Counting with neurons: rule application with nets of fatiguing leaking integrate and fire neurons.
Conference paper
Huyck, C. and Belavkin, R. 2006. Counting with neurons: rule application with nets of fatiguing leaking integrate and fire neurons. 7th International Conference on Cognitive Modelling. Trieste, Italy pp. 142-147
| Type | Conference paper |
|---|---|
| Title | Counting with neurons: rule application with nets of fatiguing leaking integrate and fire neurons. |
| Authors | Huyck, C. and Belavkin, R. |
| Abstract | This paper shows a system that performs simple symbolic processing. The system is based entirely on fatiguing Leaky Integrate and Fire Neurons, a coarse model of neurons. following Hebb, the symbols are encoded by neirons that form Cell Assemblies. Additionally simple rules of the form i f X - X + 1 are encoded by Cell Assemblies, and this symbolic computation is performed. Finally, a more comples rule while X < F - X = X + 1 is encoded using variable binding via a compensatory learning rule. This rule performs the symbolic computation of counting entirely subsymbolically. the binding can be erased and reused via spontaneious neural activation. Unlike the symbolic parallel, the counting rule fails at times when humans might fail. |
| Research Group | Artificial Intelligence group |
| Conference | 7th International Conference on Cognitive Modelling |
| Page range | 142-147 |
| Publication process dates | |
| Deposited | 08 Sep 2008 |
| Output status | Published |
| Web address (URL) | http://csjarchive.cogsci.rpi.edu/ICCM/2006/ProceedingsICCM2006.pdf |
| Language | English |
| File |
Permalink -
https://repository.mdx.ac.uk/item/80qw2
Download files
154
total views12
total downloads3
views this month0
downloads this month
Export as
Related outputs
Quasi Biologically Plausible Category Learning
Huyck, C. 2024. Quasi Biologically Plausible Category Learning. 44th SGAI International Conference on Artificial Intelligence, AI 2024. Cambridge, UK 17 - 19 Dec 2024 Springer.Parameter tuning of the Firefly Algorithm by standard Monte Carlo and Quasi-Monte Carlo methods
Joy, G., Huyck, C. and Yang, X. 2024. Parameter tuning of the Firefly Algorithm by standard Monte Carlo and Quasi-Monte Carlo methods. Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V., Dongarra, J. and Sloot, P. (ed.) 24th International Conference on Computational Science. Malaga, Spain 02 - 04 Jul 2024 Cham Springer. pp. 242–253 https://doi.org/10.1007/978-3-031-63775-9_17A proposal for extending the Common Model of Cognition to emotion
Rosenbloom, P., Laird, J., Lebiere, C., Stocco, A., Granger, R. and Huyck, C. 2024. A proposal for extending the Common Model of Cognition to emotion. 22nd International Conference on Cognitive Modeling. Tilburg University, the Netherlands 19 - 22 Jul 2024An evolutionary approach to automated class-specific data augmentation for image classification
Marc, S., Belavkin, R., Windridge, D. and Gao, X. 2024. An evolutionary approach to automated class-specific data augmentation for image classification. Moosaei, H., Hladík, M. and Pardalos, P. (ed.) 6th International Conference on the Dynamics of Information Systems. Prague, Czech Republic 03 - 06 Dec 2023 Springer. pp. 170–185 https://doi.org/10.1007/978-3-031-50320-7_12Enhancing individual UAV path planning with Parallel Multi-Swarm Treatment Coronavirus Herd Immunity Optimizer (PMST-CHIO) algorithm
Fouad, A., Abboudi, A., Huyck, C., Gao, X., Bououden, S., Khezami, N. and Shall, H. 2024. Enhancing individual UAV path planning with Parallel Multi-Swarm Treatment Coronavirus Herd Immunity Optimizer (PMST-CHIO) algorithm. IEEE Access. 12, pp. 28395-28416. https://doi.org/10.1109/ACCESS.2024.3367753Associative memory with biologically-inspired cell assemblies
