The Molecular Basis of Neural Memory. Part 7:  Neural Intelligence (NI) versus Artificial Intelligence (AI)

Gerard Marx; Chaim Gilon; Gerard Marx; Chaim Gilon

doi:10.3934/medsci.2017.3.241

AIMS Medical Science

2017, Volume 4, Issue 3: 241-260. doi: 10.3934/medsci.2017.3.241

Previous Article Next Article

Review

The Molecular Basis of Neural Memory. Part 7: Neural Intelligence (NI) versus Artificial Intelligence (AI)

Gerard Marx ^{1
,
,},
Chaim Gilon ²

1.
MX Biotech Ltd., Jerusalem, Israel
2.
Institute of Chemistry, Hebrew University, Jerusalem, Israel

Received: 30 April 2017 Accepted: 03 July 2017 Published: 15 July 2017

The link of memory to intelligence is incontestable, though the development of electronic artifacts with memory has confounded cognitive and computer scientists’ conception of memory and its relevance to “intelligence”. We propose two categories of “Intelligence”: (1) Logical (objective) — mathematics, numbers, pattern recognition, games, programmable in binary format. (2) Emotive (subjective) — sensations, feelings, perceptions, goals desires, sociability, sex, food, love. The 1^st has been reduced to computational algorithms of which we are well versed, witness global technology and the internet. The 2^nd relates to the mysterious process whereby (psychic) emotive states are achieved by neural beings sensing, comprehending, remembering and dealing with their surroundings.
Many theories and philosophies have been forwarded to rationalize this process, but as neuroscientists, we remain dissatisfied. Our own musings on universal neural memory, suggest a tripartite mechanism involving neurons interacting with their surroundings, notably the neural extracellular matrix (nECM) with dopants [trace metals and neurotransmitters (NTs)]. In particular, the NTs are the molecular encoders of emotive states. We have developed a chemographic representation of such a molecular code.
To quote Longuet-Higgins, “Perhaps it is time for the term ‘artificial intelligence’ to be replaced by something more modest and less provisional”. We suggest “artifact intelligence” (ARTI) or “machine intelligence” (MI), neither of which imply emulation of emotive neural processes, but simply refer to the ‘demotive’ (lacking emotive quality) capability of electronic artifacts that employ a recall function, to calculate algorithms.
- cognitive information; emotions; mentation; memory; metal complex; neurotransmitter
Citation: Gerard Marx, Chaim Gilon. The Molecular Basis of Neural Memory. Part 7: Neural Intelligence (NI) versus Artificial Intelligence (AI)[J]. AIMS Medical Science, 2017, 4(3): 241-260. doi: 10.3934/medsci.2017.3.241

Related Papers:

Abstract

The link of memory to intelligence is incontestable, though the development of electronic artifacts with memory has confounded cognitive and computer scientists’ conception of memory and its relevance to “intelligence”. We propose two categories of “Intelligence”: (1) Logical (objective) — mathematics, numbers, pattern recognition, games, programmable in binary format. (2) Emotive (subjective) — sensations, feelings, perceptions, goals desires, sociability, sex, food, love. The 1^st has been reduced to computational algorithms of which we are well versed, witness global technology and the internet. The 2^nd relates to the mysterious process whereby (psychic) emotive states are achieved by neural beings sensing, comprehending, remembering and dealing with their surroundings.
Many theories and philosophies have been forwarded to rationalize this process, but as neuroscientists, we remain dissatisfied. Our own musings on universal neural memory, suggest a tripartite mechanism involving neurons interacting with their surroundings, notably the neural extracellular matrix (nECM) with dopants [trace metals and neurotransmitters (NTs)]. In particular, the NTs are the molecular encoders of emotive states. We have developed a chemographic representation of such a molecular code.
To quote Longuet-Higgins, “Perhaps it is time for the term ‘artificial intelligence’ to be replaced by something more modest and less provisional”. We suggest “artifact intelligence” (ARTI) or “machine intelligence” (MI), neither of which imply emulation of emotive neural processes, but simply refer to the ‘demotive’ (lacking emotive quality) capability of electronic artifacts that employ a recall function, to calculate algorithms.

