{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:25:33Z","timestamp":1772252733226,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,3,9]],"date-time":"2017-03-09T00:00:00Z","timestamp":1489017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>This paper proposes that intelligent processes can be completely explained by thermodynamic principles. They can equally be described by information-theoretic principles that, from the standpoint of the required optimizations, are functionally equivalent. The underlying theory arises from two axioms regarding distinguishability and causality. Their consequence is a theory of computation that applies to the only two kinds of physical processes possible\u2014those that reconstruct the past and those that control the future. Dissipative physical processes fall into the first class, whereas intelligent ones comprise the second. The first kind of process is exothermic and the latter is endothermic. Similarly, the first process dumps entropy and energy to its environment, whereas the second reduces entropy while requiring energy to operate. It is shown that high intelligence efficiency and high energy efficiency are synonymous. The theory suggests the usefulness of developing a new computing paradigm called Thermodynamic Computing to engineer intelligent processes. The described engineering formalism for the design of thermodynamic computers is a hybrid combination of information theory and thermodynamics. Elements of the engineering formalism are introduced in the reverse-engineer of a cortical neuron. The cortical neuron provides perhaps the simplest and most insightful example of a thermodynamic computer possible. It can be seen as a basic building block for constructing more intelligent thermodynamic circuits.<\/jats:p>","DOI":"10.3390\/e19030107","type":"journal-article","created":{"date-parts":[[2017,3,9]],"date-time":"2017-03-09T11:12:17Z","timestamp":1489057937000},"page":"107","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Physical Intelligence and Thermodynamic Computing"],"prefix":"10.3390","volume":"19","author":[{"given":"Robert","family":"Fry","sequence":"first","affiliation":[{"name":"Applied Physics Laboratory, Johns Hopkins University, Laurel, MD 20723, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,9]]},"reference":[{"key":"ref_1","unstructured":"Fry, R.L. (2015). Computation by Biological Systems. Biomed. Sci. Today, Available online: https:\/\/pdfs.semanticscholar.org\/c645\/9f5948618a3773278514e67c05e02a35e9cf.pdf."},{"key":"ref_2","unstructured":"Shannon, C.E. (1949). The Mathematical Theory of Communication, University of Illinois Press."},{"key":"ref_3","unstructured":"Online Encyclopedia of Science. Available online: http:\/\/www.daviddarling.info\/encyclopedia\/P\/protozoan.html."},{"key":"ref_4","unstructured":"Lewis, C.I. (1956). Mind and The World Order: An Outline of a Theory of Knowledge, Reprinted in Paperback by Dover Publications, Inc."},{"key":"ref_5","unstructured":"Cox, R.T. (,  1979). Of inference and inquiry, an essay in inductive logic. Proceedings of the Maximum Entropy Formalism\u2014First Maximized Entropy Workshop, Boston, MA, USA."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"350","DOI":"10.5840\/monist192939314","article-title":"What is a question?","volume":"39","author":"Cohen","year":"1929","journal-title":"Monist"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lewis, C. (1918). A Survey of Symbolic Logic, University of California Press.","DOI":"10.1525\/9780520398252"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cox, R.T. (1961). Algebra of Probable Inference, Johns Hopkins University Press.","DOI":"10.56021\/9780801869822"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/14786448008626877","article-title":"On the Diagrammatic and Mechanical Representation of Propositions and Reasonings","volume":"10","author":"Venn","year":"1880","journal-title":"Philos. Mag. J. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1109\/18.79902","article-title":"A new outlook on Shannon\u2019s information measures","volume":"37","author":"Yeung","year":"1991","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_11","unstructured":"Spencer-Brown, G. (1972). Laws of Form, Crown Publishing Group."},{"key":"ref_12","unstructured":"The Shell Game. Available online: https:\/\/en.wikipedia.org\/wiki\/Shell_game."},{"key":"ref_13","unstructured":"Shannon, C.E. (1959). Source Coding with a Fidelity Criterion. Proc. IRE."},{"key":"ref_14","unstructured":"Cover, T.M. (1998). Untitled Contribution. IEEE Inf. Theory Soc. Newslett., 18\u201319. Available online: http:\/\/www-isl.stanford.edu\/~cover\/papers\/paper112.pdf."},{"key":"ref_15","first-page":"1","article-title":"A Method of Geometrical Representation of the Thermodynamic Properties of Substances by Means of Surfaces","volume":"Volume 1","author":"Gibbs","year":"1906","journal-title":"The Scientific Papers of J. Willard Gibbs"},{"key":"ref_16","unstructured":"Tribus, M. (1961). Thermostatics and Thermodynamics: An Introduction Energy, Information and States of Matter with Engineering Application, D. Van Nostrand, Inc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1147\/rd.53.0183","article-title":"Irreversibility and heat generation in the computing process","volume":"5","author":"Landauer","year":"1961","journal-title":"IBM J. Res. Dev."