Ji, Y., Gamez, D. and Huyck, C. 2024. Associative memory with biologically-inspired cell assemblies. Samsonovich, A.V. and Liu, T. (ed.) 2023 Annual International Conference on Brain-Inspired Cognitive Architectures for Artificial Intelligence, the 14th Annual Meeting of the BICA Society (BICA*AI 2023). Ningbo, China 13 - 15 Oct 2023 Springer. pp. 422-428 https://doi.org/10.1007/978-3-031-50381-8_43A spiking model of Cell Assemblies: Short term and associative memory
Huyck, C. 2023. A spiking model of Cell Assemblies: Short term and associative memory. Medical Research Archives. 11 (9), pp. 1-20. https://doi.org/10.18103/mra.v11i9.4406Bridging neuroscience and robotics: spiking neural networks in action
Jones, A., Gandhi, V., Mahiddine, A. and Huyck, C. 2023. Bridging neuroscience and robotics: spiking neural networks in action. Sensors. 23 (21), pp. 1-14. https://doi.org/10.3390/s23218880Review of parameter tuning methods for nature-inspired algorithms
Joy, G., Huyck, C. and Yang, X. 2023. Review of parameter tuning methods for nature-inspired algorithms. in: Yang, X. (ed.) Benchmarks and Hybrid Algorithms in Optimization and Applications Singapore Springer. pp. 33-47Value of information in the mean-square case and its application to the analysis of financial time-series forecast
Belavkin, R., Pardalos, P. and Principe, J. 2023. Value of information in the mean-square case and its application to the analysis of financial time-series forecast. Simos, D., Rasskazova, V., Archetti, F., Kotsireas, I. and Pardalos, P. (ed.) 16th International Conference on Learning and Intelligent Optimization. Milos Island, Greece 05 - 10 Jun 2022 Cham Springer. https://doi.org/10.1007/978-3-031-24866-5_39Competitive learning with spiking nets and spike timing dependent plasticity
Huyck, C. and Orume, E. 2022. Competitive learning with spiking nets and spike timing dependent plasticity. Bramer, M. and Stahl, F. (ed.) AI-2022: The Forty-second SGAI International Conference. Cambridge, England, UK 13 - 15 Dec 2022 Springer. pp. 153-166 https://doi.org/10.1007/978-3-031-21441-7_11Value of information in the binary case and confusion matrix
Belavkin, R., Pardalos, P. and Principe, J. 2022. Value of information in the binary case and confusion matrix. 41st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. Paris, France 18 - 22 Jul 2022 MDPI. pp. 1-9 https://doi.org/10.3390/psf2022005008Cell Assembly-based Task Analysis (CAbTA)
Diaper, D. and Huyck, C. 2021. Cell Assembly-based Task Analysis (CAbTA). Arai, K. (ed.) Computing Conference 2021 (formerly called Science and Information (SAI) Conference). Virtual (from London, UK) 15 - 16 Jul 2021 Springer. https://doi.org/10.1007/978-3-030-80119-9_22Learning categories with spiking nets and spike timing dependent plasticity
Huyck, C. 2020. Learning categories with spiking nets and spike timing dependent plasticity. Bramer, M. and Ellis, R. (ed.) 40th SGAI 2020. Cambridge, UK 15 - 17 Dec 2020 Springer. pp. 139-144 https://doi.org/10.1007/978-3-030-63799-6_10Hot coffee: associative memory with bump attractor cell assemblies of spiking neurons
Huyck, C. and Vergani, A. 2020. Hot coffee: associative memory with bump attractor cell assemblies of spiking neurons. Journal of Computational Neuroscience. 48 (3), pp. 299-316. https://doi.org/10.1007/s10827-020-00758-1Are quiz-games an effective revision tool in Anatomical Sciences for Higher Education and what do students think of them?
