nach oben

Journal of Computational Neuroscience

Erschienen in:

10.10.2016

Hierarchical winner-take-all particle swarm optimization social network for neural model fitting

verfasst von: Brandon S. Coventry, Aravindakshan Parthasarathy, Alexandra L. Sommer, Edward L. Bartlett

Erschienen in: Journal of Computational Neuroscience | Ausgabe 1/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models.

Vorheriger Artikel Emergence of gamma motor activity in an artificial neural network model of the corticospinal system

Nächster Artikel Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Ackley, D. H. (1987). A connectionist machine for genetic hillclimbing, 1st edn. Boston: Kluwer Academic Publishers.CrossRef

Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi an open source software for exploring and manipulating networks. In International AAAI Conference on Weblogs and Social Media (pp. 1–2).

Birge, B. (2006). Particle swarm optimization toolbox. http://www.mathworks.com/matlabcentral/fileexchange/7506-particle-swarm-optimization-toolbox.

Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization Overview and conceptual comparison. ACM Computing Surveys, 35(3), 268–308.CrossRef

Carnevale, N. T., & Hines, M. L. (2006). The NEURON Book Vol. 30: Cambridge University Press.

Clerc, M., & Kennedy, J. (2002). The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58–73.CrossRef

Coello Coello, C., & Lechuga, M. (2002). MOPSO: a proposal for multiple objective particle swarm optimization. In Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No.02TH8600), (Vol. 2 pp. 1051–1056): IEEE.

Collins, J. J., & Chow, C. C. (1998). It’s a small world. Nature, 393(6684), 409–10.CrossRefPubMed

Destexhe, A., Rudolph, M., Fellous, J. M., & Sejnowski, T. J. (2001). Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience, 107(1), 13–24.CrossRefPubMedPubMedCentral

Devroye, L. (1986). Non-uniform random variate generation, 1st edn. New York: Springer.CrossRef

Elbeltagi, E., Hegazy, T., & Grierson, D. (2005). Comparison among five evolutionary-based optimization algorithms. Advanced Engineering Informatics, 19(1), 43–53.CrossRef

Griewank, A. O. (1981). Generalized descent for global optimization. Journal of Optimization Theory and Applications, 34(1), 11–39.CrossRef

Herrmann, B., Parthasarathy, A., Han, E. X., Obleser, J., & Bartlett, E. L. (2015). Sensitivity of rat inferior colliculus neurons to frequency distributions. Journal of Neurophysiology, 114(5), 2941–54.PubMedPubMedCentral

Hu, X., & Eberhart, R. (2002). Multiobjective optimization using dynamic neighborhood particle swarm optimization. In Proceedings of the Congress on Evolutionary Computation. CEC’02 (Cat. No.02TH8600) (2002), (Vol. 2 pp. 1677–1681): IEEE.

Itti, L., & Koch, C. (2001). Computational modelling of visual attention. Nature Reviews Neuroscience, 2(3), 194–203.CrossRefPubMed

Jamil, M., & Yang, X. S. (2013). A literature survey of benchmark functions for global optimisation problems. International Journal of Mathematical Modelling and Numerical Optimisation, 4(2), 150.CrossRef

Kelly, J. B., & Caspary, D. M. (2005). Pharmacology of the inferior colliculus. In Winer, J. A., & Schreiner, C. E. (Eds.) The Inferior Colliculus (pp. 248–281). New York: Springer.

Kennedy, J. (1999). Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) (pp. 1931–1938): IEEE.

Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95 - International Conference on Neural Networks, (Vol. 4 pp. 1942–1948).

Kennedy, J., & Mendes, R. (2002). particle swarm performance. In Proceedings of the Population structure Congress on Evolutionary Computation. CEC’02 (Cat. No.02TH8600) (2002), (Vol. 2 pp. 1671–1676): IEEE.

Kentzoglanakis, K., & Poole, M. (2012). A swarm intelligence framework for reconstructing gene networks Searching for biologically plausible architectures. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9 (2), 358–371.CrossRefPubMed

Kim, J. -Y., Mun, K. -J., Kim, H. -S., & Park, J. H. (2011). Optimal power system operation using parallel processing system and PSO algorithm. International Journal of Electrical Power & Energy Systems, 33(8), 1457–1461.CrossRef

Lampl, I., Ferster, D., Poggio, T., & Riesenhuber, M. (2004). Intracellular measurements of spatial integration and the MAX operation in complex cells of the cat primary visual cortex. Journal of neurophysiology, 92 (5), 2704–2713.CrossRefPubMed

Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, 10(3), 281–295.CrossRef

Mendes, R., Kennedy, J., & Neves, J. (2004). The fully informed particle swarm: simpler, Maybe better. IEEE Transactions on Evolutionary Computation, 8(3), 204–210.CrossRef

