Grewe, Benjamin F

Stadelmann, Thilo

von der Malsburg, Christoph

English

ZORA

Zurich Open Repository andArchiveUniversity of ZurichUniversity LibraryStrickhofstrasse 39CH-8057 Zurichwww.zora.uzh.chYear: 2022Making sense of the natural environmentvon der Malsburg, Christoph ; Grewe, Benjamin F ; Stadelmann, ThiloPosted at the Zurich Open Repository and Archive, University of ZurichZORA URL: https://doi.org/10.5167/uzh-231307Conference or Workshop ItemAccepted VersionOriginally published at:von der Malsburg, Christoph; Grewe, Benjamin F; Stadelmann, Thilo (2022). Making sense of the natural envi-ronment. In: The Biannual Conference of the German Cognitive Science Society (KogWis), Freiburg, Germany,5 September 2022 - 7 September 2022, s.n..Making Sense of the Natural EnvironmentChristoph von der Malsburg (FIAS, Frankfurt and INI, ETH Zürich), Benjamin Grewe (INI,ETH Zürich), Thilo Stadelmann (CAI, ZHAW Winterthur)The neural basis of cognition is unclear to this day. We here present a conceptual frameworkresolving the conflict Fodor & Pylyshyn (1988); Dever (2006) between symbolic and neuralapproaches. In our scheme, the cortical carriers of meaning are not individual neurons butsets of neurons supporting each other by mutual excitation. These sets and their supportingconnectivity are called ’net fragments’ or simply ’fragments.’ Also fragments activate only aspart of larger nets composed of overlapping fragments. Fragments play the role of compositesymbols. As each neuron can be part of several fragments, and each fragment can overlapwith several alternative other fragments, fragments can be likened to jigsaw puzzle piecesthat fit together in innumerable different arrangements. Any such arrangement must, however,conform to a highly non-trivial consistency condition.Net fragments and the composite nets they form are supported by specific patterns of synapticconnections. These are formed in development and learning by network self-organization, aprocess studied experimentally Goodhill (2007) and theoretically Willshaw & von der Malsburg(1979); Häussler & von der Malsburg (1983) on the example of the ontogenetic establishment ofretinotopic fiber projections. This process selects net structures that are sparse (limited fan-inand fan-out of connections at each neuron) and are self-consistent such that a sufficient numberof fibers converge on any one neuron from within the net. The composition rule for fragmentsto co-activate in a net is that together they form a net that is self-consistent (and would bestable under the process of network self-organization). Any particular large net (that is, set ofactive neurons) is unlikely to occur more than once in a life-time, so that only relatively smallfragments have a chance to be active again and again to thus reach stability under networkself-organization. But as these fragments overlap in multiple ways, cortex develops into anoverlay of net fragments that supports an infinitude of consistent large-scale nets.Among all possible thus-defined net structures a particular role is played by those that real-ize schema application. Each schema is an abstract structural description under which largenumbers of instances can be united Bartlett (1932); Minsky (1974); Schank & Abelson (1977).Invariant object recognition has been modeled as schema application Arathorn (2002); Ol-shausen et al. (1995); Hinton (1981); Kree & Zippelius (1988); von der Malsburg (1988) real-izable as a net that is representing schema, instance and the structure-preserving mappingbetween them Wolfrum et al. (2008). Natural intelligence may be defined as the ability of pur-suing vital goals and intentions in varying contexts. Behavioral control has been classicallydescribed as schema application Shettleworth (2010). We propose the composition of nets outof fragments as basis for this process von der Malsburg et al. (2022).In distinction to present-day artificial neural networks the human brain can learn and generalizefrom very few examples. It is a well-established insight Geman et al. (1992); Wolpert (1996)that such efficiency must be based on a deep structural relationship between learning systemand domain. Inherent in our neural representation framework is therefore the claim that alsothe environment can be seen as a composite of a finite set of structural fragment types.Keywords: neural representation, network self-organization, compositionality, net fragments,behavioral schema, intentions.ReferencesArathorn, D. (2002). Map-seeking circuits in visual cognition – a computational mechanism forbiological and machine vision. Stanford, California: Standford Univ. Press.Bartlett, F. (1932). Remembering, a study in experimental and social psychology. Cambridge:Cambridge University Press.Dever, J. (2006). Compositionality. In E. Lepore & B. Smith (Eds.), The oxford handbook ofphilosophy of language (pp. 633–666). Oxford University Press.Fodor, J., & Pylyshyn, Z. (1988). Connectionism and cognitive architecture: A critical analysis.Cognition, 28(1), 3-71. doi: 10.1016/0010-0277(88)90031-5Geman, S., Bienenstock, E., & Doursat, R. (1992). Neural networks and the bias/variancedilemma. Neural Computation, 4, 1-58.Goodhill, G. J. (2007). Contributions of theoetical modeling to the understanding of neural mapdevelopment. Neuron, 56, 301-311.Häussler, A. F., & von der Malsburg, C. (1983). Development of retinotopic projections:An analytical treatment. J. Theoretical Neurobiology , 2, 47–73. Retrieved fromhttps://vfs.fias.science/d/3cfce0fe5a/files/?p=/Retina.pdfHinton, G. E. (1981). A Parallel Computation that Assigns Canonical Object-Based Frames ofReference. In International joint conference on artificial intelligence (pp. 683–685).Kree, R., & Zippelius, A. (1988). Recognition of topological features of graphs and images inneural networks. J. Phys. A, 21, 813-818.Minsky, M. (1974, June). A framework for representing knowledge (Tech. Rep. No. 306). MITAI Laboratory.Olshausen, B., Anderson, C., & Van Essen, D. (1995). A multiscale dynamic routing circuit forforming size- and position-invariant object representations. Journal of ComputationalNeuroscience, 2, 45-62.Schank, R., & Abelson, R. (1977). Scripts, plans, goals and understanding: An inquiry intohuman knowledge structures. New Jersey: Erlbaum.Shettleworth, S. (2010). Cognition, evolution, and behavior (2nd ed.). Oxford: Oxford UniversityPress.von der Malsburg, C. (1988). Pattern recognition by labeled graph matching. Neural Networks,1, 141–148.von der Malsburg, C., Stadelmann, T., & Grewe, B. (2022). A theory of natural intelligence. doi:10.48550/ARXIV.2205.00002Willshaw, D. J., & von der Malsburg, C. (1979). A marker induction mechanism for the estab-lishment of ordered neural mappings; its application to the retinotectal problem. Philo-sophical Transactions of the Royal Society of London, Series B, 287 , 203–243.Wolfrum, P., Wolff, C., Lücke, J., & von der Malsburg, C. (2008). A recurrent dynamicmodel for correspondence-based face recognition. Journal of Vision, 8(7), 34. doi:10.1167/8.7.34Wolpert, D. (1996). The lack of a priori distinctions between learning algorithms. NeuralComputation, 8, 1341-1390.

Making sense of the natural environment

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Making sense of the natural environment

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