Path integrals, particular kinds, and strange things.

This paper describes a path integral formulation of the free energy principle. The ensuing account expresses the paths or trajectories that a particle takes as it evolves over time. The main results are a method or principle of least action that can be used to emulate the behaviour of particles in open exchange with their external milieu. Particles are defined by a particular partition, in which internal states are individuated from external states by active and sensory blanket states. The variational principle at hand allows one to interpret internal dynamics - of certain kinds of particles - as inferring external states that are hidden behind blanket states. We consider different kinds of particles, and to what extent they can be imbued with an elementary form of inference or sentience. Specifically, we consider the distinction between dissipative and conservative particles, inert and active particles and, finally, ordinary and strange particles. Strange particles can be described as inferring their own actions, endowing them with apparent autonomy or agency. In short - of the kinds of particles afforded by a particular partition - strange kinds may be apt for describing sentient behaviour.

[1]  Lancelot Da Costa,et al.  The entropy production of stationary diffusions , 2022, Journal of Physics A: Mathematical and Theoretical.

[2]  C. Buckley,et al.  Spin glass systems as collective active inference , 2022, IWAI.

[3]  Dalton A R Sakthivadivel,et al.  A Worked Example of the Bayesian Mechanics of Classical Objects , 2022, IWAI.

[4]  Lancelot Da Costa,et al.  On Bayesian mechanics: a physics of and by beliefs , 2022, Interface Focus.

[5]  Dalton A R Sakthivadivel,et al.  Towards a Geometry and Analysis for Bayesian Mechanics , 2022, 2204.11900.

[6]  Lancelot Da Costa,et al.  Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents , 2022, ArXiv.

[7]  Lancelot Da Costa,et al.  The free energy principle made simpler but not too simple , 2022, Physics Reports.

[8]  K. Friston,et al.  A free energy principle for generic quantum systems. , 2021, Progress in biophysics and molecular biology.

[9]  Jun Tani,et al.  Active Inference in Robotics and Artificial Agents: Survey and Challenges , 2021, ArXiv.

[10]  Kai Ueltzhöffer,et al.  Stochastic Chaos and Markov Blankets , 2021, Entropy.

[11]  Dirk Abel,et al.  Review on model predictive control: an engineering perspective , 2021, The International Journal of Advanced Manufacturing Technology.

[12]  Samuel J. Gershman,et al.  Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning , 2021, ArXiv.

[13]  Grigorios A. Pavliotis,et al.  Bayesian mechanics for stationary processes , 2021, Proceedings of the Royal Society A.

[14]  Mark Girolami,et al.  A Unifying and Canonical Description of Measure-Preserving Diffusions , 2021, 2105.02845.

[15]  C. Fields,et al.  Minimal physicalism as a scale-free substrate for cognition and consciousness , 2021, Neuroscience of consciousness.

[16]  J. A. Scott Kelso,et al.  Unifying Large- and Small-Scale Theories of Coordination , 2021, Entropy.

[17]  G. Pezzulo,et al.  Simulating homeostatic, allostatic and goal-directed forms of interoceptive control using active inference , 2021, Biological Psychology.

[18]  Karl J. Friston,et al.  Action and Perception as Divergence Minimization , 2020, ArXiv.

[19]  Adeel Razi,et al.  Parcels and particles: Markov blankets in the brain , 2020, Network Neuroscience.

[20]  Karl J. Friston,et al.  Morphogenesis as Bayesian inference: A variational approach to pattern formation and control in complex biological systems. , 2020, Physics of life reviews.

[21]  Karl J. Friston,et al.  Markov blankets, information geometry and stochastic thermodynamics , 2019, Philosophical Transactions of the Royal Society A.

[22]  M. Levin The Computational Boundary of a “Self”: Developmental Bioelectricity Drives Multicellularity and Scale-Free Cognition , 2019, Front. Psychol..

[23]  Adeel Razi,et al.  On Markov blankets and hierarchical self-organisation , 2019, Journal of theoretical biology.

[24]  Andrew W. Corcoran,et al.  From allostatic agents to counterfactual cognisers: active inference, biological regulation, and the origins of cognition , 2019, Biology & Philosophy.

[25]  Karl J. Friston,et al.  Bayesian Filtering with Multiple Internal Models: Toward a Theory of Social Intelligence , 2019, Neural Computation.

[26]  Karl J. Friston,et al.  Generalised free energy and active inference , 2018, Biological Cybernetics.

[27]  Jason J. Bramburger,et al.  Poincaré maps for multiscale physics discovery and nonlinear Floquet theory , 2019, Physica D: Nonlinear Phenomena.

[28]  Kate Jeffery,et al.  On the Statistical Mechanics of Life: Schrödinger Revisited , 2019, Entropy.

[29]  Karl J. Friston,et al.  A tale of two densities: active inference is enactive inference , 2019, Adapt. Behav..

[30]  Karl J. Friston A free energy principle for a particular physics , 2019, 1906.10184.

