2010},
  publisher={Nature publishing group},
  keywords={survey},
  url={https://www.uab.edu/medicine/cinl/images/KFriston_FreeEnergy_BrainTheory.pdf}
}
@String(friston2010free="This provides a great overview for the initial intuitions behind the FEP and its application to the brain.")

@article{raja2021markov,
  title={The Markov Blanket Trick: On the Scope of the Free Energy Principle and Active Inference},
  author={Raja, Vicente and Valluri, Dinesh and Baggs, Edward and Chemero, Anthony and Aderson, Michael L},
  year={2021},
  keywords={philosophy},
  url={http://philsci-archive.pitt.edu/18831/1/The%20Markov%20Blanket%20Trick.pdf}
}
@article{aguilera2021particular,
  title={How particular is the physics of the Free Energy Principle?},
  author={Aguilera, Miguel and Millidge, Beren and Tschantz, Alexander and Buckley, Christopher L},
  journal={arXiv preprint arXiv:2105.11203},
  year={2021},
  keywords={philosophy},
  url={https://arxiv.org/pdf/2105.11203.pdf}
}
@String(aguilera2021particular="This paper critically analyses and deconstructs various philosophical claims about *what the FEP is saying*. Specifically, it argues that there is not necessarily a connection between the statistical notion of a Markov Blanket, and a functional notion, meaning that an actual *dynamical* separation (such as a cell membrane) does not necessarily imply a *statistical* separation in the form of a Markov Blanket and vice versa. Secondly, it demonstrates and clarifies that the FEP only makes claims about the flow of internal states *on average* over counterfactual realizations of the system, and therefore the FEP cannot describe the individual trajectories of a system in terms of free energy minimization.")


@article{bogacz2017tutorial,
  title={A tutorial on the free-energy framework for modelling perception and learning},
  author={Bogacz, Rafal},
  journal={Journal of mathematical psychology},
  volume={76},
  pages={198--211},
  year={2017},
  publisher={Elsevier},
  keywords={survey},
  url={https://www.sciencedirect.com/science/article/pii/S0022249615000759}
}
@String(bogacz2017tutorial="This is a great review which introduces the basics of predictive coding and the FEP, including the maths and contains MATLAB sample code. If you want to start seriously diving into the maths, I would start here.")


@article{buckley2017free,
  title={The free energy principle for action and perception: A mathematical review},
  author={Buckley, Christopher L and Kim, Chang Sub and McGregor, Simon and Seth, Anil K},
  journal={Journal of Mathematical Psychology},
  volume={81},
  pages={55--79},
  year={2017},
  publisher={Elsevier},
  keywords={survey},
  url={https://www.sciencedirect.com/science/article/pii/S0022249617300962}
}
@String(buckley2017free="This is a fantastic review which presents a complete walkthrough of the mathematical basis of the Free Energy Principle and Variational Inference, and derives predictive coding and (continuous time and state) active inference. I would reccomend reading this after Bogacz' tutorial (although be prepared -- it is a long and serious read)")

@article{smith2021step,
  title={A Step-by-Step Tutorial on Active Inference and its Application to Empirical Data},
  author={Smith, Ryan and Friston, Karl and Whyte, Christopher},
  year={2021},
  publisher={PsyArXiv},
  keywords={survey},
  url={https://psyarxiv.com/b4jm6/}
}
@String(smith2021step="A detailed and clear walkthrough of discrete-state-space active inference, including detailed MATLAB code for a sample implementation.")


@article{friston2019free,
  title={A free energy principle for a particular physics},
  author={Friston, Karl},
  journal={arXiv preprint arXiv:1906.10184},
  year={2019},
  keywords={classic},
  url={https://arxiv.org/pdf/1906.10184.pdf},
}

@String(friston2019free="This is Karl's magisterial monograph, and contains the most comprehensive description of the FEP to date")

@article{friston2006free,
  title={A free energy principle for the brain},
  author={Friston, Karl and Kilner, James and Harrison, Lee},
  journal={Journal of Physiology-Paris},
  volume={100},
  number={1-3},
  pages={70--87},
  year={2006},
  publisher={Elsevier},
  keywords={classic},
  url={https://www.sciencedirect.com/science/article/pii/S092842570600060X?casa_token=rPwSn8wQvEkAAAAA:5QeLri0QzrjAC8QYtNljoqjn0nZzRJIoBso67Uw3eY9VSFdUqcIm4mkqPBNZCwYQM8PM_VdkFfE}
}
@String(friston2006free="Perhaps the earliest paper describing the FEP. Provides a great description of the fundamental intuitions behind the theory (in needs of living systems to reduce their internal entropy to keep conditions within homeostatic bounds)")

@article{friston2005theory,
  title={A theory of cortical responses},
  author={Friston, Karl},
  journal={Philosophical transactions of the Royal Society B: Biological sciences},
  volume={360},
  number={1456},
  pages={815--836},
  year={2005},
  publisher={The Royal Society London},
  keywords={classic},
  url={https://royalsocietypublishing.org/doi/abs/10.1098/rstb.2005.1622?casa_token=9zU-Epc4Iw4AAAAA%3AmYQq9buUvH2tb1xtL8VXFp0oHtJVGZ_4MSymueoSBUreJAhsqEOB3D-fXJnSqMnbTYP3VBo0BxwHWYE}
}
@String(friston2005theory="An early but complete description of predictive coding as an application of the FEP and variational inference under Gaussian and Laplace assumptions. Also surprisingly readable. This is core reading on predictive coding and the FEP")
@article{friston2003learning,
  title={Learning and inference in the brain},
  author={Friston, Karl},
  journal={Neural Networks},
  volume={16},
  number={9},
  pages={1325--1352},
  year={2003},
  publisher={Elsevier},
  keywords={classic},
  url={https://www.sciencedirect.com/science/article/pii/S0893608003002454?casa_token=Z-HR_To6rxwAAAAA:88ducipot59VHoRHJu1Ej6Kz5oLn-RMooUV9rR1fnkH50D5aNvLNENIF2XBa_3tZ0izMX5U2ED8}
}
@article{friston2009reinforcement,
  title={Reinforcement learning or active inference?},
  author={Friston, Karl J and Daunizeau, Jean and Kiebel, Stefan J},
  journal={PloS one},
  volume={4},
  number={7},
  pages={e6421},
  year={2009},
  publisher={Public Library of Science},
  keywords={classic},
  url={https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0006421}
}
@String(friston2009reinforcement="The earliest paper (I think) on active inference. Introduces the motivation behind the continuous state and time formulation of active inference. Shows how predictive coding can be used to learn actions as well as observations (by treating them the same)")