References

[1]	Boole G (1853) The Laws of Thought. In: The Mathematical Theories of Logic and Probabilities. Project Gutenberg (EBook #15114).
[2]	Calderone J (2014) 10 Big Ideas in 10 Years of Brain Science. Scientific American MIND, November 6.
[3]	Chalmers DJ (1996) The Conscious Mind: In Search for a Fundamental Theory. New York: Oxford University Press.
[4]	Dehaene S (2014) Consciousness and the Brain. In: Deciphering How the Brain Codes Our Thoughts. New York: Penguin Publishers.
[5]	Edelman G, Tononi G (2000) A universe of consciousness: How matter becomes imagination. Basic books.
[6]	LeDoux JE (2003) Synaptic self: How our brains become who we are. New York: Penguin Publishers.
[7]	LeDoux JE (2012) Evolution of human emotion: A view through fear. Prog Brain Res 195:431-442. doi: 10.1016/B978-0-444-53860-4.00021-0
[8]	Penrose R (1989) The Emperor's New Mind. New York: Oxford University Press.
[9]	Shiffman D, Fry S, Marsh Z (2012) The nature of code. D. Shiffman.
[10]	Bostrom N (2014) Superintelligence: Paths, dangers, strategies. OUP Oxford.
[11]	Zimme (2014) The New Science of the Brain. National Geographic.
[12]	McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. B Math Biol 5: 115-133.
[13]	Turing AM (1950) Computing machinery and intelligence. Mind 59: 433-460.
[14]	Graves A, Wayne G, Danihelka I (2014) Neural turing machines. arXiv preprint arXiv:1410.5401
[15]	Von Neumann J (2012) The computer and the brain. 3rd ed. New Haven: Yale University Press: 66.
[16]	Jeffress LA (1951) Cerebral mechanisms in behavior; the Hixon Symposium. a. von Neumann J. The general and logical theory of automata: 1-41. b. McCullogh WS. Why the mind is in the head: 42-57.
[17]	Arbib MA (1987) Brains, Machines and Mathematics. In: Neural Nets and Finite Automata. 2nd ed. Berlin: Springer US: 15-29.
[18]	Franklin S (1995) Artificial Minds. Cambridge, MA: MIT Press.
[19]	Longuet-Higgins HC (1981) Artificial intelligence-a new theoretical psychology? Cognition 10: 197-201.
[20]	Neisser U (1963) The imitation of man by machine. Science 139: 193-197. doi: 10.1126/science.139.3551.193
[21]	Sloman A (1979) Epistemology and Artificial Intelligence: Expert Systems in the Microelectronic Age. Edinburgh: Edinburgh University Press.
[22]	Gardner H (1985) The Mind's New Science. New York: Basic Books.
[23]	Garland A (2015) Ex Machina – Movie.
[24]	Sejnowski TJ, Koch C, Churchland PS (1988) Computational neuroscience. Science 24: 1299-1330.
[25]	Russel S, Norvig P (2009) Artificial Intelligence: A Modern Approach. 3rd ed. NY: Pearson Publishers.
[26]	Aho AV (2012) Computation and computational thinking. Comput J 55: 832-835.
[27]	Guidolin D, Albertin G, Guescini M, et al. (2011) Central nervous system and computation. Quart Rev Biol 86: 265-85 doi: 10.1086/662456
[28]	Howard N (2012) Brain Language: The fundamental code unit. Brain Sci 1: 6-34.
[29]	Howard N, Guidere M (2012) LXIO: The mood detection Robopsych Brain Sci 1: 71-77.
[30]	Pockett S (2014) Problems with theories of consciousness. Front Syst Neurosci: 225.
[31]	Hirschberg J, Manning CD (2015) Advances in natural language processing. Science 349: 261-266. doi: 10.1126/science.aaa8685
[32]	Parkes DC, Wellman MP (2015) Economic reasoning and artificial intelligence. Science 349: 267-272. doi: 10.1126/science.aaa8403
