Learning and memory phenomena in a complex sensory environment: a neuroheuristic approach

Neuroheuristic Research Group, University of LausanneQuartier Dorigny, CH-1015 Lausanne, Switzerland, Email: avilla@neuroheuristic.orgAbstract —The concept of interdependent communica-tions systems and Wiener's assertion that a machine thatchanges its responses based on feedback is a machine thatlearns, denes the brain as a cybernetic machine. Systemstheory has traditionally focused on the structure of systemsand their models, whereas cybernetics has focused on howsystems function, how they control their actions, how theycommunicate with other systems or with their own compo-nents. However, structure and function of a system cannotbe understood in separation and cybernetics and systemstheoryshouldbeviewedastwofacetsofasingleapproach,dened as the neuroheuristic approach.1. IntroductionNorbert Wiener, a mathematician, engineer and socialphilosopher, coinedtheword”cybernetics”fromtheGreekword meaning ”steersman”. He dened it as the science ofcontrol and communication in the animal and the machine[1]. Many other denitions have followed since then, butingeneralcyberneticstakesasitsdomainthedesignordis-coveryandapplicationofprinciplesofregulationandcom-munication. Early work sought to dene and apply prin-ciples by which systems may be controlled. More recentwork has attempted to understand how systems describethemselves, control themselves, and organize themselves.The cerebral cortex is not a single entity but an impres-sive network formed by an order of tens of millions of neu-rons, most of them excitatory, and by about ten times moreglial cells. Ninety percent of the inputs received by a cor-tical area come from other areas of the cerebral cortex. Asa whole, the cerebral cortex can be viewed as a machinetalking to itself and could be seen as one big feedback sys-temsubjecttotherelentlessadvanceofentropy,whichsub-verts the exchange of messages that is essential to contin-ued existence (Wiener, 1954). This concept of interdepen-dent communications systems, also known as systems the-ory, coupled with Wiener's assertion that a machine thatchanges its responses based on feedback is a machine thatlearns, denes the cerebral cortex as a cybernetic machine.Therefore, the focus of investigation is shifted from com-munication and control to interaction. Systems theory hastraditionally focused more on the structure of systems andtheir models, whereas cybernetics has focused more onhow systems function, that is to say how they control theiractions, how they communicate with other systems or withtheir own components. However, structure and function ofa system cannot be understood in separation and cybernet-ics and systems theory should be viewed as two facets of asingle approach, dened as “the neuroheuristic approach”.2. Classical and Interactive ComputationMcCulloch and Pitts [2] proposed a modelization of thenervous system as a nite interconnection of logical de-vices. For the rst time, neural networks were consid-ered as discrete abstract machines, and the issue of theircomputational capabilities investigated from the automata-theoretic perspective. Further developments of this per-spective opened up the way to the theoretical approach toneural computation [3, 4, 5].A Turingmachine (TM)consistsofainnitetape,aheadthat can read and write on this tape, and a nite programwhich, according to the current computational state of themachine and the current symbol read by the head, deter-mines the next symbol to be written by the head on thetape, the next move of the head (left or right), and the nextcomputational state of the machine. The classical Turingparadigmofcomputationcorrespondstothecomputationalscenario where a system receives a nite input, processesthis input, and either provides a corresponding output ornever halts. According to the Church-Turing Thesis, theTuring machine model is capable of capturing all possibleaspects of algorithmic computation [6].The concept of a Turing machine with advise (TM /A)provides a model of computation beyond the Turing lim-its. It consists of a classical Turing machine provided withan additional advise function : N ! f 0;1g

[1]  Alessandro E. P. Villa,et al.  Integration and transmission of distributed deterministic neural activity in feed-forward networks , 2012, Brain Research.

[2]  Jan van Leeuwen,et al.  Beyond the Turing Limit: Evolving Interactive Systems , 2001, SOFSEM.

[3]  Alessandro E. P. Villa,et al.  Nerve growth factor modulates information processing in the auditory thalamus , 1996, Brain Research Bulletin.

[4]  Scott A. Smolka,et al.  Interactive Computation: The New Paradigm , 2006 .

[5]  Alessandro E. P. Villa,et al.  Emergence of Preferred Firing Sequences in Large Spiking Neural Networks during Simulated Neuronal Development , 2008, Int. J. Neural Syst..

[6]  Hava T. Siegelmann,et al.  On the Computational Power of Neural Nets , 1995, J. Comput. Syst. Sci..

[7]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[8]  René Thom,et al.  Structural stability and morphogenesis , 1977, Pattern Recognit..

[9]  I. Tetko,et al.  Spatiotemporal activity patterns of rat cortical neurons predict responses in a conditioned task. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Norbert Wiener,et al.  Cybernetics: Control and Communication in the Animal and the Machine. , 1949 .

[11]  S. Smale Differentiable dynamical systems , 1967 .

[12]  Alessandro E. P. Villa,et al.  The expressive power of analog recurrent neural networks on infinite input streams , 2012, Theor. Comput. Sci..

[13]  A. Turing On Computable Numbers, with an Application to the Entscheidungsproblem. , 1937 .

[14]  Marvin Minsky,et al.  Computation : finite and infinite machines , 2016 .

[15]  H T Siegelmann,et al.  Dating and Context of Three Middle Stone Age Sites with Bone Points in the Upper Semliki Valley, Zaire , 2007 .

[16]  Z. Nadasdy,et al.  Neurons of the cerebral cortex exhibit precise interspike timing in correspondence to behavior. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[17]  John von Neumann,et al.  The Computer and the Brain , 1960 .

[18]  F. Attneave,et al.  The Organization of Behavior: A Neuropsychological Theory , 1949 .

[19]  W. M. Cox,et al.  The new paradigm , 1999 .

[20]  Hava T. Siegelmann,et al.  The Dynamic Universality of Sigmoidal Neural Networks , 1996, Inf. Comput..

[21]  Hava T. Siegelmann,et al.  The Computational Power of Interactive Recurrent Neural Networks , 2012, Neural Computation.

[22]  S C Kleene,et al.  Representation of Events in Nerve Nets and Finite Automata , 1951 .

[23]  Peter Wegner,et al.  Interactive , 2021, Encyclopedia of the UN Sustainable Development Goals.

[24]  Jérémie Cabessa Interactive Evolving Recurrent Neural Networks Are Super-turing , 2012, ICAART.

[25]  Alessandro E. P. Villa,et al.  Neural Coding in the Neuroheuristic Perspective , 2008 .