@article{friston2011action,
  title={Action understanding and active inference},
  author={Friston, Karl and Mattout, J{\'e}r{\'e}mie and Kilner, James},
  journal={Biological cybernetics},
  volume={104},
  number={1-2},
  pages={137--160},
  year={2011},
  publisher={Springer},
  keywords={classic},
  url={https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3491875/}
}
@String(friston2011action="Goes deep into the neuroscientific intuitions behind why you might want to think about action as a predicted observation and not a latent variable for biological brains. Presents Karl's view that action happens primarily at the periphery through simple 'reflex arcs' while all the real work is done by the generative models generating predictions." )
@article{karl2012free,
  title={A free energy principle for biological systems},
  author={Karl, Friston},
  journal={Entropy},
  volume={14},
  number={11},
  pages={2100--2121},
  year={2012},
  publisher={Multidisciplinary Digital Publishing Institute},
  keywords={classic},
  url={https://www.mdpi.com/1099-4300/14/11/2100}
}
@article{fortier2018woodlice,
  title={Of woodlice and men},
  author={Fortier, Martin and Friedman, Daniel A},
  journal={The editors wish to express their gratitude to Cordelia Erickson-Davis, Brendan Fleig-Goldstein, Ella Letort, Olivia Marcus, and Sarvin Tafazoli for their valuable help in proofreading the interviews, and to all the contributors for accepting to participate to this issue.},
  pages={17},
  year={2018},
  keywords={classic},
  url={https://www.aliusresearch.org/uploads/9/1/6/0/91600416/alius_bulletin_n%C2%B02__2018_.pdf#page=27}
}
@String(fortier2018woodlice="A great interview with Karl. Goes into a lot of his personal motivations underlying his work on the FEP. I would recommend this perhaps as an initial place to start out if you know nothing of the FEP to grasp the underlying motivations of *what* it is trying to explain.")
@article{friston2012history,
  title={The history of the future of the Bayesian brain},
  author={Friston, Karl},
  journal={NeuroImage},
  volume={62},
  number={2},
  pages={1230--1233},
  year={2012},
  publisher={Elsevier},
  keywords={classic},
  url={https://www.sciencedirect.com/science/article/pii/S1053811911011657}
}
@article{friston2012free,
  title={Free energy, value, and attractors},
  author={Friston, Karl and Ao, Ping},
  journal={Computational and mathematical methods in medicine},
  volume={2012},
  year={2012},
  publisher={Hindawi},
  keywords={classic},
  url={https://www.hindawi.com/journals/cmmm/2012/937860/}
}
@String(friston2012free="Mathematical paper by Karl and Ping Ao which begins fleshing out formally the notion of desires as attractors")
@article{feldman2010attention,
  title={Attention, uncertainty, and free-energy},
  author={Feldman, Harriet and Friston, Karl},
  journal={Frontiers in human neuroscience},
  volume={4},
  pages={215},
  year={2010},
  publisher={Frontiers},
  keywords={classic},
  url={https://www.frontiersin.org/articles/10.3389/fnhum.2010.00215/full}
}
@String(feldman2010attention="Makes a conjectured link between precision in predictive coding and attention in the brain.")
@article{friston2008hierarchical,
  title={Hierarchical models in the brain},
  author={Friston, Karl},
  journal={PLoS Comput Biol},
  volume={4},
  number={11},
  pages={e1000211},
  year={2008},
  publisher={Public Library of Science},
  keywords={classic},
  url={https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000211}
}
@String(friston2008hierarchical="Presents the 'full-construct' predictive coding model with both hierarchies and generalised coordinates.")
@article{friston2008variational,
  title={DEM: a variational treatment of dynamic systems},
  author={Friston, Karl J and Trujillo-Barreto, N and Daunizeau, Jean},
  journal={Neuroimage},
  volume={41},
  number={3},
  pages={849--885},
  year={2008},
  publisher={Elsevier},
  keywords={classic},
  url={https://www.sciencedirect.com/science/article/pii/S1053811908001894?casa_token=RBtljR9mpKMAAAAA:EAAQB59MLINQl8q4it_Pxnz6EbRaqvO0mMey40hdf29Qy0kKkH69qWN24jnmhcOXamuXWBqFAG4}
}
@String(friston2008variational="Extends predictive coding to generalised coordinates, and derives the necessary inference algorithms for working with them -- i.e. DEM, dynamic expectation maximisation.")
@article{friston2010generalised,
  title={Generalised filtering},
  author={Friston, Karl and Stephan, Klaas and Li, Baojuan and Daunizeau, Jean},
  journal={Mathematical Problems in Engineering},
  volume={2010},
  year={2010},
  publisher={Hindawi},
  keywords={classic},
  url={https://www.hindawi.com/journals/mpe/2010/621670/}
}
@article{ramstead2020tale,
  title={A tale of two densities: Active inference is enactive inference},
  author={Ramstead, Maxwell JD and Kirchhoff, Michael D and Friston, Karl J},
  journal={Adaptive Behavior},
  volume={28},
  number={4},
  pages={225--239},
  year={2020},
  publisher={SAGE Publications Sage UK: London, England},
  keywords={philosophy},
  url={https://journals.sagepub.com/doi/pdf/10.1177/1059712319862774}
}
@article{ramstead2018answering,
  title={Answering Schr{\"o}dinger's question: A free-energy formulation},
  author={Ramstead, Maxwell James D{\'e}sormeau and Badcock, Paul Benjamin and Friston, Karl John},
  journal={Physics of life reviews},
  volume={24},
  pages={1--16},
  year={2018},
  publisher={Elsevier},
  keywords={philosophy},
  url={https://www.sciencedirect.com/science/article/pii/S1571064517301409}
}
@article{veissiere2020thinking,
  title={Thinking through other minds: A variational approach to cognition and culture},
  author={Veissi{\`e}re, Samuel PL and Constant, Axel and Ramstead, Maxwell JD and Friston, Karl J and Kirmayer, Laurence J},
  journal={Behavioral and Brain Sciences},
  volume={43},
  year={2020},
  publisher={Cambridge University Press},
  keywords={philosophy},