[33]	Gershman SJ, Horvitz EJ, Tenenbaum JB (2015) Computational rationality: A converging paradigm for intelligence in brains, minds, and machines. Science 349: 273-278. doi: 10.1126/science.aac6076
[34]	Jordan M, Mitchell TM (2015) Machine learning: Trends, perspectives, and prospects. Science 349: 255-260. doi: 10.1126/science.aaa8415
[35]	Wu Y, Schuster M, Chen Z, et al. (2016) Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv 1609.08144.
[36]	y Cajal SR (1995) Histology of the nervous system of man and vertebrates. USA: Oxford University Press.
[37]	Garcia-Lopez P, Garcia-Marin V, FreireM (2010) The histological slides and drawing s of Cajal. Front Neuroanat 4: 9
[38]	Hebb DO (1949). The Organization of Behavior. New York: Wiley.
[39]	Kandel ER, Schwartz JH, Jessell TM, et al. (2013) Principles of Neural Science. New York: MacGraw-Hill.
[40]	Arshavsky YI (2006) "The seven sins" of the Hebbian synapse: can the hypothesis of synaptic plasticity explain long-term memory consolidation?. Prog Neurobiol 80: 99-113. doi: 10.1016/j.pneurobio.2006.09.004
[41]	Gallistel CR, KingA (2009) Memory and the Computational Brain. New York: Wiley Blackwell.
[42]	Hawkins J, Blakeslee S (2005) On Intelligence. New York: St Martin's Press.
[43]	Zador A, Koch C, Brown TH (1990) Biophysical model of a Hebbian synapse. Proc Natl Acad Sci USA 87: 6718-6722. doi: 10.1073/pnas.87.17.6718
[44]	Vizi ES, Fekete A, Karoly R, et al (2010) Non-synaptic receptors and transporters involved in brain functions and targets of drug treatment. Br J Pharmacol 160: 785-809. doi: 10.1111/j.1476-5381.2009.00624.x
[45]	Vizi E (2013) Role of high-affinity receptors and membrane transporters in non-synaptic communication and drug action in the central nervous system. Pharmacol Rev 52: 63-89.
[46]	Schmitt FO, Samson FE, Irwin LN, et al. (1969) Brain cell micro-environment. NRP Bulletin: 7.
[47]	Cserr HF (1986) The Neuronal Environment. Ann NY Acad Sci: 481.
[48]	Juliano RI, Haskill S (1993) Signal transduction from extracellular matrix. J Cell Biol 120: 577-585. doi: 10.1083/jcb.120.3.577
[49]	Vargova L, Sykova E (2014) Astrocytes and extracellular matrix in extrasynaptic volume transmission. Phil Trans R Soc B 369: 20130608. doi: 10.1098/rstb.2013.0608
[50]	Giaume C, Oliet S (2016) Introduction to the special issue: Dynamic and metabolic interactions between astrocytes and neurons. Neuroscience 323: 1-2. doi: 10.1016/j.neuroscience.2016.02.062
[51]	Hrabětová S, Nicholson C (2007) Biophysical properties of brain extracellular space explored with ion-selective microelectrodes, integrative optical imaging and related techniques. In: Michael AC, Borland LM, editors. Electrochemical Methods for Neuroscience. Boca Raton, FL: CRC Press: chapter 10.
[52]	Kuhn TS (1970) The Structure of Scientific Revolutions. 2nd ed. Chicago, IL: University of Chicago Press.
[53]	Landauer R (1996) The physical nature of information. Physics Letters A 217: 188-193. doi: 10.1016/0375-9601(96)00453-7
[54]	Chua LO (2011) Resistance switching memories are memristors. Applied Physics A 102: 765-783.
[55]	Di Ventra M, Pershin YY (2011) Memory materials: A unifying description. Mater Today 14: 584-591. doi: 10.1016/S1369-7021(11)70299-1
[56]	Kamalanathan D, Akhavan A, Kozicki MN (2011) Low voltage cycling of programmable metallization cell memory devices. Nanotechnology 22: 254017.
[57]	Tian F, Jiao D, Biedermann F, et al. (2012) Orthogonal switching of a single supramolecular complex. Nat Commun 3: 1207. doi: 10.1038/ncomms2198
