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Volumn 24, Issue 2-3, 2012, Pages 71-90

Programming in the brain: A neural network theoretical framework

Author keywords

biologically plausible neural architecture; CTRNN; fixed weight network; programmability; programmable neural networks

Indexed keywords

COGNITIVE DOMAIN; CONSTRUCTION SCHEME; CTRNN; EXTENSIVE TESTING; NEURAL ARCHITECTURES; PROGRAMMABILITY; RECENT RESEARCHES; THEORETICAL FRAMEWORK;

EID: 84871869326     PISSN: 09540091     EISSN: 13600494     Source Type: Journal    
DOI: 10.1080/09540091.2012.684670     Document Type: Article
Times cited : (18)

References (46)
  • 1
    • 0002267991 scopus 로고    scopus 로고
    • Model Neurons: From Hodgkin-Huxley to Hopfield
    • In: Garrido L., editors XI Sitges Conference, Berlin, Berlin,: Springer-Verlag
    • Abbott, L. F. and Kepler, T. B. Model Neurons: From Hodgkin-Huxley to Hopfield. XI Sitges Conference. Statistical Mechanics of Neural Networks, Edited by: Garrido, L. pp.5-18. Berlin: Springer-Verlag.
    • Statistical Mechanics of Neural Networks , pp. 5-18
    • Abbott, L.F.1    Kepler, T.B.2
  • 2
    • 0031049135 scopus 로고    scopus 로고
    • Multimodal Representation of Space in the Posterior Parietal Cortex and Its Use in Planning Movements
    • doi:10.1146/annurev.neuro.20.1.303
    • Andersen, R., Snyder, L., Bradley, D. and Xing, J. 1997. Multimodal Representation of Space in the Posterior Parietal Cortex and Its Use in Planning Movements. Annual Review of Neuroscience, 20: 303-330. (doi:10.1146/annurev.neuro.20.1.303)
    • (1997) Annual Review of Neuroscience , vol.20 , pp. 303-330
    • Andersen, R.1    Snyder, L.2    Bradley, D.3    Xing, J.4
  • 3
    • 78649978073 scopus 로고    scopus 로고
    • Neural Re-use as a Fundamental Organizational Principle of the Brain
    • doi:10.1017/S0140525X10000853
    • Anderson, M. L. 2010. Neural Re-use as a Fundamental Organizational Principle of the Brain. Behavioral and Brain Sciences, 33(4): 245-266. (doi:10.1017/S0140525X10000853)
    • (2010) Behavioral and Brain Sciences , vol.33 , Issue.4 , pp. 245-266
    • Anderson, M.L.1
  • 4
    • 84977060605 scopus 로고
    • On the Dynamics of Small Continuous-Time Recurrent Neural Networks
    • doi:10.1177/105971239500300405
    • Beer, R. D. 1995. On the Dynamics of Small Continuous-Time Recurrent Neural Networks. Adaptive Behavior, 3(4): 469-509. (doi:10.1177/105971239500300405)
    • (1995) Adaptive Behavior , vol.3 , Issue.4 , pp. 469-509
    • Beer, R.D.1
  • 5
    • 84957801420 scopus 로고    scopus 로고
    • CTRNN Parameter Learning Using Differential Evolution
    • In: Ghallab M., Spyropoulos C. D., Fakotakis N., Avouris N., editors ECAI 2008, 18th European Conference on Artificial Intelligence, Amsterdam, The Netherlands, Amsterdam,: IOS Press
    • De Falco, I., Della Cioppa, A., Donnarumma, F., Maisto, D., Prevete, R. and Tarantino, E. CTRNN Parameter Learning Using Differential Evolution. ECAI 2008, 18th European Conference on Artificial Intelligence. Edited by: Ghallab, M., Spyropoulos, C. D., Fakotakis, N. and Avouris, N. pp.783-784. Amsterdam, The Netherlands: IOS Press.