  url={https://royalsocietypublishing.org/doi/full/10.1098/rsif.2017.0685}
}
@article{veissiere2020ttom,
  title={TTOM in action: Refining the variational approach to cognition and culture},
  author={Veissi{\`e}re, Samuel PL and Constant, Axel and Ramstead, Maxwell JD and Friston, Karl J and Kirmayer, Laurence J},
  journal={Behavioral and Brain Sciences},
  volume={43},
  year={2020},
  publisher={Cambridge University Press},
  keywords={philosophy},
  url={https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/abs/ttom-in-action-refining-the-variational-approach-to-cognition-and-culture/ADD060A9EE6937A3104FA23290F2C519}
}
@article{gershman2019does,
  title={What does the free energy principle tell us about the brain?},
  author={Gershman, Samuel J},
  journal={arXiv preprint arXiv:1901.07945},
  year={2019},
  keywords={philosophy},
  url={https://arxiv.org/abs/1901.07945}
}
@article{bruineberg2018anticipating,
  title={The anticipating brain is not a scientist: the free-energy principle from an ecological-enactive perspective},
  author={Bruineberg, Jelle and Kiverstein, Julian and Rietveld, Erik},
  journal={Synthese},
  volume={195},
  number={6},
  pages={2417--2444},
  year={2018},
  publisher={Springer},
  keywords={philosophy},
  url={https://link.springer.com/article/10.1007/s11229-016-1239-1}
}
@article{williams2018predictive,
  title={Predictive processing and the representation wars},
  author={Williams, Daniel},
  journal={Minds and Machines},
  volume={28},
  number={1},
  pages={141--172},
  year={2018},
  publisher={Springer},
  keywords={philosophy},
  url={https://link.springer.com/article/10.1007/s11023-017-9441-6}
}
@article{clark2013whatever,
  title={Whatever next? Predictive brains, situated agents, and the future of cognitive science},
  author={Clark, Andy},
  journal={Behavioral and brain sciences},
  volume={36},
  number={3},
  pages={181--204},
  year={2013},
  publisher={Cambridge University Press},
  keywords={philosophy},
  url={https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/whatever-next-predictive-brains-situated-agents-and-the-future-of-cognitive-science/33542C736E17E3D1D44E8D03BE5F4CD9}
}
@inproceedings{baltieri2020predictions,
  title={Predictions in the eye of the beholder: an active inference account of Watt governors},
  author={Baltieri, Manuel and Buckley, Christopher L and Bruineberg, Jelle},
  booktitle={Artificial Life Conference Proceedings},
  pages={121--129},
  year={2020},
  organization={MIT Press},
  keywords={philosophy},
  url={https://www.mitpressjournals.org/doi/abs/10.1162/isal_a_00288}
}
@book{clark2015surfing,
  title={Surfing uncertainty: Prediction, action, and the embodied mind},
  author={Clark, Andy},
  year={2015},
  publisher={Oxford University Press},
  keywords={classic},
  url={https://books.google.co.uk/books?hl=en&lr=&id=TnqECgAAQBAJ&oi=fnd&pg=PP1&dq=andy+clark+surfing+uncertainty&ots=aurm4jE3NO&sig=KxeHGJ6YJJdN9tKyr6snwDyBBKg&redir_esc=y#v=onepage&q=andy%20clark%20surfing%20uncertainty&f=false}
}
@String(friston2013life="A heuristic demonstration of the concept that Karl will later refer to as 'Bayesian mechanics', this paper surveys the notion that any random dynamical systems with the right kind of coupling among its sub-systems (i.e. a Markov blanket), will naturally appear as if it's performing a kind of approximate Bayesian inference. This argument is motivated by appeal to the existence of a non-equilibrium steady-state density, to which the system's probability distribution converges over time.")
@article{friston2013life,
  title={Life as we know it},
  author={Friston, Karl},
  journal={Journal of the Royal Society Interface},
  volume={10},
  number={86},
  pages={20130475},
  year={2013},
  publisher={The Royal Society},
  keywords={self-organisation},
  url={https://royalsocietypublishing.org/doi/full/10.1098/rsif.2013.0475}
}
@article{friston2015knowing,
  title={Knowing one's place: a free-energy approach to pattern regulation},
  author={Friston, Karl and Levin, Michael and Sengupta, Biswa and Pezzulo, Giovanni},
  journal={Journal of the Royal Society Interface},
  volume={12},
  number={105},
  pages={20141383},
  year={2015},
  publisher={The Royal Society},
  keywords={self-organisation},
  url={https://royalsocietypublishing.org/doi/full/10.1098/rsif.2014.1383}
}
@article{kuchling2019morphogenesis,
  title={Morphogenesis as Bayesian inference: A variational approach to pattern formation and control in complex biological systems},
  author={Kuchling, Franz and Friston, Karl and Georgiev, Georgi and Levin, Michael},
  journal={Physics of life reviews},
  year={2019},
  publisher={Elsevier},
  keywords={self-organisation},
  url={https://www.sciencedirect.com/science/article/pii/S1571064519300909?casa_token=IrMsxOkLhfYAAAAA:2agLQQPi8aeYTxxqotIPCyEzsHbpvOLwf_0eK5oW2Li0Gi5THHn2XRpWYfNT99M0Xy5pPD_S9CA}
}
@article{ramstead2020neural,
  title={Neural and phenotypic representation under the free-energy principle},
  author={Ramstead, Maxwell JD and Hesp, Casper and Tschantz, Alexander and Smith, Ryan and Constant, Axel and Friston, Karl},
  journal={Neuroscience \& Biobehavioral Reviews},
  year={2020},
  publisher={Elsevier},
  keywords={self-organisation},
  url={https://www.sciencedirect.com/science/article/pii/S0149763420306643?casa_token=16rC0ManFBUAAAAA:3mbntn5I7fObnA_Y397rvZbWrnUzkqmALD1LtS88tGrIRxbw9RQvU55XJuH-zKdBi6tPaN9faDM}
}
@article{friston2020parcels,
  title={Parcels and particles: Markov blankets in the brain},
  author={Friston, Karl J and Fagerholm, Erik D and Zarghami, Tahereh S and Parr, Thomas and Hip{\'o}lito, In{\^e}s and Magrou, Lo{\"\i}c and Razi, Adeel},
  journal={arXiv preprint arXiv:2007.09704},
  year={2020},
  keywords={self-organisation},
  url={https://arxiv.org/abs/2007.09704}
}
@article{hipolito2020markov,
  title={Markov blankets in the brain},