Mikki, S. M., & Kishk, A. A. (2008). Particle swarm optimization a physics-based approach. Synthesis Lectures on Computational Electromagnetics, 3(1), 1–103.CrossRef

Mühlenbein, H., Schomisch, M., & Born, J. (1991). The parallel genetic algorithm as function optimizer. Parallel Computing, 17(6-7), 619–632.CrossRef

Oleksiak, A., Klink, P. C., Postma, A., Van der Ham, I. J. M., Lankheet, M. J., & Van Wezel, R. J. A. (2011). Spatial summation in macaque parietal area 7a follows a winner-take-all rule. Journal of neurophysiology, 105(3), 1150–1158.CrossRefPubMed

Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization. Swarm Intelligence, 1(1), 33–57.CrossRef

Rabang, C. F., Parthasarathy, A., Venkataraman, Y., Fisher, Z. L., Gardner, S. M., & Bartlett, E. L. (2012). A computational model of inferior colliculus responses to amplitude modulated sounds in young and aged rats. Frontiers in Neural Circuits 6.

Rosenbrock, H. H. (1960). An automatic method for finding the greatest or least value of a function. The Computer Journal, 3(3), 175–184.CrossRef

Saldaña, E., Aparicio, M. A., Fuentes-Santamaría, V., & Berrebi, A. S. (2009). Connections of the superior paraolivary nucleus of the rat: projections to the inferior colliculus. Neuroscience, 163(1), 372–387.CrossRefPubMedPubMedCentral

Salerno, J. (1997). Using the particle swarm optimization technique to train a recurrent neural model. In Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence (pp. 45–49): IEEE Computer Society.

Salzman, C., & Newsome, W. (1994). Neural mechanisms for forming a perceptual decision. Science, 264 (5156), 231–237.CrossRefPubMed

Shang, Y. -W., & Qiu, Y. -H. (2006). A note on the extended rosenbrock function. Evoluationary Computation, 14(1), 119– 126.CrossRef

Shi, L., Niu, X., & Wan, H. (2015). Effect of the small-world structure on encoding performance in the primary visual cortex: an electrophysiological and modeling analysis. Journal of Comparative Physiology A, 201(5), 471–483.CrossRef

Shi, Y., & Eberhart, R. (1998). A modified particle swarm optimizer. 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360) (1998), pp. 69– 73.

Styblinski, M., & Tang, T. -S. (1990). Experiments in nonconvex optimization Stochastic approximation with function smoothing and simulated annealing. Neural Networks, 3(4), 467–483.CrossRef

Tiilikainen, J., Bosund, V., Mattila, M., Hakkarainen, T., Sormunen, J., & Lipsanen, H. (2007). Fitness function and nonunique solutions in x-ray reflectivity curve fitting: crosserror between surface roughness and mass density. Journal of Physics D: Applied Physics, 40(14), 4259–4263.CrossRef

Van den Heuvel, M. P., & Sporns, O. (2011). Rich-club organization of the human connectome. Journal of Neuroscience, 31(44), 15775–15786.CrossRefPubMed

Van Geit, W., Achard, P., & De Schutter, E. (2007). Neurofitter: a parameter tuning package for a wide range of electrophysiological neuron models. Frontiers in neuroinformatics Frontiers in neuroinformatics 1.

VandenBergh, F., & Engelbrecht, A. (2004). A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 225–239.CrossRef

Vayrynen, E., Noponen, K., Vipin, A., Yuan, T. X., Al-Nashash, H., Kortelainen, J., & All, A. (2016). Automatic parametrization of somatosensory evoked potentials with chirp modeling. IEEE Transactions on Neural Systems and Rehabilitation Engineering 4320, c, 1–1.

Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ’small-world’ networks. Nature, 393(6684), 440–2.CrossRefPubMed

Wolpert, D., & Macready, W. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82.CrossRef

Zhang, C., & Shao, H. (2000). Particle swarm optimisation for evolving artificial neural network. SMC 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. Cybernetics Evolving to Systems, Humans, Organizations, and their Complex Interactions (Cat. No.00CH37166) 4, 2487–2490.

Titel: Hierarchical winner-take-all particle swarm optimization social network for neural model fitting
verfasst von: Brandon S. Coventry
Aravindakshan Parthasarathy
Alexandra L. Sommer
Edward L. Bartlett
Publikationsdatum: 10.10.2016
Verlag: Springer US
Erschienen in: Journal of Computational Neuroscience / Ausgabe 1/2017
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI: https://doi.org/10.1007/s10827-016-0628-2

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 1/2017

Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience

Modeling the differentiation of A- and C-type baroreceptor firing patterns

Artefactual origin of biphasic cortical spike-LFP correlation

Emergence of gamma motor activity in an artificial neural network model of the corticospinal system

Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks

Optimal nonlinear cue integration for sound localization