    • de Falco, I.1    Della Cioppa, A.2    Donnarumma, F.3    Maisto, D.4    Prevete, R.5    Tarantino, E.6
  • 6
    • 33645769652 scopus 로고    scopus 로고
    • Evolution of Human Cortical Circuits for Reading an Arithmetic: The Neuronal Recycling' Hypothesis, from Monkey Brain to Human Brain
    • In: Dehaene S., Duhamel J.-R., Hauser M. D., Rizzolatti G., editors Evolution of Human Cortical Circuits for Reading an Arithmetic: The 'Neuronal Recycling' Hypothesis, from Monkey Brain to Human Brain. A Fyssen Foundation Symposium, Bradford, Bradford,: MIT Press
    • Dehaene, S. 8. Evolution of Human Cortical Circuits for Reading an Arithmetic: The 'Neuronal Recycling' Hypothesis, from Monkey Brain to Human Brain. A Fyssen Foundation Symposium. Edited by: Dehaene, S., Duhamel, J.-R., Hauser, M. D. and Rizzolatti, G. pp.133-157. Bradford: MIT Press.
    • Dehaene, S.1
  • 7
    • 78649936114 scopus 로고    scopus 로고
    • How and Over What Timescales Does Neural Reuse Actually Occur? Commentary on Neural re-use as a Fundamental Organizational Principle of the Brain
    • doi:10.1017/S0140525X10001184, by Michael L. Anderson
    • Donnarumma, F., Prevete, R. and Trautteur, G. 2010. How and Over What Timescales Does Neural Reuse Actually Occur? Commentary on "Neural re-use as a Fundamental Organizational Principle of the Brain" by Michael L. Anderson. Behavioral and Brain Sciences, 33(4): 272-273. (doi:10.1017/S0140525X10001184)
    • (2010) Behavioral and Brain Sciences , vol.33 , Issue.4 , pp. 272-273
    • Donnarumma, F.1    Prevete, R.2    Trautteur, G.3
  • 8
    • 4344665185 scopus 로고    scopus 로고
    • A Neural Network Model of Chemotaxis Predicts Functions of Synaptic Connections in the Nematode Caenorhabditis elegans
    • doi:10.1023/B:JCNS.0000037679.42570.d5
    • Dunn, N. A., Lockery, S. R., Pierce-Shimomura, J. T. and Conery, J. S. 2004. A Neural Network Model of Chemotaxis Predicts Functions of Synaptic Connections in the Nematode Caenorhabditis elegans. Journal of Computational Neuroscience, 17(2): 137-147. (doi:10.1023/B:JCNS.0000037679.42570.d5)
    • (2004) Journal of Computational Neuroscience , vol.17 , Issue.2 , pp. 137-147
    • Dunn, N.A.1    Lockery, S.R.2    Pierce-Shimomura, J.T.3    Conery, J.S.4
  • 9
    • 18544379461 scopus 로고    scopus 로고
    • A Unified Approach to Building and Controlling Spiking Attractor Networks
    • doi:10.1162/0899766053630332
    • Eliasmith, C. 2005. A Unified Approach to Building and Controlling Spiking Attractor Networks. Neural Computation, 17(6): 1276-1314. (doi:10.1162/0899766053630332)
    • (2005) Neural Computation , vol.17 , Issue.6 , pp. 1276-1314
    • Eliasmith, C.1
  • 11
    • 0027154319 scopus 로고
    • Approximation of Dynamical Systems by Continuous Time Recurrent Neural Networks
    • doi:10.1016/S0893-6080(05)80125-X
    • Funahashi, K. I. and Nakamura, Y. 1993. Approximation of Dynamical Systems by Continuous Time Recurrent Neural Networks. Neural Networks, 6(6): 801-806. (doi:10.1016/S0893-6080(05)80125-X)
    • (1993) Neural Networks , vol.6 , Issue.6 , pp. 801-806
    • Funahashi, K.I.1    Nakamura, Y.2
  • 12
    • 33947142057 scopus 로고    scopus 로고
    • Hippocampal Remapping and Grid Realignment in Entorhinal Cortex
    • doi:10.1038/nature05601
