A free energy principle for a particular physics

This monograph attempts a theory of every 'thing' that can be distinguished from other things in a statistical sense. The ensuing statistical independencies, mediated by Markov blankets, speak to a recursive composition of ensembles (of things) at increasingly higher spatiotemporal scales. This decomposition provides a description of small things; e.g., quantum mechanics - via the Schrodinger equation, ensembles of small things - via statistical mechanics and related fluctuation theorems, through to big things - via classical mechanics. These descriptions are complemented with a Bayesian mechanics for autonomous or active things. Although this work provides a formulation of every thing, its main contribution is to examine the implications of Markov blankets for self-organisation to nonequilibrium steady-state. In brief, we recover an information geometry and accompanying free energy principle that allows one to interpret the internal states of something as representing or making inferences about its external states. The ensuing Bayesian mechanics is compatible with quantum, statistical and classical mechanics and may offer a formal description of lifelike particles.

[1]  The Principle of Relativity , 2013, Nature.

[2]  E. Schrödinger What is life? : the physical aspect of the living cell , 1944 .

[3]  W. Ashby,et al.  Principles of the self-organizing dynamic system. , 1947, The Journal of general psychology.

[4]  R. Feynman,et al.  Space-Time Approach to Non-Relativistic Quantum Mechanics , 1948 .

[5]  H. Jehle,et al.  Albert Einstein: Philosopher-Scientist. , 1951 .

[6]  A. Turing The chemical basis of morphogenesis , 1952, Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences.

[7]  D. Bohm A SUGGESTED INTERPRETATION OF THE QUANTUM THEORY IN TERMS OF "HIDDEN" VARIABLES. II , 1952 .

[8]  E. Jaynes Information Theory and Statistical Mechanics , 1957 .

[9]  Myron Tribus,et al.  Thermostatics and thermodynamics : an introduction to energy, information and states of matter, with engineering applications , 1961 .

[10]  Ernest Nagel,et al.  The Structure of Science , 1962 .

[11]  Rolf Landauer,et al.  Irreversibility and heat generation in the computing process , 1961, IBM J. Res. Dev..

[12]  R. L. Stratonovich,et al.  Topics in the theory of random noise , 1967 .

[13]  E. Lorenz Deterministic nonperiodic flow , 1963 .

[14]  H. D. Miller,et al.  The Theory Of Stochastic Processes , 1977, The Mathematical Gazette.

[15]  Alfréd Rényi,et al.  Probability Theory , 1970 .

[16]  W. Ashby,et al.  Every Good Regulator of a System Must Be a Model of That System , 1970 .

[17]  Leslie E Ballentine,et al.  The statistical interpretation of quantum mechanics , 1970 .

[18]  The Selected Writings of Hermann von Helmholtz , 1972 .

[19]  D. Campbell ‘Downward Causation’ in Hierarchically Organised Biological Systems , 1974 .

[20]  H. Barlow Inductive Inference, Coding, Perception, and Language , 1974, Perception.

[21]  Francisco J. Ayala,et al.  Studies in the philosophy of biology. Reduction and related problems , 1974, Medical History.

[22]  H. Haken Synergetics: an Introduction, Nonequilibrium Phase Transitions and Self-organization in Physics, Chemistry, and Biology , 1977 .

[23]  I. Prigogine Time, Structure, and Fluctuations , 1978, Science.

[24]  D. S. Jones,et al.  Elementary information theory , 1979 .

[25]  J. Carr Applications of Centre Manifold Theory , 1981 .

[26]  F. Takens Detecting strange attractors in turbulence , 1981 .

[27]  G. Parisi,et al.  Supersymmetric field theories and stochastic differential equations , 1982 .

[28]  Solitons and instantons, operator quantization , 1986 .

[29]  J. Barrow,et al.  The Anthropic Cosmological Principle , 1987 .

[30]  Tang,et al.  Self-Organized Criticality: An Explanation of 1/f Noise , 2011 .

[31]  P. Bak,et al.  Self-organized criticality. , 1988, Physical review. A, General physics.

[32]  Tang,et al.  Self-organized criticality. , 1988, Physical review. A, General physics.

[33]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[34]  R Linsker,et al.  Perceptual neural organization: some approaches based on network models and information theory. , 1990, Annual review of neuroscience.

[35]  F. D. Silva Neural mechanisms underlying brain waves: from neural membranes to networks. , 1991 .