  author={Hipolito, Ines and Ramstead, Maxwell and Convertino, Laura and Bhat, Anjali and Friston, Karl and Parr, Thomas},
  journal={arXiv preprint arXiv:2006.02741},
  year={2020},
  keywords={self-organisation},
  url={https://arxiv.org/abs/2006.02741}
}
@String(parr2020modules="The 'free energy' response to the Fodorian notion of 'modularity' as an explanation of functional segregation, here motivated by an appeal to the stochastic dynamics of Markov-blanketed systems. Parr et al. argue that, given a particular conditional independency structure among the components that comprise a random dynamical system, one can interpret the system and its dynamics as entertaining a mean-field factorised generative model of its local environment, as opposed to appealing to philosophically or otherwise-unsatisfying notions such as 'modularity'.")
@article{parr2020modules,
  title={Modules or Mean-Fields?},
  author={Parr, Thomas and Sajid, Noor and Friston, Karl J},
  journal={Entropy},
  volume={22},
  number={5},
  pages={552},
  year={2020},
  publisher={Multidisciplinary Digital Publishing Institute},
  keywords={self-organisation},
  url={https://www.mdpi.com/1099-4300/22/5/552},
}
@article{palacios2017biological,
  title={Biological self-organisation and Markov blankets},
  author={Palacios, Ensor Rafael and Razi, Adeel and Parr, Thomas and Kirchhoff, Michael and Friston, Karl},
  journal={BioRxiv},
  pages={227181},
  year={2017},
  publisher={Cold Spring Harbor Laboratory},
  keywords={self-organisation},
  url={https://www.biorxiv.org/content/10.1101/227181v1.abstract}
}
@String(parr2020markov="This paper gives succinct and schematic treatments of several of the main concepts explored in a Free Energy Principle for a Particular Physics, particularly those related to Bayesian mechanics and information geometry. This work importantly delineates some of the conditions required of a system, so that its internal states approximately parameterise beliefs about external states. Fluctuation theorems are also invoked to relate the probability of trajectories or sequences of states to existing concepts in the active inference world, such as information gain, risk, and ambiguity resolution.")
@article{parr2020markov,
  title={Markov blankets, information geometry and stochastic thermodynamics},
  author={Parr, Thomas and Da Costa, Lancelot and Friston, Karl},
  journal={Philosophical Transactions of the Royal Society A},
  volume={378},
  number={2164},
  pages={20190159},
  year={2020},
  publisher={The Royal Society Publishing},
  keywords={information_geometry},
  url={https://royalsocietypublishing.org/doi/full/10.1098/rsta.2019.0159}
}
@article{da2020active,
  title={Active inference on discrete state-spaces: a synthesis},
  author={Da Costa, Lancelot and Parr, Thomas and Sajid, Noor and Veselic, Sebastijan and Neacsu, Victorita and Friston, Karl},
  journal={arXiv preprint arXiv:2001.07203},
  year={2020},
  keywords={tutorial},
  url={https://arxiv.org/abs/2001.07203}
}
@String(da2020active="This is a great and thorough tutorial on discrete-state-space active inference. I would reccomend it to everybody new to the field.")
@article{friston2015active,
  title={Active inference and epistemic value},
  author={Friston, Karl and Rigoli, Francesco and Ognibene, Dimitri and Mathys, Christoph and Fitzgerald, Thomas and Pezzulo, Giovanni},
  journal={Cognitive neuroscience},
  volume={6},
  number={4},
  pages={187--214},
  year={2015},
  publisher={Taylor \& Francis},
  keywords={discrete},
  url={https://www.tandfonline.com/doi/full/10.1080/17588928.2015.1020053?casa_token=IiMRlTIPAXUAAAAA%3ASvxdCeRv4yruAnjsNhletPuaWzdb8dfm-5s1YvTBaup1IgHNChHDKgCe1DY40DAvYHK6ZO4_guujAA}
}
@String(friston2015active="Introduces the main intuitions behind active inference, as well as the crucial epistemic foraging behaviour of the expected free energy. Illustrated on a simple T-maze task.")
@article{friston2016active,
  title={Active inference and learning},
  author={Friston, Karl and FitzGerald, Thomas and Rigoli, Francesco and Schwartenbeck, Philipp and Pezzulo, Giovanni and others},
  journal={Neuroscience \& Biobehavioral Reviews},
  volume={68},
  pages={862--879},
  year={2016},
  publisher={Elsevier},
  keywords={discrete},
  url={https://www.sciencedirect.com/science/article/pii/S0149763416301336}
}
@article{friston2012active,
  title={Active inference and agency: optimal control without cost functions},
  author={Friston, Karl and Samothrakis, Spyridon and Montague, Read},
  journal={Biological cybernetics},
  volume={106},
  number={8-9},
  pages={523--541},
  year={2012},
  publisher={Springer},
  keywords={discrete},
  url={https://link.springer.com/article/10.1007/s00422-012-0512-8}
}
@String(friston2012active="The first (I think) discrete-state-space paper on active inference. Notable for using the standard variational free energy as objective function and not the expected free energy. Describes some of the intuitions behind active inference.")
@article{friston2017active,
  title={Active inference: a process theory},
  author={Friston, Karl and FitzGerald, Thomas and Rigoli, Francesco and Schwartenbeck, Philipp and Pezzulo, Giovanni},
  journal={Neural computation},
  volume={29},
  number={1},
  pages={1--49},
  year={2017},
  publisher={MIT Press},
  keywords={discrete},
  url={https://www.mitpressjournals.org/doi/full/10.1162/NECO_a_00912}
}
@String(friston2017active="Provides a very good and thorough description of discrete-state-space active inference and ties its updates closely to neural physiology. I would reccomend this after the Da Costa introduction.")
@article{parr2017uncertainty,
  title={Uncertainty, epistemics and active inference},
  author={Parr, Thomas and Friston, Karl J},
  journal={Journal of The Royal Society Interface},
  volume={14},
  number={136},
  pages={20170376},
  year={2017},
  publisher={The Royal Society},