Wilkinson, K., Dafoulas, G., Garelick, H. and Huyck, C. 2020. Are quiz-games an effective revision tool in Anatomical Sciences for Higher Education and what do students think of them? British Journal of Educational Technology. 51 (3), pp. 761-777. https://doi.org/10.1111/bjet.12883A neural cognitive architecture
Huyck, C. 2020. A neural cognitive architecture. Cognitive Systems Research. 59, pp. 171-178. https://doi.org/10.1016/j.cogsys.2019.09.023Relation between the Kantorovich-Wasserstein metric and the Kullback-Leibler divergence
Belavkin, R. 2018. Relation between the Kantorovich-Wasserstein metric and the Kullback-Leibler divergence. IGAIA IV 2016: Information Geometry and Its Applications. Liblice, Czech Republic 12 - 17 Jun 2016 Springer International Publishing. https://doi.org/10.1007/978-3-319-97798-0_15A brain-inspired cognitive system that mimics the dynamics of human thought
Ji, Y., Gamez, D. and Huyck, C. 2018. A brain-inspired cognitive system that mimics the dynamics of human thought. AI-2018 Thirty-eighth SGAI International Conference on Artificial Intelligence. Cambridge, UK 11 - 13 Dec 2018 Springer. pp. 50-62 https://doi.org/10.1007/978-3-030-04191-5_4Two simple NeuroCognitive associative memory models
Huyck, C. and Ji, Y. 2018. Two simple NeuroCognitive associative memory models. International Conference on Cognitive Modeling 2018. Madison Wisconsin 20 - 24 Jul 2018 pp. 31-36Implementing Rules with Aritificial Neurons
Huyck, C. and Kreivena, D. 2018. Implementing Rules with Aritificial Neurons. AI-2018 38th SGAI International Conference on Artificial Intelligence. Cambridge 11 - 13 Dec 2018 Springer. pp. 21-33 https://doi.org/10.1007/978-3-030-04191-5_2A spiking half-cognitive model for classification
Huyck, C. and Kulkarni, R. 2018. A spiking half-cognitive model for classification. Connection Science. 30 (3), pp. 285-305. https://doi.org/10.1080/09540091.2018.1443317CABots and other neural agents
Huyck, C. and Mitchell, I. 2018. CABots and other neural agents. Frontiers in Neurorobotics. 12, pp. 1-12. https://doi.org/10.3389/fnbot.2018.00079Environmental pleiotropy and demographic history direct adaptation under antibiotic selection
Gifford, D., Krašovec, R., Aston, E., Belavkin, R., Channon, A. and Knight, C. 2018. Environmental pleiotropy and demographic history direct adaptation under antibiotic selection. Heredity. 121 (5), pp. 438-448. https://doi.org/10.1038/s41437-018-0137-3Opposing effects of final population density and stress on Escherichia coli mutation rate
Krašovec, R., Richards, H., Gifford, D., Belavkin, R., Channon, A., Aston, E., McBain, A. and Knight, C. 2018. Opposing effects of final population density and stress on Escherichia coli mutation rate. The ISME journal. 12 (12), pp. 2981-2987. https://doi.org/10.1038/s41396-018-0237-3The neural cognitive architecture
Huyck, C. 2017. The neural cognitive architecture. AAAI 2017 FALL Symposium Series: Symposium on A Standard Models of the Mind. Arlington, Virginia, USA 09 - 11 Nov 2017 Association for the Advancement of Artificial Intelligence (AAAI). pp. 365-370Neuron-based control mechanisms for a robotic arm and hand
Singh, N., Huyck, C., Gandhi, V. and Jones, A. 2017. Neuron-based control mechanisms for a robotic arm and hand. International Journal of Computer, Electrical, Automation, Control and Information Engineering. 11 (2), pp. 221-229. https://doi.org/10.5281/zenodo.1128871Critical mutation rate has an exponential dependence on population size for eukaryotic-length genomes with crossover
Aston, E., Channon, A., Belavkin, R., Gifford, D., Krašovec, R. and Knight, C. 2017. Critical mutation rate has an exponential dependence on population size for eukaryotic-length genomes with crossover. Scientific Reports. 7 (1). https://doi.org/10.1038/s41598-017-14628-xSpontaneous mutation rate is a plastic trait associated with population density across domains of life
Krašovec, R., Richards, H., Gifford, D., Hatcher, C., Faulkner, K., Belavkin, R., Channon, A., Aston, E., McBain, A. and Knight, C. 2017. Spontaneous mutation rate is a plastic trait associated with population density across domains of life. PLOS Biology. 15 (8). https://doi.org/10.1371/journal.pbio.2002731Programming a cognitive architecture with simulated neurons, Chris Eliasmith. How to Build a Brain: A Neural Architecture for Biological Cognition. Oxford University Press, Oxford (2013). 456 pp., ISBN: 978-0-19-026212-9 [Book review]
