Yilmaz, Ozgur2025-10-242025-10-242015978966654489997880737800299788024810256978998634274897880737817129782954494807978802482391197895623619898024810255807378002X1613-0073https://hdl.handle.net/20.500.12899/3261NIPS Workshop on Cognitive Computation, CoCo 2015 -- -- Montreal; QC -- 122144In this paper, we introduce a framework of reservoir computing that is capable of both connectionist machine intelligence and symbolic computation. Cellular automaton is used as the reservoir of dynamical systems. A cellular automaton is a very sparsely connected network with logical nodes and nonlinear/logical connection functions, hence the proposed system corresponds to a binary valued and nonlinear neuro-symbolic architecture. Input is randomly projected onto the initial conditions of automaton cells and nonlinear computation is performed on the input via application of a rule in the automaton for a period of time. The evolution of the automaton creates a space-time volume of the automaton state space, and it is used as the reservoir. In addition to being used as the feature representation for pattern recognition, binary reservoir vectors can be combined using Boolean operations as in hyperdimensional computing, paving a direct way symbolic processing. To demonstrate the capability of the proposed system, we make analogies directly on image data by asking 'What is the Automobile of Air'?, and make logical inference using rules by asking 'Which object is the largest?'. © 2016 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/closedAccessArtificial intelligenceBinsCellular automataDynamical systemsPattern recognitionConnection functionFeature representationInitial conditionsMachine intelligenceNonlinear computationsReservoir ComputingSymbolic computationSymbolic processingCognitive systemsConference Object15832-s2.0-84977506190Q4