  keywords={discrete},
  url={https://royalsocietypublishing.org/doi/full/10.1098/rsif.2017.0376}
}
@article{friston2018deep,
  title={Deep temporal models and active inference},
  author={Friston, Karl J and Rosch, Richard and Parr, Thomas and Price, Cathy and Bowman, Howard},
  journal={Neuroscience \& Biobehavioral Reviews},
  volume={90},
  pages={486--501},
  year={2018},
  publisher={Elsevier},
  keywords={discrete},
  url={https://www.sciencedirect.com/science/article/pii/S0149763416307096}
}
@article{friston2017graphical,
  title={The graphical brain: belief propagation and active inference},
  author={Friston, Karl J and Parr, Thomas and de Vries, Bert},
  journal={Network Neuroscience},
  volume={1},
  number={4},
  pages={381--414},
  year={2017},
  publisher={MIT Press},
  keywords={free_energy},
  url={https://www.mitpressjournals.org/doi/full/10.1162/NETN_a_00018}
}
@String(friston2017graphical="Introduces the general factor-graph message passing viewpoint on active inference. Also introduces hierarchical active inference models.")
@article{parr2019neuronal,
  title={Neuronal message passing using Mean-field, Bethe, and Marginal approximations},
  author={Parr, Thomas and Markovic, Dimitrije and Kiebel, Stefan J and Friston, Karl J},
  journal={Scientific reports},
  volume={9},
  number={1},
  pages={1--18},
  year={2019},
  publisher={Nature Publishing Group},
  keywords={free_energy},
  url={https://www.nature.com/articles/s41598-018-38246-3}
}
@String(parr2019neuronal="Discusses in depth the different potential message passing inference algorithms which can be used to implement active inference on factor graphs.")
@article{schwobel2018active,
  title={Active inference, belief propagation, and the bethe approximation},
  author={Schw{\"o}bel, Sarah and Kiebel, Stefan and Markovi{\'c}, Dimitrije},
  journal={Neural computation},
  volume={30},
  number={9},
  pages={2530--2567},
  year={2018},
  publisher={MIT Press},
  keywords={free_energy},
  url={https://www.mitpressjournals.org/doi/abs/10.1162/neco_a_01108}
}
@String(schwobel2018active="Introduces the Bethe free energy, as a result of making the Bethe approximation instead of the mean-field variational assumption to derive the message passing algorithms.")
@article{parr2019generalised,
  title={Generalised free energy and active inference},
  author={Parr, Thomas and Friston, Karl J},
  journal={Biological cybernetics},
  volume={113},
  number={5-6},
  pages={495--513},
  year={2019},
  publisher={Springer},
  keywords={free_energy},
  url={https://link.springer.com/article/10.1007/s00422-019-00805-w}
}
@article{millidge2020whence,
  title={Whence the Expected Free Energy?},
  author={Millidge, Beren and Tschantz, Alexander and Buckley, Christopher L},
  journal={arXiv preprint arXiv:2004.08128},
  year={2020},
  keywords={free_energy},
  url={https://arxiv.org/abs/2004.08128}
}
@String(millidge2020whence="Discusses whether we can derive the expected free energy objective function on principled ground from the FEP, and discusses different potential objective functions for active inference.")
@article{bruineberg2020emperor,
  title={The Emperor’s New Markov Blankets},
  author={Bruineberg, Jelle and Dolega, Krzysztof and Dewhurst, Joe and Baltieri, Manuel},
  year={2020},
  keywords={self-organisation},
  url={http://philsci-archive.pitt.edu/18467/}
}
@article{friston2020sophisticated,
  title={Sophisticated Inference},
  author={Friston, Karl and Da Costa, Lancelot and Hafner, Danijar and Hesp, Casper and Parr, Thomas},
  journal={arXiv preprint arXiv:2006.04120},
  year={2020},
  keywords={discrete},
  url={https://arxiv.org/abs/2006.04120}
}
@String(friston2020sophisticated="Introduces the next stage of active inference. 'Sophisticated' active inference, where agents make decisions not just on their beliefs about the future, but on how their beliefs will change in the future. Allows the simulation of real epistemic value -- i.e. act so as to change your beliefs in the future.")
@article{sajid2019active,
  title={Active inference: demystified and compared},
  author={Sajid, Noor and Ball, Philip J and Friston, Karl J},
  journal={arXiv},
  pages={arXiv--1909},
  year={2019},
  keywords={discrete},
  url={https://ui.adsabs.harvard.edu/abs/2019arXiv190910863S/abstract}
}
@article{da2020relationship,
  title={The relationship between dynamic programming and active inference: The discrete, finite-horizon case},
  author={Da Costa, Lancelot and Sajid, Noor and Parr, Thomas and Friston, Karl and Smith, Ryan},
  journal={arXiv preprint arXiv:2009.08111},
  year={2020},
  keywords={discrete},
  url={https://arxiv.org/abs/2009.08111}
}
@String(da2020relationship="Discusses the relationship between active inference and dynamic programming solutions to reinforcement learning problems (i.e. Q learning, value functions etc). Shows that they are largely equivalent except with different objectives (Expected Free Energy vs Expected Discounted Reward).")
@article{friston2009reinforcement,
  title={Reinforcement learning or active inference?},
  author={Friston, Karl J and Daunizeau, Jean and Kiebel, Stefan J},
  journal={PloS one},
  volume={4},
  number={7},
  pages={e6421},
  year={2009},
  publisher={Public Library of Science},
  keywords={continuous},
  url={https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0006421}
}
@article{millidge2020relationship,
  title={On the Relationship Between Active Inference and Control as Inference},
  author={Millidge, Beren and Tschantz, Alexander and Seth, Anil K and Buckley, Christopher L},
  journal={arXiv preprint arXiv:2006.12964},
  year={2020},
  keywords={free_energy},
  url={https://arxiv.org/abs/2006.12964}
}
@String(millidge2020relationship="Discusses the relationship between Active Inference and Control as Inference, a variational framework for understanding action selection which has emerged from RL.")

