Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Artificial General Intelligence

The Journal of the Artificial General Intelligence Society

3 Issues per year

Open Access
See all formats and pricing
More options …

Black-box Brain Experiments, Causal Mathematical Logic, and the Thermodynamics of Intelligence

Sergio Pissanetzky / Felix Lanzalaco
Published Online: 2014-04-25 | DOI: https://doi.org/10.2478/jagi-2013-0005


Awareness of the possible existence of a yet-unknown principle of Physics that explains cognition and intelligence does exist in several projects of emulation, simulation, and replication of the human brain currently under way. Brain simulation projects define their success partly in terms of the emergence of non-explicitly programmed biophysical signals such as self-oscillation and spreading cortical waves. We propose that a recently discovered theory of Physics known as Causal Mathematical Logic (CML) that links intelligence with causality and entropy and explains intelligent behavior from first principles, is the missing link. We further propose the theory as a roadway to understanding more complex biophysical signals, and to explain the set of intelligence principles. The new theory applies to information considered as an entity by itself. The theory proposes that any device that processes information and exhibits intelligence must satisfy certain theoretical conditions irrespective of the substrate where it is being processed. The substrate can be the human brain, a part of it, a worm’s brain, a motor protein that self-locomotes in response to its environment, a computer. Here, we propose to extend the causal theory to systems in Neuroscience, because of its ability to model complex systems without heuristic approximations, and to predict emerging signals of intelligence directly from the models. The theory predicts the existence of a large number of observables (or “signals”), all of which emerge and can be directly and mathematically calculated from non-explicitly programmed detailed causal models. This approach is aiming for a universal and predictive language for Neuroscience and AGI based on causality and entropy, detailed enough to describe the finest structures and signals of the brain, yet general enough to accommodate the versatility and wholeness of intelligence. Experiments are focused on a black-box as one of the devices described above of which both the input and the output are precisely known, but not the internal implementation. The same input is separately supplied to a causal virtual machine, and the calculated output is compared with the measured output. The virtual machine, described in a previous paper, is a computer implementation of CML, fixed for all experiments and unrelated to the device in the black box. If the two outputs are equivalent, then the experiment has quantitatively succeeded and conclusions can be drawn regarding details of the internal implementation of the device. Several small black-box experiments were successfully performed and demonstrated the emergence of non-explicitly programmed cognitive function in each case

Keywords: AGI; brain emulation; causal mathematical logic; self-programming; causal entropic forces


  • Berut, A.; Arakelyan, A.; and et al., A. P. 2012. Experimental Verification of Landauer’s Principle linking Information and Thermodynamics. Nature 483:187-189. Abstract: http://www.nature.com/nature/journal/v483/n7388/full/nature10872.html.Google Scholar

  • Chen, W.; Li, X.; Pu, J.; and Luo, Q. 2010. Spatial-temporal dynamics of chaotic behavior in cultured hippocampal networks. Physical Review E 81(061903).Google Scholar

  • Cuntz, H.; Mathy, A.; and H¨ausser, M. June 2012. A scaling law derived from optimal dendritic wiring. PNAS, 2012, DOI: 10.1073/pnas.1200430109 1-5. Available electronically from http://www.pnas.org/content/109/27/11014.CrossrefGoogle Scholar

  • Deca, D. 2011. Available tools for whole brain emulation. Int. J. of Machine Consciousness 04:67.Google Scholar

  • Demeyer, S.; Rysselberghe, F. V.; and et. al. 2005. The LAN-simulation: a refactoring teaching example. 8th Int. Workshop on Principles of Software Evolution, Lisbon 1:123-134. Code and teaching materials available from www.lore.ua.ac.be/Research/Artefacts/RefactoringLabSession.Google Scholar

  • Eagleman, D. 2011. Incognito. New York: Pantheon Books.Google Scholar

  • Eigen, M. 2013. From Strange Simplicity to Complex Familiarity. New York: Oxford University Press.Google Scholar