[36]  S. Kauffman,et al.  Coevolution to the edge of chaos: coupled fitness landscapes, poised states, and coevolutionary avalanches. , 1991, Journal of theoretical biology.

[37]  Geoffrey E. Hinton,et al.  Autoencoders, Minimum Description Length and Helmholtz Free Energy , 1993, NIPS.

[38]  Douglas Poland,et al.  Cooperative catalysis and chemical chaos: a chemical model for the Lorenz equations , 1993 .

[39]  H. Crauel,et al.  Attractors for random dynamical systems , 1994 .

[40]  W J Freeman,et al.  Characterization of state transitions in spatially distributed, chaotic, nonlinear, dynamical systems in cerebral cortex , 1994, Integrative physiological and behavioral science : the official journal of the Pavlovian Society.

[41]  R. Peierls,et al.  The observational foundations of physics , 1994 .

[42]  G. Edelman,et al.  A measure for brain complexity: relating functional segregation and integration in the nervous system. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[43]  The kiss of chaos and the sleeping beauty of psychology. , 1995 .

[44]  N C Andreasen,et al.  Symptoms of Schizophrenia: Methods, Meanings, and Mechanisms , 1995 .

[45]  Helen E. Longino,et al.  Discovering Complexity: Decomposition and Localization as Strategies in Scientific Research , 1995 .

[46]  J. Cardy Scaling and Renormalization in Statistical Physics , 1996 .

[47]  J. Yorke,et al.  Differentiable generalized synchronization of chaos , 1997 .

[48]  R. Littlejohn,et al.  Gauge fields in the separation of rotations and internal motions in the n-body problem , 1997 .

[49]  C. Jarzynski Nonequilibrium Equality for Free Energy Differences , 1996, cond-mat/9610209.

[50]  Kestutis Pyragas Conditional Lyapunov exponents from time series , 1997 .

[51]  Ken Sekimoto,et al.  Langevin Equation and Thermodynamics , 1998 .

[52]  Alessandro Vespignani,et al.  How self-organized criticality works: A unified mean-field picture , 1997, cond-mat/9709192.

[53]  Shun-ichi Amari,et al.  Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.

[54]  B. Balleine,et al.  Goal-directed instrumental action: contingency and incentive learning and their cortical substrates , 1998, Neuropharmacology.

[55]  A. Goldman,et al.  Mirror neurons and the simulation theory of mind-reading , 1998, Trends in Cognitive Sciences.

[56]  G Buzsáki,et al.  Memory consolidation during sleep: a neurophysiological perspective. , 1998, Journal of sleep research.

[57]  Doina Precup,et al.  Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning , 1999, Artif. Intell..

[58]  Zoubin Ghahramani,et al.  A Unifying Review of Linear Gaussian Models , 1999, Neural Computation.

[59]  Hans Crauel,et al.  Global random attractors are uniquely determined by attracting deterministic compact sets , 1999 .

[60]  Jaegwon Kim,et al.  Making Sense of Emergence , 1999 .

[61]  J. Wheeler Information, physics, quantum: the search for links , 1999 .

[62]  T. Cassidy,et al.  Stress, Cognition and Health , 1999 .

[63]  David L. Dowe,et al.  Minimum Message Length and Kolmogorov Complexity , 1999, Comput. J..

[64]  Rajesh P. N. Rao,et al.  Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. , 1999 .

[65]  Percolation transition and the onset of nonexponential relaxation in fully frustrated models , 1998, cond-mat/9803202.

[66]  W. C. Kerr,et al.  Generalized phase space version of Langevin equations and associated Fokker-Planck equations , 2000 .

[67]  J. Olsen,et al.  Molecular electronic-structure theory , 2000 .

[68]  K. Elworthy RANDOM DYNAMICAL SYSTEMS (Springer Monographs in Mathematics) , 2000 .

[69]  Naftali Tishby,et al.  Predictability, Complexity, and Learning , 2000, Neural Computation.

[70]  F. Varela,et al.  Radical embodiment: neural dynamics and consciousness , 2001, Trends in Cognitive Sciences.

[71]  B Cessac,et al.  Lyapunov exponents and transport in the Zhang model of self-organized criticality. , 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

[72]  I. Tsuda Toward an interpretation of dynamic neural activity in terms of chaotic dynamical systems. , 2001, The Behavioral and brain sciences.