@article{tschantz2020reinforcement,
  title={Reinforcement Learning through Active Inference},
  author={Tschantz, Alexander and Millidge, Beren and Seth, Anil K and Buckley, Christopher L},
  journal={arXiv preprint arXiv:2002.12636},
  year={2020},
  keywords={deep},
  url={https://arxiv.org/abs/2002.12636}
}
@String(tschantz2020reinforcement="Demonstrates that the exploration afforded by the Expected Free Energy Objective is useful in a deep reinforcement learning setting. Also maintains uncertainty through model ensembles applied in a model-based RL setting.")

@inproceedings{tschantz2020scaling,
  title={Scaling active inference},
  author={Tschantz, Alexander and Baltieri, Manuel and Seth, Anil K and Buckley, Christopher L},
  booktitle={2020 International Joint Conference on Neural Networks (IJCNN)},
  pages={1--8},
  year={2020},
  organization={IEEE},
  keywords={deep},
  url={https://ieeexplore.ieee.org/abstract/document/9207382/?casa_token=TfYG9cq3UvwAAAAA:Fn2oE7PCTGHlEN0jPQQE4P-qBiO_V-7xRFXXqYn7ubVoZeoiBd6ViBAxWf1L7j-R1wiKEOKvaXQ}
}
@String(tschantz2020scaling="Implements Deep Active Inference in a model-based RL setting using explicit planning with a transition model.")
@article{millidge2020deep,
  title={Deep active inference as variational policy gradients},
  author={Millidge, Beren},
  journal={Journal of Mathematical Psychology},
  volume={96},
  pages={102348},
  year={2020},
  publisher={Elsevier},
  keywords={deep},
  url={https://www.sciencedirect.com/science/article/pii/S0022249620300298?casa_token=GQLxvJzk3zMAAAAA:uotM5EqPWP9SIUV-5N8vvNnkVWSqFlS8W03MZ_W9GYCoyhWGRAhN9YmGjiTIaxCGHd4iwrjzElg}
}
@String(millidge2020deep="Implements deep active inference in a model-free policy gradient setting by amortising the learning of the expected-free-energy value function. Uses a transition model for the state-information gain term in the expected free energy.")
@article{ueltzhoffer2018deep,
  title={Deep active inference},
  author={Ueltzh{\"o}ffer, Kai},
  journal={Biological cybernetics},
  volume={112},
  number={6},
  pages={547--573},
  year={2018},
  publisher={Springer},
  keywords={deep},
  url={https://link.springer.com/article/10.1007/s00422-018-0785-7}
}
@String(ueltzhoffer2018deep="The first paper to try combining active inference with deep neural networks. Demonstrates the importance of the exploratory terms of the EFE to solve the mountain-car problem.")