    • Fyhn, M., Hafting, T., Treves, A., Moser, M. B. and Moser, E. I. 2007. Hippocampal Remapping and Grid Realignment in Entorhinal Cortex. Nature, 446(7132): 190-194. (doi:10.1038/nature05601)
    • (2007) Nature , vol.446 , Issue.7132 , pp. 190-194
    • Fyhn, M.1    Hafting, T.2    Treves, A.3    Moser, M.B.4    Moser, E.I.5
  • 13
    • 58349116006 scopus 로고    scopus 로고
    • Computational Virtuality in Biological Systems
    • doi:10.1016/j.tcs.2008.09.044
    • Garzillo, C. and Trautteur, G. 2009. Computational Virtuality in Biological Systems. Theoretical Computer Science, 410(4-5): 323-331. (doi:10.1016/j.tcs.2008.09.044)
    • (2009) Theoretical Computer Science , vol.410 , Issue.4-5 , pp. 323-331
    • Garzillo, C.1    Trautteur, G.2
  • 15
    • 0001327717 scopus 로고
    • Learning and Extracting Finite State Automata with Second-Order Recurrent Neural Networks
    • doi:10.1162/neco.1992.4.3.393
    • Giles, C. L., Miller, C. B., Chen, D., Chen, H. H., Sun, G. Z. and Lee, Y. C. 1992. Learning and Extracting Finite State Automata with Second-Order Recurrent Neural Networks. Neural Computation, 4: 393-405. (doi:10.1162/neco.1992.4.3.393)
    • (1992) Neural Computation , vol.4 , pp. 393-405
    • Giles, C.L.1    Miller, C.B.2    Chen, D.3    Chen, H.H.4    Sun, G.Z.5    Lee, Y.C.6
  • 17
    • 84958985283 scopus 로고    scopus 로고
    • Learning to Learn Using Gradient Descent
    • ICANN '01: Proceedings of the International Conference on Artificial Neural Networks, London, London,: Springer-Verlag
    • Hochreiter, S., Younger, S. A. and Conwell, P. R. Learning to Learn Using Gradient Descent. ICANN '01: Proceedings of the International Conference on Artificial Neural Networks. pp.87-94. London: Springer-Verlag.
    • Hochreiter, S.1    Younger, S.A.2    Conwell, P.R.3
  • 18
    • 0021835689 scopus 로고
    • Neural Computation of Decisions in Optimization Problems
    • Hopfield, J. J. and Tank, D. W. 1985. Neural Computation of Decisions in Optimization Problems. Biological Cybernetics, 52(3): 141-152.
    • (1985) Biological Cybernetics , vol.52 , Issue.3 , pp. 141-152
    • Hopfield, J.J.1    Tank, D.W.2
  • 19
    • 0022504321 scopus 로고
    • Computing with Neural Circuits: A Model
    • doi:10.1126/science.3755256
    • Hopfield, J. J. and Tank, D. W. 1986. Computing with Neural Circuits: A Model. Science, 233: 625-633. (doi:10.1126/science.3755256)
    • (1986) Science , vol.233 , pp. 625-633
    • Hopfield, J.J.1    Tank, D.W.2
  • 20
    • 42049121440 scopus 로고    scopus 로고
    • The Shared Circuits Model (SCM): How Control, Mirroring, and Simulation Can Enable Imitation, Deliberation, and Mindreading
    • doi:10.1017/S0140525X07003123
    • Hurley, S. 2008. The Shared Circuits Model (SCM): How Control, Mirroring, and Simulation Can Enable Imitation, Deliberation, and Mindreading. Behavioral and Brain Sciences, 31(1): 1-22. (doi:10.1017/S0140525X07003123)
    • (2008) Behavioral and Brain Sciences , vol.31 , Issue.1 , pp. 1-22
    • Hurley, S.1
  • 21
    • 35048850504 scopus 로고    scopus 로고
    • Generalization in Learning Multiple Temporal Patterns Using RNNPB
    • ICONIP: International Conference on Neural Information Processing
    • Ito, M. and Tani, J. Generalization in Learning Multiple Temporal Patterns Using RNNPB. ICONIP: International Conference on Neural Information Processing. pp.592-598.