[73]  Robert W. Batterman,et al.  The devil in the details : asymptotic reasoning in explanation, reduction, and emergence , 2002 .

[74]  W. Fleming,et al.  Risk‐Sensitive Control and an Optimal Investment Model , 2000 .

[75]  Debra J. Searles,et al.  The Fluctuation Theorem , 2002 .

[76]  S. Boccaletti,et al.  Synchronization of chaotic systems , 2001 .

[77]  Charles H. Bennett,et al.  Notes on Landauer's Principle, Reversible Computation, and Maxwell's Demon , 2002, physics/0210005.

[78]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[79]  Matthew J. Beal Variational algorithms for approximate Bayesian inference , 2003 .

[80]  John M. Beggs,et al.  Neuronal Avalanches in Neocortical Circuits , 2003, The Journal of Neuroscience.

[81]  Hagai Attias,et al.  Planning by Probabilistic Inference , 2003, AISTATS.

[82]  Evelyn Sander,et al.  The geometry of chaos synchronization. , 2003, Chaos.

[83]  W. Zurek Decoherence, einselection, and the quantum origins of the classical , 2001, quant-ph/0105127.

[84]  Ichiro Tsuda,et al.  A Complex Systems Approach to an Interpretation of Dynamic Brain Activity I: Chaotic Itinerancy Can Provide a Mathematical Basis for Information Processing in Cortical Transitory and Nonstationary Dynamics , 2003, Summer School on Neural Networks.

[85]  S. Nara Can potentially useful dynamics to solve complex problems emerge from constrained chaos and/or chaotic itinerancy? , 2003, Chaos.

[86]  Karl J. Friston,et al.  Comparing dynamic causal models , 2004, NeuroImage.

[87]  D. Knill,et al.  The Bayesian brain: the role of uncertainty in neural coding and computation , 2004, Trends in Neurosciences.

[88]  T. Frank Nonlinear Fokker-Planck Equations: Fundamentals and Applications , 2004 .

[89]  J. Fuster Upper processing stages of the perception–action cycle , 2004, Trends in Cognitive Sciences.

[90]  G. Rizzolatti,et al.  The mirror-neuron system. , 2004, Annual review of neuroscience.

[91]  Jun Namikawa,et al.  Chaotic itinerancy and power-law residence time distribution in stochastic dynamical systems. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[92]  Trevor Darrell,et al.  Avoiding the "streetlight effect": tracking by exploring likelihood modes , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[93]  M. E. J. Newman,et al.  Power laws, Pareto distributions and Zipf's law , 2005 .

[94]  H. Kappen Path integrals and symmetry breaking for optimal control theory , 2005, physics/0505066.

[95]  M. Schlosshauer Decoherence, the measurement problem, and interpretations of quantum mechanics , 2003, quant-ph/0312059.

[96]  Michael Breakspear,et al.  Dynamics of a neural system with a multiscale architecture , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[97]  Jürgen Schmidhuber,et al.  Optimal Artificial Curiosity, Creativity, Music, and the Fine Arts , 2005 .

[98]  T. Sejnowski,et al.  Network Oscillations: Emerging Computational Principles , 2006, The Journal of Neuroscience.

[99]  Karl J. Friston,et al.  A free energy principle for the brain , 2006, Journal of Physiology-Paris.

[100]  Michael J Davis,et al.  Low-dimensional manifolds in reaction-diffusion equations. 1. Fundamental aspects. , 2006, The journal of physical chemistry. A.

[101]  J. O’Keefe,et al.  An oscillatory interference model of grid cell firing , 2007, Hippocampus.

[102]  D. Plenz,et al.  The organizing principles of neuronal avalanches: cell assemblies in the cortex? , 2007, Trends in Neurosciences.

[103]  Karl J. Friston,et al.  Variational free energy and the Laplace approximation , 2007, NeuroImage.

[104]  Gavin E Crooks,et al.  Measuring thermodynamic length. , 2007, Physical review letters.

[105]  Justin Dauwels,et al.  On Variational Message Passing on Factor Graphs , 2007, 2007 IEEE International Symposium on Information Theory.

[106]  William Bechtel,et al.  Top-down Causation Without Top-down Causes , 2007 .

[107]  H. Haken,et al.  Intentionality in non-equilibrium systems? The functional aspects of self-organized pattern formation , 2007 .

[108]  E. Thompson,et al.  Mind in life : biology, phenomenology, and the sciences of mind , 2007 .