@article{fountas2020deep,
  title={Deep active inference agents using Monte-Carlo methods},
  author={Fountas, Zafeirios and Sajid, Noor and Mediano, Pedro AM and Friston, Karl},
  journal={arXiv preprint arXiv:2006.04176},
  year={2020},
  keywords={deep},
  url={https://arxiv.org/abs/2006.04176}
}
@inproceedings{baltieri2017active,
  title={An active inference implementation of phototaxis},
  author={Baltieri, Manuel and Buckley, Christopher L},
  booktitle={Artificial Life Conference Proceedings 14},
  pages={36--43},
  year={2017},
  organization={MIT Press},
  keywords={continuous},
  url={https://www.mitpressjournals.org/doi/abs/10.1162/isal_a_011}
}
@String(baltieri2017active="Active inference in plants!!!")

@article{baltieri2019pid,
  title={PID control as a process of active inference with linear generative models},
  author={Baltieri, Manuel and Buckley, Christopher L},
  journal={Entropy},
  volume={21},
  number={3},
  pages={257},
  year={2019},
  publisher={Multidisciplinary Digital Publishing Institute},
  keywords={continuous},
  url={https://www.mdpi.com/1099-4300/21/3/257}
}
@String(baltieri2019pid="Active inference under a linear gaussian generative model can replicate PID, but also provide a natural method for learning the tuning coefficients (by understanding them as precisions).")
@article{baltieri2020kalman,
  title={On Kalman-Bucy filters, linear quadratic control and active inference},
  author={Baltieri, Manuel and Buckley, Christopher L},
  journal={arXiv preprint arXiv:2005.06269},
  year={2020},
  keywords={continuous},
  url={https://arxiv.org/abs/2005.06269}
}
@String(baltieri2020kalman="A key step towards understanding how active inference relates to classical control theory methods such as Kalman Filters and LQR control.")
@article{watson2020active,
  title={Active Inference or Control as Inference? A Unifying View},
  author={Watson, Joe and Imohiosen, Abraham and Peters, Jan},
  journal={arXiv preprint arXiv:2010.00262},
  year={2020},
  keywords={continous},
  url={https://arxiv.org/abs/2010.00262}
}
@article{van2019application,
  title={Application of the Free Energy Principle to Estimation and Control},
  author={van de Laar, Thijs and {\"O}z{\c{c}}elikkale, Ay{\c{c}}a and Wymeersch, Henk},
  journal={arXiv preprint arXiv:1910.09823},
  year={2019},
  keywords={continuous},
  url={https://arxiv.org/abs/1910.09823}
}
@String(van2019application="Another approach to understanding how active inference relates to and extends classical control theory methods.")
@article{grimbergen2019state,
  title={The State Space Formulation of Active Inference: Towards Brain-Inspired Robot Control},
  author={Grimbergen, Sherin},
  year={2019},
  keywords={continuous},
  url={https://repository.tudelft.nl/islandora/object/uuid:0f56c37c-d22b-478b-8a85-dca615a8f419}
}
@String(grimbergen2019state="An excellent overview and fantastic piece of work on the linear time-indepenent formulation of active inference and its relation to classical control theory.")
@article{pio2016active,
  title={Active inference and robot control: a case study},
  author={Pio-Lopez, L{\'e}o and Nizard, Ange and Friston, Karl and Pezzulo, Giovanni},
  journal={Journal of The Royal Society Interface},
  volume={13},
  number={122},
  pages={20160616},
  year={2016},
  publisher={The Royal Society},
  keywords={robotics},
  url={https://royalsocietypublishing.org/doi/full/10.1098/rsif.2016.0616}
}
@article{oliver2019active,
  title={Active inference body perception and action for humanoid robots},
  author={Oliver, Guillermo and Lanillos, Pablo and Cheng, Gordon},
  journal={arXiv preprint arXiv:1906.03022},
  year={2019},
  keywords={robotics},
  url={https://arxiv.org/abs/1906.03022}
}
@article{sancaktar2019end,
  title={End-to-end pixel-based deep active inference for body perception and action},
  author={Sancaktar, Cansu and Lanillos, Pablo},
  journal={arXiv preprint arXiv:2001.05847},
  year={2019},
  keywords={robotics},
  url={https://arxiv.org/abs/2001.05847}
}
@article{vanderbroeck2019active,
  title={Active inference for robot control: A factor graph approach},
  author={Vanderbroeck, Mees and Baioumy, Mohamed and van der Lans, Daan and de Rooij, Rens and van der Werf, Tiis},
  journal={Student Undergraduate Research E-journal!},
  volume={5},
  pages={1--5},
  year={2019},
  keywords={robotics},
  url={https://pdfs.semanticscholar.org/e177/b21b0a7f43ad3969ceb42bd8f1d912ea8d43.pdf}
}
@article{pezzato2020novel,
  title={A novel adaptive controller for robot manipulators based on active inference},
  author={Pezzato, Corrado and Ferrari, Riccardo and Corbato, Carlos Hern{\'a}ndez},
  journal={IEEE Robotics and Automation Letters},
  volume={5},
  number={2},
  pages={2973--2980},
  year={2020},
  publisher={IEEE},
  keywords={robotics},
  url={https://ieeexplore.ieee.org/abstract/document/9000729/?casa_token=OBL93SqhogUAAAAA:6w99NtC-Fk7P0tIiXx6QmTNsWnPS-GKK0OtsJEz-HWgniXwpY0Rtue_qlc5Fe9HQ2vj0ropx7hM}
}
@article{pezzulo2018hierarchical,
  title={Hierarchical active inference: A theory of motivated control},
  author={Pezzulo, Giovanni and Rigoli, Francesco and Friston, Karl J},
  journal={Trends in cognitive sciences},
  volume={22},
  number={4},
  pages={294--306},
  year={2018},
  publisher={Elsevier},
  keywords={continuous},
  url={https://www.sciencedirect.com/science/article/pii/S1364661318300226},
}
@article{corcoran2020allostatic,
  title={From allostatic agents to counterfactual cognisers: active inference, biological regulation, and the origins of cognition},
  author={Corcoran, Andrew W and Pezzulo, Giovanni and Hohwy, Jakob},
  journal={Biology \& Philosophy},
  volume={35},
  number={3},
  pages={1--45},
  year={2020},
  publisher={Springer},
  keywords={philosophy},
  url={https://link.springer.com/article/10.1007/s10539-020-09746-2},
}
@article{seth2013interoceptive,
  title={Interoceptive inference, emotion, and the embodied self},
  author={Seth, Anil K},
  journal={Trends in cognitive sciences},
  volume={17},
  number={11},
  pages={565--573},
  year={2013},
  publisher={Elsevier},
  keywords={philosophy},
  url={https://www.sciencedirect.com/science/article/pii/S1364661313002118}
}
@article{seth2016active,
  title={Active interoceptive inference and the emotional brain},
  author={Seth, Anil K and Friston, Karl J},
  journal={Philosophical Transactions of the Royal Society B: Biological Sciences},
  volume={371},
  number={1708},
  pages={20160007},
  year={2016},
  publisher={The Royal Society},
  keywords={philosophy},
  url={https://royalsocietypublishing.org/doi/full/10.1098/rstb.2016.0007}
}
@book{seth2014cybernetic,
  title={The cybernetic Bayesian brain},
  author={Seth, Anil K},
  year={2014},
  publisher={Open MIND. Frankfurt am Main: MIND Group},
  keywords={philosophy},
  url={https://open-mind.net/papers/the-cybernetic-bayesian-brain},
}
@article{seth2015presence,
  title={Presence, objecthood, and the phenomenology of predictive perception},
  author={Seth, Anil K},
  journal={Cognitive neuroscience},
  volume={6},
  number={2-3},
  pages={111--117},
  year={2015},
  publisher={Taylor \& Francis},
  keywords={philosophy},
  url={https://www.tandfonline.com/doi/full/10.1080/17588928.2015.1026888?casa_token=IA7GL_oM2VAAAAAA%3AXkJ7HbhxHcW7qCdSWyRSerOo8iZevM3x_8QV1b7C7qAvAqJMveeq4IeGRBTCCbQOCJ6Ix9p70VkKww}
}