    • Ito, M.1    Tani, J.2
  • 22
    • 4344661328 scopus 로고    scopus 로고
    • Which Model to Use for Cortical Spiking Neurons?
    • doi:10.1109/TNN.2004.832719
    • Izhikevich, E. M. 2004. Which Model to Use for Cortical Spiking Neurons?. IEEE Transactions on Neural Networks, 15(5): 1063-1070. (doi:10.1109/TNN.2004.832719)
    • (2004) IEEE Transactions on Neural Networks , vol.15 , Issue.5 , pp. 1063-1070
    • Izhikevich, E.M.1
  • 23
    • 0001887517 scopus 로고    scopus 로고
    • Attractor Dynamics and Parallelism in a Connectionist Sequential Machine
    • Proceedings of the Eighth Annual Conference of the Cognitive Science Society, Hillsdale, NJ, Hillsdale, NJ,: Erlbaum
    • Jordan, M. I. Attractor Dynamics and Parallelism in a Connectionist Sequential Machine. Proceedings of the Eighth Annual Conference of the Cognitive Science Society. pp.531-546. Hillsdale, NJ: Erlbaum.
    • Jordan, M.I.1
  • 24
    • 57149100752 scopus 로고    scopus 로고
    • A Hierarchy of Time-Scales and the Brain
    • doi:10.1371/journal.pcbi.1000209
    • Kiebel, S. J., Daunizeau, J. and Friston, K. J. 2008. A Hierarchy of Time-Scales and the Brain. PLoS Computational Biology, 4(11): e1000209 (doi:10.1371/journal.pcbi.1000209)
    • (2008) PLoS Computational Biology , vol.4 , Issue.11
    • Kiebel, S.J.1    Daunizeau, J.2    Friston, K.J.3
  • 25
    • 33746909800 scopus 로고    scopus 로고
    • Design and Implementation of Multipattern Generators in Analog VLSI
    • doi:10.1109/TNN.2006.875983
    • Kier, R. J., Ames, J. C., Beer, R. D. and Harrison, R. R. 2006. Design and Implementation of Multipattern Generators in Analog VLSI. IEEE Transactions on Neural Networks, 17(4): 1025-1038. (doi:10.1109/TNN.2006.875983)
    • (2006) IEEE Transactions on Neural Networks , vol.17 , Issue.4 , pp. 1025-1038
    • Kier, R.J.1    Ames, J.C.2    Beer, R.D.3    Harrison, R.R.4
  • 26
    • 33751547740 scopus 로고    scopus 로고
    • Multiple Time Scales of Temporal Response in Pyramidal and Fast Spiking Cortical Neurons
    • doi:10.1152/jn.00453.2006
    • La Camera, G., Rauch, A., Thurbon, D., Luscher, H. R., Senn, W. and Fusi, S. 2006. Multiple Time Scales of Temporal Response in Pyramidal and Fast Spiking Cortical Neurons. Journal of Neurophysiology, 96(6): 3448-3464. (doi:10.1152/jn.00453.2006)
    • (2006) Journal of Neurophysiology , vol.96 , Issue.6 , pp. 3448-3464
    • La Camera, G.1    Rauch, A.2    Thurbon, D.3    Luscher, H.R.4    Senn, W.5    Fusi, S.6
  • 27
    • 0003123651 scopus 로고    scopus 로고
    • Why Have Dendrites? A Computational Perspective
    • In: Stuart G., Spruston N., Häusser M., editors Oxford, Oxford,: Oxford University Press
    • Mel, B. W. 1999. "Why Have Dendrites? A Computational Perspective". In Dendrites, Edited by: Stuart, G., Spruston, N. and Häusser, M. 271-289. Oxford: Oxford University Press.