Huyck, C. 2017. Programming a cognitive architecture with simulated neurons, Chris Eliasmith. How to Build a Brain: A Neural Architecture for Biological Cognition. Oxford University Press, Oxford (2013). 456 pp., ISBN: 978-0-19-026212-9 [Book review]. Cognitive Systems Research. 41, pp. 36-37. https://doi.org/10.1016/j.cogsys.2016.09.002Programming with simulated neurons: a first design pattern
Evans, C., Mitchell, I. and Huyck, C. 2016. Programming with simulated neurons: a first design pattern. PPIG 2016 - 27th Annual Workshop of the Psychology of Programming Interest Group. University of Cambridge, Cambridge, UK 07 - 10 Sep 2016 Psychology of Programming Interest Group. pp. 36-45PlaNeural: spiking neural networks that plan
Mitchell, I., Huyck, C. and Evans, C. 2016. PlaNeural: spiking neural networks that plan. 7th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2016. New York City, NY, USA 16 Jul 2016 Elsevier. pp. 198-204 https://doi.org/10.1016/j.procs.2016.07.425Monotonicity of fitness landscapes and mutation rate control
Belavkin, R., Channon, A., Aston, E., Aston, J., Krašovec, R. and Knight, C. 2016. Monotonicity of fitness landscapes and mutation rate control. Journal of Mathematical Biology. 73 (6-7), pp. 1491-1524. https://doi.org/10.1007/s00285-016-0995-3Advancing ambient assisted living with caution
Huyck, C., Augusto, J., Gao, X. and Botia, J. 2015. Advancing ambient assisted living with caution. in: Helfert, M., Holzinger, A., Ziefle, M., Fred, A., O'Donoghue, J. and Röcker, C. (ed.) Information and Communication Technologies for Ageing Well and e-Health: First International Conference, ICT4AgeingWell 2015, Lisbon, Portugal, May 20-22, 2015. Revised Selected Papers Springer.Neural constraints and flexibility in language processing
Huyck, C. 2016. Neural constraints and flexibility in language processing. Behavioral and Brain Sciences: An International Journal of Current Research and Theory with Open Peer Commentary. 39, p. e78. https://doi.org/10.1017/s0140525x15000837Self organising maps with a point neuron model
Huyck, C. and Mitchell, I. 2013. Self organising maps with a point neuron model. Intl Conf. on Cognitive and Neural Systems.A comparison of simple agents implemented in simulated neurons
Huyck, C., Evans, C. and Mitchell, I. 2015. A comparison of simple agents implemented in simulated neurons. Biologically Inspired Cognitive Architectures. 12, pp. 9-19. https://doi.org/10.1016/j.bica.2015.03.001Programming the MIRTO robot with neurons
Huyck, C., Primiero, G. and Raimondi, F. 2014. Programming the MIRTO robot with neurons. Procedia Computer Science. 41, pp. 75-82. https://doi.org/10.1016/j.procs.2014.11.087A neuro-computational approach to PP attachment ambiguity resolution
Nadh, K. and Huyck, C. 2012. A neuro-computational approach to PP attachment ambiguity resolution. Neural Computation. 24 (7), pp. 1906-1925. https://doi.org/10.1162/NECO_a_00298A review of cell assemblies
Huyck, C. and Passmore, P. 2013. A review of cell assemblies. Biological Cybernetics. 107 (3), pp. 263-288. https://doi.org/10.1007/s00422-013-0555-5Compensatory Hebbian learning for categorisation in simulated biological neural nets
Huyck, C. and Mitchell, I. 2013. Compensatory Hebbian learning for categorisation in simulated biological neural nets. Biologically Inspired Cognitive Architectures. 6 (5), pp. 3-7. https://doi.org/10.1016/j.bica.2013.06.003Asymmetric topologies on statistical manifolds
Belavkin, R. 2015. Asymmetric topologies on statistical manifolds. 2nd International Conference on Geometric Science of Information. Palaiseau, France 28 - 30 Oct 2015 Springer. https://doi.org/10.1007/978-3-319-25040-3_23Local semantic indexing for resource discovery on overlay network using mobile agents
Singh, M., Cheng, X. and Belavkin, R. 2014. Local semantic indexing for resource discovery on overlay network using mobile agents. International Journal of Computational Intelligence Systems. 7 (3), pp. 432-455. https://doi.org/10.1080/18756891.2013.856257Where antibiotic resistance mutations meet quorum-sensing