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1007\/BF02084158","article-title":"The thermodynamics of computation\u2014A review","volume":"21","author":"Bennett","year":"1982","journal-title":"Int. J. Theor. Phys."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1109\/TIT.2003.810631","article-title":"To Code, or Not to Code: Lossy Source-Channel Coding Communication Revisited","volume":"49","author":"Gastpar","year":"2003","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/0100000005","article-title":"Geometric Programming for Communication Systems","volume":"2","author":"Chiang","year":"2005","journal-title":"Found. Trends Commu. Inf. Theory"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ben-Tal, A., and Nemirovski, A. (2001). Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications, SIAM.","DOI":"10.1137\/1.9780898718829"},{"key":"ref_22","unstructured":"Bertsekas, D.P. (1999). Nonlinear Programming, Athena Scientific. [2nd ed.]."},{"key":"ref_23","unstructured":"Bertsekas, D.P., Nedic, E., and Ozdaglar, A. (2003). Convex Analysis and Optimization, Athena Scientific."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Boyd, S., and Vandenberghe, L. (2004). Convex Optimization, Cambridge University Press.","DOI":"10.1017\/CBO9780511804441"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nesterov, Y., and Nemirovsky, A. (1994). Interior Point Polynomial Algorithms in Convex Programming, SIAM.","DOI":"10.1137\/1.9781611970791"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nocedal, J., and Wright, S.J. (1999). Numerical Optimization, Springer.","DOI":"10.1007\/b98874"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1137\/1035044","article-title":"Lagrange Multipliers and Optimality","volume":"35","author":"Rockafellar","year":"1993","journal-title":"SIAM Rev."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Rockafellar, R.T. (1970). Convex Analysis, Princeton University Press.","DOI":"10.1515\/9781400873173"},{"key":"ref_29","unstructured":"Csiszar, I., and Korner, J. (1981). Information Theory: Coding Theorems for Discrete Memoryless Systems, Academic Press."},{"key":"ref_30","unstructured":"Phelps, R.R. (1991). Convex Functions, Monotone Operators and Differentiability, Springer. [2nd ed.]."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Avriel, M. (1980). Advances in Geometric Programming, Plenum Press.","DOI":"10.1007\/978-1-4615-8285-4"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wissner-Gross, A.D., and Freer, C.E. (2013). Causal Entropic Forces. Phys. Rev. Lett., 110, Available online: http:\/\/math.mit.edu\/~freer\/papers\/PhysRevLett_110-168702.pdf.","DOI":"10.1103\/PhysRevLett.110.168702"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.neucom.2004.11.001","article-title":"Neural Statics and Dynamics","volume":"65","author":"Fry","year":"2005","journal-title":"Neurocomputing"},{"key":"ref_34","unstructured":"Berger, T. (1971). Rate Distortion Theory: A Mathematical Basis for Data Compression, Prentice Hall."},{"key":"ref_35","unstructured":"The Nerve Cell. Available online: https:\/\/www.britannica.com\/science\/nervous-system\/The-nerve-cell#ref606326."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1109\/72.392254","article-title":"Observer-Participant Models of Neural Processing","volume":"6","author":"Fry","year":"1995","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_37","unstructured":"Hebb, D.O. (1949). The Organization of Behavior, Wiley and Sons."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4746","DOI":"10.1523\/JNEUROSCI.22-11-04746.2002","article-title":"Energy-Efficient Neuronal Computation via Quantal Synaptic Failures","volume":"22","author":"Levy","year":"2002","journal-title":"J. Neurosci."},{"key":"ref_39","unstructured":"Hinton, G.E., and Sejnowsku, T.J. (, January June). Optimal Perceptual Inference. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA. Available online: https:\/\/papers.cnl.salk.edu\/PDFs\/Optimal%20Perceptual%20Inference%201983-646.pdf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/BF00275687","article-title":"A simplified neuron model as a principal component analyzer","volume":"15","author":"Oja","year":"1985","journal-title":"J. Math. Biol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Fry, R.L. (2008, January 23\u201324). Computation by neural and cortical systems. Proceedings of the CNS Workshop on Methods of Information Theory and Neural Processing, BMC Neuroscience, Portland, OR, USA.","DOI":"10.1186\/1471-2202-9-S1-P66"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1016\/j.neucom.2005.12.051","article-title":"Double Matching: The Problem that Neurons Solve","volume":"69","author":"Fry","year":"2005","journal-title":"Neurocomputing"},{"key":"ref_43","unstructured":"Hill, T.L. (2013). Thermodynamics of Small Systems, Parts I & II, Dover."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/19\/3\/107\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:30:08Z","timestamp":1760207408000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/19\/3\/107"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3,9]]},"references-count":43,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2017,3]]}},"alternative-id":["e19030107"],"URL":"https:\/\/doi.org\/10.3390\/e19030107","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints201701.0097.v1","asserted-by":"object"}]},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,3,9]]}}}