[109]  Many Worlds in One , 2001, gr-qc/0102010.

[110]  Karl J. Friston,et al.  Predictive coding: an account of the mirror neuron system , 2007, Cognitive Processing.

[111]  Pierre-Yves Oudeyer,et al.  What is Intrinsic Motivation? A Typology of Computational Approaches , 2007, Frontiers Neurorobotics.

[112]  P. Ao Stochastic Dynamical Structure (SDS) of Nonequilibrium Processes in the Absence of Detailed Balance. II: construction of SDS with nonlinear force and multiplicative noise , 2004, 0803.4356.

[113]  Mark A. Bedau,et al.  Emergence : contemporary readings in philosophy and science , 2008 .

[114]  P. Ao Emerging of Stochastic Dynamical Equalities and Steady State Thermodynamics from Darwinian Dynamics. , 2008, Communications in theoretical physics.

[115]  G. Shean,et al.  Symptoms of schizophrenia and social cognition , 2009, Psychiatry Research.

[116]  W. Zurek Quantum Darwinism , 2009, 0903.5082.

[117]  M. Esposito,et al.  Nonequilibrium fluctuations, fluctuation theorems, and counting statistics in quantum systems , 2008, 0811.3717.

[118]  Karl J. Friston,et al.  Population dynamics under the Laplace assumption , 2009, NeuroImage.

[119]  Pierre Baldi,et al.  Bayesian surprise attracts human attention , 2005, Vision Research.

[120]  Pierre-Yves Oudeyer,et al.  R-IAC: Robust Intrinsically Motivated Exploration and Active Learning , 2009, IEEE Transactions on Autonomous Mental Development.

[121]  H. Meyer-Ortmanns,et al.  On the role of frustration in excitable systems. , 2010, Chaos.

[122]  Christoph Kayser,et al.  Complex Times for Earthquakes, Stocks, and the Brain's Activity , 2010, Neuron.

[123]  Jürgen Schmidhuber,et al.  Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010) , 2010, IEEE Transactions on Autonomous Mental Development.

[124]  Andreas Daffertshofer,et al.  Generative Models of Cortical Oscillations: Neurobiological Implications of the Kuramoto Model , 2010, Front. Hum. Neurosci..

[125]  Aihua Hu,et al.  The existence of generalized synchronization of chaotic systems in complex networks. , 2010, Chaos.

[126]  W. Singer,et al.  Abnormal neural oscillations and synchrony in schizophrenia , 2010, Nature Reviews Neuroscience.

[127]  Hilbert J. Kappen,et al.  Risk Sensitive Path Integral Control , 2010, UAI.

[128]  S. Ramaswamy The Mechanics and Statistics of Active Matter , 2010, 1004.1933.

[129]  Yian Ma,et al.  Potential function in dynamical systems and the relation with Lyapunov function , 2011, Proceedings of the 30th Chinese Control Conference.

[130]  G. Sugihara,et al.  Generalized Theorems for Nonlinear State Space Reconstruction , 2011, PloS one.

[131]  Daniel Polani,et al.  Information Theory of Decisions and Actions , 2011 .

[132]  Yi Sun,et al.  Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments , 2011, AGI.

[133]  Doina Precup,et al.  An information-theoretic approach to curiosity-driven reinforcement learning , 2012, Theory in Biosciences.

[134]  S. Capozziello,et al.  Extended Theories of Gravity , 2011, 1108.6266.

[135]  Woodrow L. Shew,et al.  Information Capacity and Transmission Are Maximized in Balanced Cortical Networks with Neuronal Avalanches , 2010, The Journal of Neuroscience.

[136]  Jürgen Schmidhuber,et al.  Artificial General Intelligence - 4th International Conference, AGI 2011, Mountain View, CA, USA, August 3-6, 2011. Proceedings , 2011, AGI.

[137]  Karl J. Friston,et al.  Action understanding and active inference , 2011, Biological Cybernetics.

[138]  Karl J. Friston,et al.  Perceptions as Hypotheses: Saccades as Experiments , 2012, Front. Psychology.

[139]  J. Kwapień,et al.  Physical approach to complex systems , 2012 .

[140]  H. B. Barlow,et al.  Possible Principles Underlying the Transformations of Sensory Messages , 2012 .

[141]  J. Lisman Excitation, inhibition, local oscillations, or large-scale loops: what causes the symptoms of schizophrenia? , 2012, Current Opinion in Neurobiology.

