001052376 001__ 1052376
001052376 005__ 20260220104610.0
001052376 0247_ $$2arXiv$$aarXiv:2512.24977
001052376 037__ $$aFZJ-2026-00970
001052376 088__ $$2arXiv$$aarXiv:2512.24977
001052376 1001_ $$0P:(DE-Juel1)171197$$aZajzon, Barna$$b0$$eCorresponding author
001052376 245__ $$aSymSeqBench: a unified framework for the generation and analysis of rule-based symbolic sequences and datasets
001052376 260__ $$c2025
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001052376 520__ $$aSequential structure is a key feature of multiple domains of natural cognition and behavior, such as language, movement and decision-making. Likewise, it is also a central property of tasks to which we would like to apply artificial intelligence. It is therefore of great importance to develop frameworks that allow us to evaluate sequence learning and processing in a domain agnostic fashion, whilst simultaneously providing a link to formal theories of computation and computability. To address this need, we introduce two complementary software tools: SymSeq, designed to rigorously generate and analyze structured symbolic sequences, and SeqBench, a comprehensive benchmark suite of rule-based sequence processing tasks to evaluate the performance of artificial learning systems in cognitively relevant domains. In combination, SymSeqBench offers versatility in investigating sequential structure across diverse knowledge domains, including experimental psycholinguistics, cognitive psychology, behavioral analysis, neuromorphic computing and artificial intelligence. Due to its basis in Formal Language Theory (FLT), SymSeqBench provides researchers in multiple domains with a convenient and practical way to apply the concepts of FLT to conceptualize and standardize their experiments, thus advancing our understanding of cognition and behavior through shared computational frameworks and formalisms. The tool is modular, openly available and accessible to the research community.
001052376 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x0
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001052376 536__ $$0G:(DE-82)BMBF-16ME0398K$$aBMBF 16ME0398K - Verbundprojekt: Neuro-inspirierte Technologien der künstlichen Intelligenz für die Elektronik der Zukunft - NEUROTEC II - (BMBF-16ME0398K)$$cBMBF-16ME0398K$$x2
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001052376 536__ $$0G:(DE-Juel1)BMBF-03ZU1106CB$$aBMBF 03ZU1106CB - NeuroSys: Algorithm-Hardware Co-Design (Projekt C) - B (BMBF-03ZU1106CB)$$cBMBF-03ZU1106CB$$x4
001052376 536__ $$0G:(DE-Juel1)BMBF-03ZU2106CB$$aBMFTR 03ZU2106CB - NeuroSys: Algorithm-Hardware Co-Design (Projekt C) - B (BMBF-03ZU2106CB)$$cBMBF-03ZU2106CB$$x5
001052376 536__ $$0G:(BMBF)01IS22094B$$aWestAI - AI Service Center West (01IS22094B)$$c01IS22094B$$x6
001052376 588__ $$aDataset connected to arXivarXiv
001052376 7001_ $$0P:(DE-Juel1)176778$$aBouhadjar, Younes$$b1
001052376 7001_ $$0P:(DE-Juel1)201205$$aFabre, Maxime$$b2
001052376 7001_ $$0P:(DE-HGF)0$$aSchmidt, Felix$$b3
001052376 7001_ $$0P:(DE-Juel1)209829$$aOstendorf, Noah$$b4
001052376 7001_ $$0P:(DE-Juel1)188273$$aNeftci, Emre$$b5
001052376 7001_ $$0P:(DE-Juel1)151166$$aMorrison, Abigail$$b6
001052376 7001_ $$0P:(DE-Juel1)165640$$aDuarte, Renato$$b7
001052376 773__ $$aarXiv:2512.24977
001052376 909CO $$ooai:juser.fz-juelich.de:1052376$$pVDB
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001052376 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5234$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x1
001052376 9141_ $$y2025
001052376 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
001052376 9201_ $$0I:(DE-Juel1)PGI-15-20210701$$kPGI-15$$lNeuromorphic Software Eco System$$x1
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