    • (1999) Dendrites , pp. 271-289
    • Mel, B.W.1
  • 28
    • 40749140305 scopus 로고    scopus 로고
    • Learning Multiple Goal-Directed Actions Through Self-organization of a Dynamic Neural Network Model: A Humanoid Robot Experiment
    • doi:10.1177/1059712308089185
    • Nishimoto, R., Namikawa, J. and Tani, J. 2008. Learning Multiple Goal-Directed Actions Through Self-organization of a Dynamic Neural Network Model: A Humanoid Robot Experiment. Adaptive Behavior, 16(2-3): 166-181. (doi:10.1177/1059712308089185)
    • (2008) Adaptive Behavior , vol.16 , Issue.2-3 , pp. 166-181
    • Nishimoto, R.1    Namikawa, J.2    Tani, J.3
  • 29
    • 0006310873 scopus 로고
    • Towards Instructable Connectionist Systems
    • In: Sun R., Bookman L., editors Needham, MA, Needham, MA,: Kluwer Academic Publishers
    • Noelle, D. C. and Cottrell, G. W. 1995. "Towards Instructable Connectionist Systems". In Computational Architectures Integrating Neural and Symbolic Processes, Edited by: Sun, R. and Bookman, L. 187-221. Needham, MA: Kluwer Academic Publishers.
    • (1995) Computational Architectures Integrating Neural and Symbolic Processes , pp. 187-221
    • Noelle, D.C.1    Cottrell, G.W.2
  • 30
    • 77955889533 scopus 로고    scopus 로고
    • Computational Models of Cognitive Control
    • doi:10.1016/j.conb.2010.01.008
    • O'Reilly, R. C., Herd, S. A. and Pauli, W. M. 2010. Computational Models of Cognitive Control. Current Opinion in Neurobiology, 20(2): 257-261. (doi:10.1016/j.conb.2010.01.008)
    • (2010) Current Opinion in Neurobiology , vol.20 , Issue.2 , pp. 257-261
    • O'Reilly, R.C.1    Herd, S.A.2    Pauli, W.M.3
  • 31
    • 0036690078 scopus 로고    scopus 로고
    • Schema Design and Implementation of the Grasp-Related Mirror Neuron System
    • doi:10.1007/s00422-002-0318-1
    • Oztop, E. and Arbib, M. 2002. Schema Design and Implementation of the Grasp-Related Mirror Neuron System. Biological Cybernetics, 87(2): 116-140. (doi:10.1007/s00422-002-0318-1)
    • (2002) Biological Cybernetics , vol.87 , Issue.2 , pp. 116-140
    • Oztop, E.1    Arbib, M.2
  • 32
    • 9144259348 scopus 로고    scopus 로고
    • Motor Primitive and Sequence Self-organization in a Hierarchical Recurrent Neural Network
    • doi:10.1016/j.neunet.2004.08.005
    • Paine, R. W. and Tani, J. 2004. Motor Primitive and Sequence Self-organization in a Hierarchical Recurrent Neural Network. Neural Networks, 17(8-9): 1291-1309. (doi:10.1016/j.neunet.2004.08.005)
    • (2004) Neural Networks , vol.17 , Issue.8-9 , pp. 1291-1309
    • Paine, R.W.1    Tani, J.2
  • 33
    • 5444262001 scopus 로고    scopus 로고
    • Robustness of Multiplicative Processes in Auditory Spatial Tuning
    • doi:10.1523/JNEUROSCI.2924-04.2004
    • Pena, J. and Konishi, M. 2004. Robustness of Multiplicative Processes in Auditory Spatial Tuning. The Journal of Neuroscience, 24(40): 8907-8910. (doi:10.1523/JNEUROSCI.2924-04.2004)
    • (2004) The Journal of Neuroscience , vol.24 , Issue.40 , pp. 8907-8910
    • Pena, J.1    Konishi, M.2
  • 34
    • 80053456319 scopus 로고    scopus 로고
    • Time Scale Hierarchies in the Functional Organization of Complex Behaviors
    • doi:10.1371/journal.pcbi.1002198
    • Perdikis, D., Huys, R. and Jirsa, V. K. 2011. Time Scale Hierarchies in the Functional Organization of Complex Behaviors. PLoS Computational Biology, 7(9): e1002198 (doi:10.1371/journal.pcbi.1002198)
    • (2011) PLoS Computational Biology , vol.7 , Issue.9
    • Perdikis, D.1    Huys, R.2    Jirsa, V.K.3
  • 36
    • 0036083081 scopus 로고    scopus 로고
    • Adaptive Behavior with Fixed Weights in RNN: An Overview
    • IJCNN '02. Proceedings of the 2002 International Joint Conference on Neural Networks, 2002
    • Prokhorov, D., Feldkarnp, L. and Tyukin, I. Adaptive Behavior with Fixed Weights in RNN: An Overview. IJCNN '02. Proceedings of the 2002 International Joint Conference on Neural Networks, 2002. Vol. 3, pp.2018-2022.