Krašovec, R., Belavkin, R., Aston, J., Channon, A., Aston, E., Rash, B., Kadirvel, M., Forbes, S. and Knight, C. 2014. Where antibiotic resistance mutations meet quorum-sensing. Microbial Cell. 1 (7), pp. 250-252. https://doi.org/10.15698/mic2014.07.158On variational definition of quantum entropy
Belavkin, R. 2014. On variational definition of quantum entropy. 34th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2014). Clos Lucé, Amboise, France 21 - 26 Sep 2014 American Institute of Physics (AIP). https://doi.org/10.1063/1.4905979Post and pre-compensatory Hebbian Learning for categorisation
Huyck, C. and Mitchell, I. 2014. Post and pre-compensatory Hebbian Learning for categorisation. Cognitive Neurodynamics. 8 (4), pp. 299-311. https://doi.org/10.1007/s11571-014-9282-4Asymmetry of risk and value of information
Belavkin, R. 2014. Asymmetry of risk and value of information. in: Vogiatzis, C., Walteros, J. and Pardalos, P. (ed.) Dynamics of Information Systems: Computational and Mathematical Challenges Springer.Mutation rate plasticity in rifampicin resistance depends on Escherichia coli cell–cell interactions
Krašovec, R., Belavkin, R., Aston, J., Channon, A., Aston, E., Rash, B., Kadirvel, M., Forbes, S. and Knight, C. 2014. Mutation rate plasticity in rifampicin resistance depends on Escherichia coli cell–cell interactions. Nature Communications. 5, pp. 1-8. https://doi.org/10.1038/ncomms4742True global optimality of the pressure vessel design problem: a benchmark for bio-inspired optimisation algorithms
Yang, X., Huyck, C., Karamanoglu, M. and Khan, N. 2013. True global optimality of the pressure vessel design problem: a benchmark for bio-inspired optimisation algorithms. International Journal of Bio-Inspired Computation. 5 (6), pp. 329-335. https://doi.org/10.1504/IJBIC.2013.058910Minimum of information distance criterion for optimal control of mutation rate in evolutionary systems
Belavkin, R. 2013. Minimum of information distance criterion for optimal control of mutation rate in evolutionary systems. in: Accardi, L., Freudenberg, W. and Ohya, M. (ed.) Quantum Bio-Informatics V : Proceedings of the Quantum Bio-Informatics 2011. Tokyo University of Science, Japan, 7 – 12 March 2011 World Scientific Publishing Co. Pte Ltd.Law of cosines and Shannon-Pythagorean theorem for quantum information
Belavkin, R. 2013. Law of cosines and Shannon-Pythagorean theorem for quantum information. in: Nielsen, F. and Barbaresco, F. (ed.) Geometric Science of Information : First International Conference, GSI 2013, Paris, France, August 28-30, 2013. Proceedings Berlin Springer.Optimal measures and Markov transition kernels
Belavkin, R. 2013. Optimal measures and Markov transition kernels. Journal of Global Optimization. 55 (2), pp. 387-416. https://doi.org/10.1007/s10898-012-9851-1Critical mutation rate has an exponential dependence on population size
Channon, A., Aston, E., Day, C., Belavkin, R. and Knight, C. 2011. Critical mutation rate has an exponential dependence on population size. Lenaerts, T., Giacobini, M., Bersini, H., Bourgine, P., Dorigo, M. and Doursat, R. (ed.) ECAL 2011: The 11th European Conference on Artificial Life. Paris, France 08 - 12 Aug 2011 The MIT Press. pp. 117-124 https://doi.org/10.7551/978-0-262-29714-1-ch021Dynamics of information and optimal control of mutation in evolutionary systems
Belavkin, R. 2012. Dynamics of information and optimal control of mutation in evolutionary systems. in: Sorokin, A., Murphey, R., Thai, M. and Pardalos, P. (ed.) Dynamics of Information Systems: Mathematical Foundations New York Springer.Maximal connectivity and constraints in the human brain
Belavkin, R. 2012. Maximal connectivity and constraints in the human brain. in: Pardalos, P., Coleman, T. and Xanthopoulos, P. (ed.) Optimization and data analysis in biomedical informatics New York Springer.On evolution of an information dynamic system and its generating operator
Belavkin, R. 2012. On evolution of an information dynamic system and its generating operator. Optimization Letters. 6 (5), pp. 827-840. https://doi.org/10.1007/s11590-011-0325-zCell assemblies for query expansion in information retrieval