[142]  G. P. Pavlos,et al.  Tsallis non-extensive statistics, intermittent turbulence, SOC and chaos in the solar plasma, Part one: Sunspot dynamics , 2012 .

[143]  Karl J. Friston,et al.  Predictions not commands: active inference in the motor system , 2012, Brain Structure and Function.

[144]  M. Botvinick,et al.  Planning as inference , 2012, Trends in Cognitive Sciences.

[145]  Karl J. Friston,et al.  Canonical Microcircuits for Predictive Coding , 2012, Neuron.

[146]  David Poeppel,et al.  Cortical oscillations and speech processing: emerging computational principles and operations , 2012, Nature Neuroscience.

[147]  Vicenç Gómez,et al.  Optimal control as a graphical model inference problem , 2009, Machine Learning.

[148]  Robert Haslinger,et al.  Statistical modeling approach for detecting generalized synchronization. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[149]  Karl J. Friston,et al.  Free Energy, Value, and Attractors , 2011, Comput. Math. Methods Medicine.

[150]  Bernard W. Balleine,et al.  Actions, Action Sequences and Habits: Evidence That Goal-Directed and Habitual Action Control Are Hierarchically Organized , 2013, PLoS Comput. Biol..

[151]  Menas C. Kafatos,et al.  Complementarity in biological systems: A complexity view , 2013, Complex..

[152]  Jin Wang,et al.  Nonequilibrium landscape theory of neural networks , 2013, Proceedings of the National Academy of Sciences.

[153]  G. Buzsáki,et al.  Memory, navigation and theta rhythm in the hippocampal-entorhinal system , 2013, Nature Neuroscience.

[154]  Tim Palmer,et al.  Singular vectors, predictability and ensemble forecasting for weather and climate , 2013 .

[155]  Karl J. Friston Life as we know it , 2013, Journal of The Royal Society Interface.

[156]  J. Proust,et al.  The Philosophy of Metacognition: Mental Agency and Self-Awareness , 2013 .

[157]  P. Bressloff,et al.  Stochastic models of intracellular transport , 2013 .

[158]  P. Dayan,et al.  Goals and Habits in the Brain , 2013, Neuron.

[159]  R. Kleeman A Path Integral Formalism for Non-equilibrium Hamiltonian Statistical Systems , 2013, 1307.1102.

[160]  A D Wissner-Gross,et al.  Causal entropic forces. , 2013, Physical review letters.

[161]  Tim Sanchez,et al.  Topology and dynamics of active nematic vesicles , 2014, Science.

[162]  N. Buric,et al.  Lagrangian form of Schrödinger equation , 2014 .

[163]  D. Ramsay,et al.  Clarifying the roles of homeostasis and allostasis in physiological regulation. , 2014, Psychological review.

[164]  Karl J. Friston,et al.  Cognitive Dynamics: From Attractors to Active Inference , 2014, Proceedings of the IEEE.

[165]  Ao Ping,et al.  Lyapunov function as potential function: A dynamical equivalence * , 2014 .

[166]  Bart Gips,et al.  Temporal coding organized by coupled alpha and gamma oscillations prioritize visual processing , 2014, Trends in Neurosciences.

[167]  Erik Rietveld,et al.  Self-organization, free energy minimization, and optimal grip on a field of affordances , 2014, Front. Hum. Neurosci..

[168]  A. Caticha The basics of information geometry , 2014, 1412.5633.

[169]  A. Seth Inference to the Best Prediction , 2015 .

[170]  Karl J. Friston,et al.  Knowing one's place: a free-energy approach to pattern regulation , 2015, Journal of The Royal Society Interface.

[171]  Louis M Pecora,et al.  Synchronization of chaotic systems. , 2015, Chaos.

[172]  J. S. Wettlaufer,et al.  Maximal Stochastic Transport in the Lorenz Equations , 2015, 1508.03665.

[173]  André Longtin,et al.  Contrast coding in the electrosensory system: parallels with visual computation , 2015, Nature Reviews Neuroscience.

[174]  Georgi Georgiev,et al.  Self-organization in non-equilibrium systems , 2015 .

[175]  Karl J. Friston,et al.  Evidence for surprise minimization over value maximization in choice behavior , 2015, Scientific Reports.

[176]  T. Sagawa,et al.  Thermodynamics of information , 2015, Nature Physics.