  • Fuster, J. M. 2005. Cortex and Mind. New York: Oxford University Press.Google Scholar

  • Gardner, A., and Conlon, J. P. May 2013. Cosmological natural selection and the purpose of the universe. Complexity doi: 10.1002/cplx.21446.CrossrefGoogle Scholar

  • Gell-Mann, M. 1994. The Quark and the Jaguar. New York: W. H. Freeman and Company.Google Scholar

  • Gjuvsland, A.; Vik, J.; Beard, D.; and et al. 2013. Bridging the genotype-phenotype gap: what does it take? J. Physiology 591(8):2055-2066.Google Scholar

  • Hales, C. G.; Grayden, D. B.; and Quiney, H. 2011. The electric field system of a macular ion channel plaque. In 33rd Annual Conf. of the IEEE EMBS, Boston, MA.Google Scholar

  • Hales, C. G. 2011. On the status of computationalism as a law of nature. Int. J. of Machine Consciousness 3(1):55-89.Google Scholar

  • Hawkins, J. 2004. On Intelligence. New york: Times Books.Google Scholar

  • Karr, J.; Sanghvi, J.; Macklin, D.; and et al. 2012. A whole cell computational model predicts phenotype from genotype. Cell 150:389-401.Google Scholar

  • Kauffman, S. 2011. Answering Descartes: Beyond Turing. In European Conference on Artificial Life (ECAL), 11-22. Available from: http://mitpress.mit.edu/sites/default/files/titles/alife/0262297140chap4.pdf.Google Scholar

  • Koene, R.; Hutter, M.; and Hales, C. October 2012. Intelligence, substrates, and computation. Possibilities for the future. Available electronically at https://www.youtube.com/watch?v=KgcBvFpCD2k.Google Scholar

  • Landauer, R. 1996. The physical nature of information. Physics Letters A 217:188-193. Available electronically from http://www.sciencedirect.com/science/article/pii/0375960196004537.Google Scholar

  • Landauer, R. 1999. Information is a physical entity. Physica A: Statistical Mechanics and its applications 263:63-67. Available electronically from http://www.sciencedirect.com/science/article/pii/S0378437198005135.Google Scholar

  • Lanzalaco, F., and Pissanetzky, S. 2013. Causal Mathematical Language as a guiding framework for the prediction of ”Intelligence signals” in brain simulations. J. of Artifical General Intelligence December 2013.Google Scholar

  • Lin, L.; Osan, R.; and Tsien, J. Z. 2006. Organizing principles of real-time memory encoding: neural clique assemblies and universal neural codes. Trends in Neuroscience 29(1):48-57. Available electronically from http://www.ncbi.nlm.nih.gov/pubmed/16325278.Web of ScienceGoogle Scholar

  • Noble, D. 2012. A theory of biological relativity: no privileged level of causation. Interface Focus 2(1):55-64.Web of SciencePubMedGoogle Scholar

  • Opdyke, W. F. 1992. Refactoring Object-Oriented Frameworks. Ph.D. Dissertation, Dep. of Computer Science, Univ. of Illinois, Urbana-Champaign, Illinois, USA. Available electronically from http://www-public.itsudparis. eu/ gibson/Teaching/CSC7302/ReadingMaterial/Opdyke92.pdf.Google Scholar

  • Papo, D. 2013. Why should cognitive neuroscientists study the brain’s resting state? Opinion article doi: 10.3389/fnhum.2013.00045.CrossrefGoogle Scholar

  • Pissanetzky, S. 2008. A new type of Structured Artificial Neural Networks based on the Matrix Model of Computation. In Proc. Int. Conf. in Artificial Intelligence (ICAI 2008, 251-257. Abstract: http://www.scicontrols.com/Publications/AbstractSP2008C.pdf.Google Scholar