[58]	Sakata Y, Furukawa S, Kondo M, et al. (2013) Shape-memory nanopores induced in coordination frameworks by crystal downsizing. Science 339: 193-196. doi: 10.1126/science.1231451
[59]	Lin WP, Liu S, Gong T, et al. (2014) Polymer-based resistive memory materials and devices. Adv Mater 26: 570-606. doi: 10.1002/adma.201302637
[60]	Zhou X, Xia M, Rao F, et al. (2014) Understanding phase-change behaviors of carbon-doped Ge₂Sb₂Te₅ for phase-change memory application. ACS Appl Mater Interfaces 6: 14207-14214. doi: 10.1021/am503502q
[61]	Agnati LF, Fuxe K (2014) Extracellular-vesicle type of volume transmission and tunnelling-nanotube type of wiring transmission add a new dimension to brain neuro-glial networks. Philos Trans R Soc Lond B Biol Sci 369: pii: 20130505.
[62]	Fuxe K, Borroto-Escuela DO, Romero-Fernandez W, et al. (2013) Volume transmission and its different forms in the central nervous system. Chin J Integr Med 19: 323-329. doi: 10.1007/s11655-013-1455-1
[63]	Fuxe L, Borroto-Escuela DO (2016) Volume transmission and receptor-receptor interactions in heteroreceptor complexes: Understanding the role of new concepts for brain communication. Neural Regen Res 11: 1220-1223. doi: 10.4103/1673-5374.189168
[64]	Fodor JA (1975) The Language of Thought. Boston, MA: Harvard University Press.
[65]	Sloman A, Croucher M (1981) Why robots will have emotions. Sussex University.
[66]	Mayer JD (1986) How mood influences cognition. Advances in Cognitive Science 1: 290-314.
[67]	Salovey P, Mayer JD (1990) Emotional intelligence. Imagination, Cognition and Personality 9: 285-311.
[68]	Goleman D (1995) Emotional Intelligence. New York: Bantam Books.
[69]	Valverdu J, Casacuberta D (2009) Handbook of Research on Synthetic Emotions and Sociable Robotics: New Applications in Affective Computing and Artificial Intelligence. Information Science Reference. New York: Hershey.
[70]	Meyer JJ, Dastani MM (2010) The logical structure of emotions. Dutch Companion project grant nr: IS053013. SIKS Dissertation Series No. 2010-2.
[71]	Hasson C (2011) Modelisation des mecanismes emotionnels pour un robot autonome: perspective developpementale et sociale. PhD Thesis, Universite de Cergy Pontoise, France.
[72]	Meshulam M, Winter E, Ben-Shakhar G, et al. (2011). Rational emotions. Soc Neurosci 1: 1-7.
[73]	Steunebrink BR. The Logical structure of emotions. Utrecht University, 2010.
[74]	Steunebrink BR, Dastani M, Meyer JJC (2012) A formal model of emotion triggers: an approach for BDI agents. Synthese 185: 83-129. doi: 10.1007/s11229-011-0004-8
[75]	Bosse T, Broekens J, Dias J, et al. (2014) Emotion Modeling. Towards Pragmatic Computational Models of Affective Processes. New York: Springer.
[76]	a. Hudlicka E. From habits to standards: Towards systematic design of emotion models and affective architecture: 3-23.
[77]	b. Dastani M, Floor C. Meyer JJ. Programming agents with emotions: 57-75.
[78]	c. Lowe R, Kiryazov K, Utilizing emotions in autonomous robots: An enactive approach: 76-98.
[79]	76. Smailes D, Moseley P, Wilkinson S (2015) A commentary on: Affective coding: the emotional dimension of agency. Front Hum Neurosci 9: 142.
[80]	77. Lewis M (2016) The Undoing Project: A Friendship That Changed Our Minds. New York:W.W. Norton & Co.
[81]	78. Binet A, Simon T (1916) The Intelligence of the Feeble Minded. Baltimore, MD: Williams and Wilkins.