Volpe, I., Moreira, V. and Huyck, C. 2011. Cell assemblies for query expansion in information retrieval. 2011 International Joint Conference on Neural Networks (IJCNN). San Jose, CA, USA 31 Jul - 05 Aug 2011 IEEE. pp. 551-558 https://doi.org/10.1109/IJCNN.2011.6033269Theory and practice of optimal mutation rate control in Hamming spaces of DNA sequences
Belavkin, R., Channon, A., Aston, E., Aston, J. and Knight, C. 2011. Theory and practice of optimal mutation rate control in Hamming spaces of DNA sequences. Lenaerts, T., Giacobini, M., Bersini, H., Bourgine, P., Dorigo, M. and Doursat, R. (ed.) ECAL 2011: The 11th European Conference on Artificial Life. Paris, France 08 - 12 Aug 2011 The MIT Press. pp. 85-92 https://doi.org/10.7551/978-0-262-29714-1-ch017Mutation and optimal search of sequences in nested Hamming spaces
Belavkin, R. 2011. Mutation and optimal search of sequences in nested Hamming spaces. IEEE Information Theory Workshop (ITW). Paraty, Brazil 16 - 20 Oct 2011 IEEE. pp. 90-94 https://doi.org/10.1109/ITW.2011.6089592Conflict resolution and learning probability matching in a neural cell-assembly architecture
Belavkin, R. and Huyck, C. 2011. Conflict resolution and learning probability matching in a neural cell-assembly architecture. Cognitive Systems Research. 12 (2), pp. 93-101. https://doi.org/10.1016/j.cogsys.2010.08.003A Pong playing agent modelled with massively overlapping cell assemblies
Nadh, K. and Huyck, C. 2010. A Pong playing agent modelled with massively overlapping cell assemblies. Neurocomputing. 73 (16-18), pp. 2928-2934. https://doi.org/10.1016/j.neucom.2010.07.013Multi-associative memory in fLIF cell assemblies.
Huyck, C. and Nadh, K. 2009. Multi-associative memory in fLIF cell assemblies. 9th International Conference on Cognitive Modeling. Manchester 24 - 26 Jul 2009Processing with cell assemblies
Byrne, E. and Huyck, C. 2010. Processing with cell assemblies. Neurocomputing. 74 (1-3), pp. 76-83. https://doi.org/10.1016/j.neucom.2009.09.024Using cohesive devices to recognize rhetorical relations in text.
Le, H., Abeysinghe, G. and Huyck, C. 2003. Using cohesive devices to recognize rhetorical relations in text. 4th Computational Linguistics UK Research Colloquium (CLUK-4). Edinburgh University Jan 2003 pp. 123-128Automated discourse segmentation by syntactic information and cue phrases.
Le, H., Abeysinghe, G. and Huyck, C. 2004. Automated discourse segmentation by syntactic information and cue phrases. IASTED International Conference on Artificial Intelligence and Applications (AIA 2004). Innsbruck, Austria 16 - 18 Feb 2004 pp. 293-298Generating discourse structures for written texts
Le, H., Abeysinghe, G. and Huyck, C. 2004. Generating discourse structures for written texts. International Conference on Computational Linguistics, (COLING 2004). University of Geneva, Switzerland 23 - 27 Aug 2004 pp. 329-355A study to improve the efficiency of a discourse parsing system
Le, H., Abeysinghe, G. and Huyck, C. 2003. A study to improve the efficiency of a discourse parsing system. 4th International Conference on Intelligent Text Processing and Computational Linguistics, (CICLing’03). Mexico City 16 - 22 Feb 2003 pp. 101-114Towards a theory of decision-making without paradoxes.
Belavkin, R. 2006. Towards a theory of decision-making without paradoxes. Fum, D., Missier, F. and Stocco, A. (ed.) Proceedings of the Seventh International Conference on Cognitive Modeling. Trieste, Italy 05 - 08 Apr 2006 Trieste, Italy Edizioni Goliardiche. pp. 38-43Emergence of rules in cell assemblies of fLIF neurons.
Belavkin, R. and Huyck, C. 2008. Emergence of rules in cell assemblies of fLIF neurons. The 18th European Conference on Artificial Intelligence. University of Patras, Greece 21 - 25 Jul 2008A model of probability matching in a two-choice task based on stochastic control of learning in neural cell-assemblies.
Belavkin, R. and Huyck, C. 2009. A model of probability matching in a two-choice task based on stochastic control of learning in neural cell-assemblies. 9th International conference on cognitive modelling {ICCM 2009]. University of Manchester 24 - 26 Jul 2009Models of cell assembly decay
Passmore, P. and Huyck, C. 2008. Models of cell assembly decay. Institute of Electrical and Electronics Engineers. pp. 1-6 https://doi.org/10.1109/UKRICIS.2008.4798946Dialogue based interfaces for universal access.