  • Pissanetzky, S. 2009. A new Universal Model of Computation and its Contribution to Learning, Intelligence, Parallelism, Ontologies, Refactoring, and the Sharing of Resources. Int. J. of Information and Mathematical Sciences 5:143-173. Available electronically from https://www.waset.org/journals/ijims/v5/v5-2-17.pdf.Google Scholar

  • Pissanetzky, S. 2010. Coupled Dynamics in Host-Guest Complex Systems Duplicates Emergent Behavior in the Brain. World Academy of Science, Engineering, and Technology 68:1-9. Available electronically from https://www.waset.org/journals/waset/v44/v44-1.pdf.Google Scholar

  • Pissanetzky, S. 2011a. Emergence and Self-organization in Partially Ordered Sets. Complexity 17(2):19-38.Google Scholar

  • Pissanetzky, S. 2011b. Emergent inference and the future of NASA. Workshop, NASA, NASA Gilruth Center, Johnson Space Center, Clear Lake, TX. Available electronically at http://www.scicontrols.com/Publications/AbstractNASA2011.pdf.Google Scholar

  • Pissanetzky, S. 2012a. A case study: the European Example. Available electronically at http://www.scicontrols.com/Articles/EuropeanExample.htm.Google Scholar

  • Pissanetzky, S. 2012b. Causality, symmetry, brain, evolution, DNA, and a new theory of Physics. Bulletin of the American Physical Society 57(10):E1.0003. Available electronically from http://meetings.aps.org/link/BAPS.2012.TSF.E1.3.Google Scholar

  • Pissanetzky, S. 2012c. The Detailed Dynamics of Dynamical Systems. Available electronically at http://www.scicontrols.com/Articles/TheoryOfDetailedDynamics.htm.Google Scholar

  • Pissanetzky, S. 2012d. Differential Euler Equations obtained from Causal Mathematical Logic. Available electronically at: http://www.scicontrols.com/Publications/Euler.pdf.Google Scholar

  • Pissanetzky, S. 2012e. Project Plan for a core implementation of Causal Mathematical Logic. Available electronically at: http://www.scicontrols.com/ProjectPlan/CoreImplementation.htm.Google Scholar

  • Pissanetzky, S. 2012f. Reasoning with Computer Code: a new Mathematical Logic. J. of Artificial General Intelligence, special issue on Self-programming 3:11-42. Available electronically from http://www.degruyter.com/view/j/jagi.2012.3.issue-3/issue-files/jagi.2012.3.issue-3.xml.Google Scholar

  • Pissanetzky, S. 2012g. Separating points. Available electronically at http://www.scicontrols.com/Articles/PointSeparation.htm.Google Scholar

  • Pissanetzky, S. 2012h. The theory of Detailed Dynamics and the Combinatorial Explosion. Available electronically at http://www.scicontrols.com/Articles/TheTheoryAndTheCombinatorialExplosion.htm.Google Scholar

  • Pissanetzky, S. 2013. The unification of symmetry and conservation. Bulletin of the American Physical Society 58(3):N2.0001. Available electronically from http://meetings.aps.org/link/BAPS.2013.TSS.N2.1. Google Scholar

  • Wallace, D. 2013. Thermodynamics as control theory. Available electronically at http://philsci-archive.pitt.edu/9904/.Google Scholar

  • Wissner-Gross, A. D., and Freer, C. E. April 2013. Causal Entropic Forces. Physical Review Letters 168702:1-5. Available from: http://www.alexwg.org/publications/PhysRevLett110-168702.pdf. Google Scholar

About the article

Received: 2013-08-01

Accepted: 2013-11-17

Published Online: 2014-04-25

Published in Print: 2013-12-01

Citation Information: Journal of Artificial General Intelligence, ISSN (Online) 1946-0163, DOI: https://doi.org/10.2478/jagi-2013-0005.

Export Citation

© by SergioPissanetzky . This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. BY-NC-ND 3.0

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Sergio Pissanetzky
Applied Mathematics, 2014, Volume 05, Number 21, Page 3489

Comments (0)

Please log in or register to comment.
Log in