[31]  Manuel Baltieri,et al.  PID Control as a Process of Active Inference with Linear Generative Models † , 2019, Entropy.

[32]  Ignazio Licata,et al.  Event-Based Quantum Mechanics: A Context for the Emergence of Classical Information , 2019, Symmetry.

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

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

[35]  Karl J. Friston,et al.  ‘Seeing the Dark’: Grounding Phenomenal Transparency and Opacity in Precision Estimation for Active Inference , 2018, Front. Psychol..

[36]  Karl J. Friston,et al.  Computational Neuropsychology and Bayesian Inference , 2018, Front. Hum. Neurosci..

[37]  Wanja Wiese,et al.  Action Is Enabled by Systematic Misrepresentations , 2017 .

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

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

[40]  Jakub Limanowski (Dis-)Attending to the Body , 2017 .

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

[42]  Karl J. Friston,et al.  Active interoceptive inference and the emotional brain , 2016, Philosophical Transactions of the Royal Society B: Biological Sciences.

[43]  Karl J. Friston,et al.  Scene Construction, Visual Foraging, and Active Inference , 2016, Front. Comput. Neurosci..

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

[45]  Karl J. Friston,et al.  Towards a Neuronal Gauge Theory , 2016, PLoS biology.

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

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

[48]  Tianqi Chen,et al.  A Complete Recipe for Stochastic Gradient MCMC , 2015, NIPS.

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

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

[51]  G. Pavliotis Stochastic Processes and Applications: Diffusion Processes, the Fokker-Planck and Langevin Equations , 2014 .

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

[53]  P. Dechent,et al.  Neural correlates of ideomotor effect anticipations , 2014, Neuroscience.

[54]  A. Seth Interoceptive inference, emotion, and the embodied self , 2013, Trends in Cognitive Sciences.

[55]  A. Barto,et al.  Novelty or Surprise? , 2013, Front. Psychol..

[56]  Felix Blankenburg,et al.  Minimal self-models and the free energy principle , 2013, Front. Hum. Neurosci..

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

[58]  A. Clark Whatever next? Predictive brains, situated agents, and the future of cognitive science. , 2013, The Behavioral and brain sciences.

[59]  Andy Clark,et al.  The many faces of precision (Replies to commentaries on “Whatever next? Neural prediction, situated agents, and the future of cognitive science”) , 2013, Front. Psychol..

[60]  Karl J. Friston,et al.  A Free Energy Principle for Biological Systems. , 2012, Entropy.

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

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

[63]  Yi-An Ma,et al.  Potential Function in a Continuous Dissipative Chaotic System: Decomposition Scheme and Role of Strange Attractor , 2012, Int. J. Bifurc. Chaos.

[64]  F. Zhang,et al.  The potential and flux landscape theory of evolution. , 2012, The Journal of chemical physics.

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

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

[67]  U. Seifert Stochastic thermodynamics, fluctuation theorems and molecular machines , 2012, Reports on progress in physics. Physical Society.

[68]  L. P. Karakatsanis,et al.  Tsallis non-extensive statistics, intermittent turbulence, SOC and chaos in the solar plasma, Part one: Sunspot dynamics , 2012, 1201.6498.

[69]  Tianqi Chen,et al.  Relation of a New Interpretation of Stochastic Differential Equations to Ito Process , 2011, 1111.2987.

[70]  Karl J. Friston,et al.  Bayesian state estimation using generalized coordinates , 2011, Defense + Commercial Sensing.

[71]  Warren Mansell,et al.  Control of Perception Should be Operationalized as a Fundamental Property of the Nervous System , 2011, Top. Cogn. Sci..

[72]  Lejla Batina,et al.  Mutual Information Analysis: a Comprehensive Study , 2011, Journal of Cryptology.

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

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

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

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

[77]  Karl J. Friston,et al.  Action and behavior: a free-energy formulation , 2010, Biological Cybernetics.

[78]  Karl J. Friston The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.

[79]  Dileep George,et al.  Towards a Mathematical Theory of Cortical Micro-circuits , 2009, PLoS Comput. Biol..

[80]  Emanuel Todorov,et al.  General duality between optimal control and estimation , 2008, 2008 47th IEEE Conference on Decision and Control.

[81]  Karl J. Friston Variational filtering , 2008, NeuroImage.

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

[83]  Steven J Schiff,et al.  Kalman filter control of a model of spatiotemporal cortical dynamics , 2008, BMC Neuroscience.

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

[85]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[86]  Angela J. Yu,et al.  Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

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

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

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

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

[91]  Chrystopher L. Nehaniv,et al.  Empowerment: a universal agent-centric measure of control , 2005, 2005 IEEE Congress on Evolutionary Computation.

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

[93]  Charles M. Bishop,et al.  Variational Message Passing , 2005, J. Mach. Learn. Res..

[94]  P. Ao,et al.  Laws in Darwinian Evolutionary Theory , 2005, ArXiv.

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

[96]  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.

[97]  Shun-ichi Amari,et al.  Stochastic Reasoning, Free Energy, and Information Geometry , 2004, Neural Computation.