[177]  Karl J. Friston,et al.  Active inference and epistemic value , 2015, Cognitive neuroscience.

[178]  Ariel Caticha,et al.  Entropic Dynamics , 2001, Entropy.

[179]  Paul Skrzypczyk,et al.  The role of quantum information in thermodynamics—a topical review , 2015, 1505.07835.

[180]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[181]  Jeremy L. England Dissipative adaptation in driven self-assembly. , 2015, Nature nanotechnology.

[182]  M. Rigol,et al.  From quantum chaos and eigenstate thermalization to statistical mechanics and thermodynamics , 2015, 1509.06411.

[183]  Raymond J. Dolan,et al.  Active Inference, Evidence Accumulation, and the Urn Task , 2015, Neural Computation.

[184]  Nihat Ay,et al.  Information Geometry on Complexity and Stochastic Interaction , 2015, Entropy.

[185]  Karl J. Friston,et al.  A Duet for one , 2015, Consciousness and Cognition.

[186]  J. Hohwy The self-evidencing brain , 2016 .

[187]  Max Tegmark,et al.  Why Does Deep and Cheap Learning Work So Well? , 2016, Journal of Statistical Physics.

[188]  Jonathan Oppenheim,et al.  Fluctuating States: What is the Probability of a Thermodynamical Transition? , 2015, 1504.00020.

[189]  'Alvaro M. Alhambra,et al.  Fluctuating Work: From Quantum Thermodynamical Identities to a Second Law Equality , 2016, 1601.05799.

[190]  Igor V. Ovchinnikov,et al.  Introduction to Supersymmetric Theory of Stochastics , 2015, Entropy.

[191]  Lilian A. E. Weber,et al.  Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression , 2016, Front. Hum. Neurosci..

[192]  Karl J. Friston,et al.  Active Inference: A Process Theory , 2017, Neural Computation.

[193]  Karl J. Friston,et al.  Predicting green: really radical (plant) predictive processing , 2017, Journal of The Royal Society Interface.

[194]  Karl J. Friston,et al.  The graphical brain: Belief propagation and active inference , 2017, Network Neuroscience.

[195]  Jennifer C. Brookes Quantum effects in biology: golden rule in enzymes, olfaction, photosynthesis and magnetodetection , 2017, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[196]  Karl J. Friston,et al.  Deep temporal models and active inference , 2017, Neuroscience & Biobehavioral Reviews.

[197]  Karl J. Friston,et al.  Deep temporal models and active inference , 2017, Neuroscience & Biobehavioral Reviews.

[198]  Andy Clark,et al.  How to Knit Your Own Markov Blanket , 2017 .

[199]  Karl J. Friston,et al.  Sentient Self-Organization: Minimal dynamics and circular causality , 2017, 1705.08265.

[200]  Adeel Razi,et al.  Biological Self-organisation and Markov blankets , 2017, bioRxiv.

[201]  T. Koide Perturbative expansion of irreversible work in Fokker–Planck equation à la quantum mechanics , 2017, 1701.01716.

[202]  Lou Massa,et al.  Notes on The Energy Equivalence of Information. , 2017, The journal of physical chemistry. A.

[203]  Vivien Lecomte,et al.  Rules of calculus in the path integral representation of white noise Langevin equations: the Onsager–Machlup approach , 2017, 1704.03501.

[204]  N. Daw,et al.  Reinforcement Learning and Episodic Memory in Humans and Animals: An Integrative Framework , 2017, Annual review of psychology.

[205]  Karl J. Friston,et al.  Active Inference, Curiosity and Insight , 2017, Neural Computation.

[206]  M. Sahin,et al.  Translational use of event-related potentials to assess circuit integrity in ASD , 2017, Nature Reviews Neurology.

[207]  Frank Cichos,et al.  Active particles bound by information flows , 2018, Nature Communications.

[208]  Eun-Jin Kim,et al.  Investigating Information Geometry in Classical and Quantum Systems through Information Length , 2018, Entropy.

[209]  Karl J. Friston,et al.  Answering Schrödinger's question: A free-energy formulation , 2017, Physics of life reviews.

[210]  Karl J. Friston,et al.  A variational approach to niche construction , 2018, Journal of The Royal Society Interface.

[211]  J. Anders,et al.  Coherent fluctuation relations: from the abstract to the concrete , 2018, Quantum.