Huyck, C. 2010. Dialogue based interfaces for universal access. Universal Access in the Information Society. https://doi.org/10.1007/s10209-010-0209-5A psycholinguistic model of natural language parsing implemented in simulated neurons
Huyck, C. 2009. A psycholinguistic model of natural language parsing implemented in simulated neurons. Cognitive Neurodynamics. 3 (4), pp. 316-330. https://doi.org/10.1007/s11571-009-9080-6Variable binding by synaptic strength change
Huyck, C. 2009. Variable binding by synaptic strength change. Connection Science. 21 (4), pp. 327-357. https://doi.org/10.1080/09540090902954188Resource discovery using mobile agents
Singh, M., Cheng, X. and Belavkin, R. 2010. Resource discovery using mobile agents. Frontier of Computer Science and Technology (FCST), 2010 Fifth International Conference. Changchun, Jilin Province 18 - 22 Aug 2010 IEEE. pp. 72 -77 https://doi.org/10.1109/FCST.2010.93Utility and value of information in cognitive science, biology and quantum theory.
Belavkin, R. 2010. Utility and value of information in cognitive science, biology and quantum theory. Accardi, L., Freudenberg, W. and Ohya, M. (ed.) Quantum Bio-Informatics III. Tokyo, Japan 11 - 14 Mar 2009 London World Scientific Publishing Co. Pte Ltd. pp. 33-47 https://doi.org/10.1142/9789814304061_0004Information trajectory of optimal learning
Belavkin, R. 2010. Information trajectory of optimal learning. in: Hirsch, M., Pardalos, P. and Murphey, R. (ed.) Dynamics of Information Systems: Theory and Applications Springer.Prepositional phrase attachment ambiguity resolution using semantic hierarchies
Nadh, K. and Huyck, C. 2009. Prepositional phrase attachment ambiguity resolution using semantic hierarchies. Hamza, M. (ed.) 9th IASTED International Conference on Artificial Intelligence and Applications. Innsbruck, Austria 17 - 18 Feb 2009 Acta Press.Learning behaviour patterns of classroom and distance students using flexible learning resources.
Dimitrova, M., Belavkin, R., Milankovic-Atkinson, M., Sadler, C. and Murphy, A. 2003. Learning behaviour patterns of classroom and distance students using flexible learning resources. in: Lee, K. and Mitchell, K. (ed.) International conference on computers in education 2003: a conference of the Asia-Pacific chapter of the association for the advancement of computing in education (AACE). Hong Kong ICCE.Neural cell assemblies for practical applications.
Huyck, C. and Bavan, A. 2002. Neural cell assemblies for practical applications. in: Callaos, N. (ed.) SCI 2002: ISAS: the 6th world multiconference on systemics, cybernetics and informatics: proceedings. Orlando, Florida. International Institute of Informatics and Systemics.. pp. 174-177Agent design method for enhancing accessibility.
Cook, J., Huyck, C. and Whitney, G. 2004. Agent design method for enhancing accessibility. in: McLoughlin, C. and Cantoni, L. (ed.) ED-MEDIA 2004: world conference on educational multimedia, hypermedia and telecommunications: proceedings of ED-MEDIA 2004. Association for the Advancement of Computing in Education.Bounds of optimal learning
Belavkin, R. 2009. Bounds of optimal learning. 2009 IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning. Nashville, TN, USA 30 Mar - 02 Apr 2009 IEEE. pp. 199-204 https://doi.org/10.1109/ADPRL.2009.4927545The use of entropy for analysis and control of cognitive models
Belavkin, R. and Ritter, F. 2003. The use of entropy for analysis and control of cognitive models. The Fifth International Conference on Cognitive Modelling. Bamberg, Germany 2003 pp. 21-26Conflict resolution by random estimated costs
Belavkin, R. 2003. Conflict resolution by random estimated costs. 17th European Simulation Multiconference. Nottingham UK pp. 105-110On relation between emotion and entropy
Belavkin, R. 2004. On relation between emotion and entropy. AISB'04 Symposium on Emotion, Cognition and Affective Computing. Leeds UK pp. 1-8OPTIMIST: A new conflict resolution algorithm for ACT-R.
Belavkin, R. and Ritter, F. 2004. OPTIMIST: A new conflict resolution algorithm for ACT-R. Sixth International Conference on Cognitive Modelling. Mahwah, NJ pp. 40-45