    • , vol.3 , pp. 2018-2022
    • Prokhorov, D.1    Feldkarnp, L.2    Tyukin, I.3
  • 39
    • 0029958385 scopus 로고    scopus 로고
    • A Model of Multiplicative Neural Responses in Parietal Cortex
    • doi:10.1073/pnas.93.21.11956
    • Salinas, E. and Abbott, L. 1996. A Model of Multiplicative Neural Responses in Parietal Cortex. PNAS, 93: 11956-11961. (doi:10.1073/pnas.93.21.11956)
    • (1996) PNAS , vol.93 , pp. 11956-11961
    • Salinas, E.1    Abbott, L.2
  • 40
    • 33646570507 scopus 로고    scopus 로고
    • Parallel and Distributed Neural Models of the Ideomotor Principle: An Investigation of Imitative Cortical Pathways
    • doi:10.1016/j.neunet.2006.02.003
    • Sauser, E. and Billard, A. 2006. Parallel and Distributed Neural Models of the Ideomotor Principle: An Investigation of Imitative Cortical Pathways. Neural Networks, 19(3): 285-298. (doi:10.1016/j.neunet.2006.02.003)
    • (2006) Neural Networks , vol.19 , Issue.3 , pp. 285-298
    • Sauser, E.1    Billard, A.2
  • 41
    • 0346377064 scopus 로고
    • Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks
    • doi:10.1162/neco.1992.4.1.131
    • Schmidhuber, J. 1992. Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks. Neural Computation, 4(1): 131-139. (doi:10.1162/neco.1992.4.1.131)
    • (1992) Neural Computation , vol.4 , Issue.1 , pp. 131-139
    • Schmidhuber, J.1
  • 42
    • 0001640273 scopus 로고    scopus 로고
    • An Instructable Connectionist/Control Architecture: Using Rule-Based Instructions to Accomplish Connectionist Learning in a Human Time Scale
    • In: Lehn K. V., editors Architectures for Intelligence: The 22nd Carnegie Mellon Symposium on Cognition, Hillsdale, NJ, Hillsdale, NJ,: Erlbaum
    • Schneider, W. and Oliver, W. L. An Instructable Connectionist/Control Architecture: Using Rule-Based Instructions to Accomplish Connectionist Learning in a Human Time Scale. Architectures for Intelligence: The 22nd Carnegie Mellon Symposium on Cognition. Edited by: Lehn, K. V. pp.113-145. Hillsdale, NJ: Erlbaum.
    • Schneider, W.1    Oliver, W.L.2
  • 45
    • 0025521210 scopus 로고
    • BoltzCONS: Dynamic Symbol Structures in a Connectionist Network
    • doi:10.1016/0004-3702(90)90003-I
    • Touretzky, D. S. 1990. BoltzCONS: Dynamic Symbol Structures in a Connectionist Network. Artificial Intelligence, 46(1-2): 5-46. (doi:10.1016/0004-3702(90)90003-I)
    • (1990) Artificial Intelligence , vol.46 , Issue.1-2 , pp. 5-46
    • Touretzky, D.S.1
  • 46
    • 57149090913 scopus 로고    scopus 로고
    • Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment
    • doi:10.1371/journal.pcbi.1000220
    • Yamashita, Y. and Tani, J. 2008. Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment. PLoS Computational Biology, 4(11): e1000220 (doi:10.1371/journal.pcbi.1000220)
    • (2008) PLoS Computational Biology , vol.4 , Issue.11
    • Yamashita, Y.1    Tani, J.2


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