[82]	79. Griffin D (2001) Animal Minds: Beyond Cognition to Consciousness. Chicago, IL: University Chicago Press.
[83]	80. Albuquerque N, Guo K, Wilkinson A, et al. (2016) Dogs recognize dog and human emotions. Biol Lett 12: 201508 doi: 10.1098/rsbl.2015.0883
[84]	81. Andics A, Gábor A, Gácsi M, et al. (2016) Neural mechanisms for lexical processing in dogs. Science 353:1030-1032. doi: 10.1126/science.aaf3777
[85]	82. De Waal F (2016) Are We Smart Enough To Know How Smart Animals Are? New York: WW Norton & Co.
[86]	83. Kekecs Z, Szollosi A, Palfi B, et al. (2016). Commentary: Oxytocin-gaze positive loop and the coevolution of human-dog bond. Front Neurosci 10:155.
[87]	84. Kovács K, Kis A, Kanizsár O, et al. (2016) The effect of oxytocin on biological motion perception in dogs (Canis familiaris). Animal Cogn 19: 513-522. doi: 10.1007/s10071-015-0951-4
[88]	85. Wasserman EA (2016). Thinking abstractly like a duck(ling). Science 353: 222-223.
[89]	86. Wynne CDL (2004) Do Animals Think? New Jersey: Princeton University Press.
[90]	87. Levy S (1993) Artificial Life. New York: Random House.
[91]	88. Marx G, Gilon C (2012) The molecular basis of memory. ACS Chem Neurosci 3: 633-642. doi: 10.1021/cn300097b
[92]	89. Marx G, Gilon C (2013) The molecular basis of memory. MBM Pt 2: The chemistry of the tripartite mechanism. ACS Chem Neurosci 4: 983–993.
[93]	90. Marx G, Gilon C (2014) The molecular basis of memory. MBM Pt 3: Tagging with neurotransmitters (NTs). Front Neurosci 3: 58.
[94]	91. Marx G, Gilon C (2016) The molecular basis of neural memory. MBM Pt 4: The brain is not a computer. "Binary" computation versus "multinary" mentation. Neuroscience and Biomedical Engineering 4: 14-24.
[95]	92. Marx G, Gilon C (2016) The Molecular Basis of Neural Memory Part 5: Chemograhic notations from alchemy to psycho-chemistry. In Press.
[96]	93. Marx G, Gilon C (2016) The molecular basis of neural memory. MBM Pt 6: Chemical coding of logical and emotive modes. Int J Neurology Res 2: 259-268.
[97]	94. Asimov I (1950) I, Robot. New York: Gnome Press.
[98]	95. Waldrop MM (1992) Complexity: The Emerging Science at the Edge of Order and Chaos. New York: Viking Penguin Group.
[99]	96. Freedman DH (1) Brainmakers. New York: Touchstone Press.
[100]	97. Maass W, Joshi P, Sontag ED (2007) Computational aspects of feedback in neural circuits. PLoS Comput Biol 3: e165.
[101]	98. Picard RW, Vyzas E, Healey J (2001) Toward machine emotional intelligence: Analysis of affective physiological state. IEEE transactions on pattern analysis and machine intelligence 23: 1175-1191. doi: 10.1109/34.954607
[102]	99. Hirschberg J, Manning CD (2015) Advances in natural language processing. Science 349: 261-266.
[103]	100. Critique of Pure Reason (1781) Translated by Norman Kemp Smith. London Macmillan 1934.
[104]	101. Sloman A (2008) Kantian philosophy of mathematics and young robots. In: Proceedings 7th International Conference on Mathematical Knowledge Management Birmingham, UK, July 28-30. Available from: http://events.cs.bham.ac.uk/cicm08/mkm08/.
[105]	102. Berto F (2010). There's Something about Gödel: The Complete Guide to the Incompleteness Theorem. New York: John Wiley and Sons.
[106]	103. Ryle G (1949) The Concept of Mind. UK: Penguin Books.

Reader Comments

Your name:*

Email:*
© 2017 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)