@String(friston2008variational2="Foundational treatment of variational inference for dynamical systems, as represented in generalised coordinates. Also relates variational filtering to other non-variational schemes like particle filtering and Kalman filtering.")
@article{friston2008variational2,
  title={Variational filtering},
  author={Friston, Karl J},
  journal={NeuroImage},
  volume={41},
  number={3},
  pages={747--766},
  year={2008},
  publisher={Elsevier},
  keywords={classic},
  url={https://www.sciencedirect.com/science/article/pii/S1053811908002462?casa_token=bzK7h_aIzY0AAAAA:rg1CzE6vNo-cktIHO_9EAoqmR5Zpy89klEn-Wy3NAzMoR8NcWgaF5_zEzyhrRB76N5RPyCZTIlY}
}

@article{smith2020recent,
  title={Recent advances in the application of predictive coding and active inference models within clinical neuroscience},
  author={Smith, Ryan and Badcock, Paul and Friston, Karl J},
  journal={Psychiatry and Clinical Neurosciences},
  year={2020},
  publisher={Wiley Online Library},
  keywords={applications},
  url={https://onlinelibrary.wiley.com/doi/abs/10.1111/pcn.13138}
}
@String(smith2020recent="A comprehensive review of neuroscientific and computational psychiatry applications of the FEP and Active Inference.")

@article{whyte2020predictive,
  title={The Predictive Global Neuronal Workspace: A Formal Active Inference Model of Visual Consciousness},
  author={Whyte, Christopher J and Smith, Ryan},
  journal={bioRxiv},
  year={2020},
  publisher={Cold Spring Harbor Laboratory},
  keywords={applications},
  url={https://www.sciencedirect.com/science/article/pii/S0301008220301738}
}
@article{smith2019neurocomputational,
  title={Neurocomputational mechanisms underlying emotional awareness: insights afforded by deep active inference and their potential clinical relevance},
  author={Smith, Ryan and Lane, Richard D and Parr, Thomas and Friston, Karl J},
  journal={Neuroscience \& Biobehavioral Reviews},
  volume={107},
  pages={473--491},
  year={2019},
  publisher={Elsevier},
  keywords={applications},
  url={https://www.sciencedirect.com/science/article/pii/S014976341930541X?casa_token=c1UILIbIsKYAAAAA:tQbclbvyieFsSPf_oqulTP8Manv6fU6CeI1iHGZe5Iq4TryLR4pGRjK0Y7RD4gZCfjJo02uWyvw}
}

@article{smith2019simulating,
  title={Simulating emotions: An active inference model of emotional state inference and emotion concept learning},
  author={Smith, Ryan and Parr, Thomas and Friston, Karl J},
  journal={Frontiers in psychology},
  volume={10},
  pages={2844},
  year={2019},
  publisher={Frontiers},
  keywords={applications},
  url={https://www.frontiersin.org/articles/10.3389/fpsyg.2019.02844/full}
}
@article{andrews2020math,
  title={The Math is not the Territory: Navigating the Free Energy Principle},
  author={Andrews, Mel},
  year={2020},
  keywords={philosophy},
  url={http://philsci-archive.pitt.edu/18315/}
}
@article{smith2017hierarchical,
  title={The hierarchical basis of neurovisceral integration},
  author={Smith, Ryan and Thayer, Julian F and Khalsa, Sahib S and Lane, Richard D},
  journal={Neuroscience \& biobehavioral reviews},
  volume={75},
  pages={274--296},
  year={2017},
  publisher={Elsevier},
  keywords={applications},
  url={https://www.sciencedirect.com/science/article/pii/S014976341630673X?casa_token=V0kmqFxZWg4AAAAA:QE5VJB7jp6k22u8L7S7iWYh2_EiLV9a4gyXGUfSMIXOte_zYxGsH2YXUkvKY6PeDKriYy8nu11o}
}
@inproceedings{lanillos2018adaptive,
  title={Adaptive robot body learning and estimation through predictive coding},
  author={Lanillos, Pablo and Cheng, Gordon},
  booktitle={2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={4083--4090},
  year={2018},
  organization={IEEE},
  keywords={robotics},
  url={https://ieeexplore.ieee.org/document/8593684/}
}
@article{friston2010action,
  title={Action and behavior: a free-energy formulation},
  author={Friston, Karl J and Daunizeau, Jean and Kilner, James and Kiebel, Stefan J},
  journal={Biological cybernetics},
  volume={102},
  number={3},
  pages={227--260},
  year={2010},
  publisher={Springer},
  keywords={classic},
  url={https://link.springer.com/article/10.1007/s00422-010-0364-z}
}
@article{cullen2018active,
  title={Active inference in OpenAI gym: a paradigm for computational investigations into psychiatric illness},
  author={Cullen, Maell and Davey, Ben and Friston, Karl J and Moran, Rosalyn J},
  journal={Biological psychiatry: cognitive neuroscience and neuroimaging},
  volume={3},
  number={9},
  pages={809--818},
  year={2018},
  publisher={Elsevier},
  keywords={applications},
  url={https://www.sciencedirect.com/science/article/pii/S2451902218301617?casa_token=HRn2UHzCR18AAAAA:lTqZvwAL3WoYmchm2W2WYXKsastkTOVcEDpu9Fxtm5aZIfr6jmhexYHurChmmpFaNufNEpFuuPo}
}