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

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

[100]  P. Yodzis,et al.  THE COLOR OF ENVIRONMENTAL NOISE , 2004 .

[101]  M. Qian,et al.  Mathematical Theory of Nonequilibrium Steady States: On the Frontier of Probability and Dynamical Systems , 2004 .

[102]  P Ao,et al.  LETTER TO THE EDITOR: Potential in stochastic differential equations: novel construction , 2004 .

[103]  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.

[104]  M. Tribus,et al.  Probability theory: the logic of science , 2003 .

[105]  Adrian L. Williams,et al.  Task-Related Changes in Cortical Synchronization Are Spatially Coincident with the Hemodynamic Response , 2002, NeuroImage.

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

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

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

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

[110]  C. Pillet,et al.  Ergodic properties of classical dissipative systems I , 1998 .

[111]  M. Mackey,et al.  Chaos, Fractals, and Noise: Stochastic Aspects of Dynamics , 1998 .

[112]  Gregory L. Eyink,et al.  Hydrodynamics and fluctuations outside of local equilibrium: Driven diffusive systems , 1996 .

[113]  David J. C. MacKay,et al.  Information-Based Objective Functions for Active Data Selection , 1992, Neural Computation.

[114]  Ivan Kadar,et al.  Signal Processing, Sensor Fusion, and Target Recognition , 1992 .

[115]  Jürgen Schmidhuber,et al.  Curious model-building control systems , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

[116]  S. Shipp,et al.  The functional logic of cortical connections , 1988, Nature.

[117]  J. Berger Statistical Decision Theory and Bayesian Analysis , 1988 .

[118]  D. Bohm A new theory of the relationship of mind and matter. , 1986 .

[119]  A G Barto,et al.  Toward a modern theory of adaptive networks: expectation and prediction. , 1981, Psychological review.

[120]  Robert Graham,et al.  Covariant formulation of non-equilibrium statistical thermodynamics , 1977 .

[121]  Robert Graham,et al.  Path integral formulation of general diffusion processes , 1977 .

[122]  I. Prigogine,et al.  Formative Processes. (Book Reviews: Self-Organization in Nonequilibrium Systems. From Dissipative Structures to Order through Fluctuations) , 1977 .

[123]  Bruce A. Francis,et al.  The internal model principle of control theory , 1976, Autom..

[124]  Kenton F. Machina,et al.  Truth, belief, and vagueness , 1976, J. Philos. Log..

[125]  Edward L. Deci,et al.  Intrinsic Motivation and Self-Determination in Human Behavior , 1975, Perspectives in Social Psychology.

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

[127]  J. Gyr Is a theory of direct visual perception adequate? , 1972, Psychological bulletin.

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

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

[130]  D. Lindley On a Measure of the Information Provided by an Experiment , 1956 .

[131]  A. Wald An Essentially Complete Class of Admissible Decision Functions , 1947 .

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

[133]  E. Rowland Theory of Games and Economic Behavior , 1946, Nature.

[134]  Illtyd Trethowan Causality , 1938 .

[135]  E. M.,et al.  Statistical Mechanics , 2021, Manual for Theoretical Chemistry.

[136]  W. Cannon ORGANIZATION FOR PHYSIOLOGICAL HOMEOSTASIS , 1929 .

[137]  V. Fock,et al.  Beweis des Adiabatensatzes , 1928 .

[138]  D. A. R. Sakthivadivel A CONSTRAINT GEOMETRY FOR INFERENCE AND INTEGRATION , 2022 .

[139]  N. Kantas,et al.  Optimizing interacting Langevin dynamics using spectral gaps , 2021 .

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

[141]  James Babcock,et al.  Artificial General Intelligence , 2016, Lecture Notes in Computer Science.

[142]  Marina Bosch,et al.  Applications Of Centre Manifold Theory , 2016 .

[143]  Karl J. Friston,et al.  Generalised Filtering , 2010 .

[144]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[145]  Marcus Hutter Simulation Algorithms for Computational Systems Biology , 2017, Texts in Theoretical Computer Science. An EATCS Series.

[146]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

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

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

[149]  H. Loeliger,et al.  Least Squares and Kalman Filtering on Forney Graphs , 2002 .

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

[151]  P. Sterling,et al.  Allostasis: A new paradigm to explain arousal pathology. , 1988 .

[152]  L. Optican,et al.  Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. III. Information theoretic analysis. , 1987, Journal of neurophysiology.

[153]  S. Mitter,et al.  Toward a theory of nonlinear stochastic realization , 1982 .

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

[155]  A. Tversky,et al.  Prospect theory: analysis of decision under risk , 1979 .

[156]  W. Heisenberg Physics and Beyond: Encounters and Conversations , 1971 .

[157]  Viktor Mikhaĭlovich Glushkov,et al.  An Introduction to Cybernetics , 1957, The Mathematical Gazette.

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

[159]  J. Haldane The inequality of man and other essays , 1932 .

[160]  R. Cooper Sensory Communication , 2022 .