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

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.

[1]  Xin-She Yang,et al.  A literature survey of benchmark functions for global optimisation problems , 2013, Int. J. Math. Model. Numer. Optimisation.

[2]  Yun Shang,et al.  A Note on the Extended Rosenbrock Function , 2006 .

[3]  Edward L. Bartlett,et al.  Sensitivity of rat inferior colliculus neurons to frequency distributions. , 2015, Journal of neurophysiology.

[4]  Ahmed A. Kishk,et al.  Particle Swarm Optimization: A Physics-Based Approach , 2008, Particle Swarm Optimization.

[5]  Kyriakos Kentzoglanakis,et al.  A Swarm Intelligence Framework for Reconstructing Gene Networks: Searching for Biologically Plausible Architectures , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[6]  Michael L. Hines,et al.  The NEURON Book , 2006 .

[7]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[8]  Yu Li,et al.  Particle swarm optimisation for evolving artificial neural network , 2000, 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.0.

[9]  H. H. Rosenbrock,et al.  An Automatic Method for Finding the Greatest or Least Value of a Function , 1960, Comput. J..

[10]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[11]  June Ho Park,et al.  Optimal power system operation using parallel processing system and PSO algorithm , 2011 .

[12]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[13]  J. Kelly,et al.  Pharmacology of the Inferior Colliculus , 2005 .

[14]  O. Sporns,et al.  Rich-Club Organization of the Human Connectome , 2011, The Journal of Neuroscience.

[15]  Erik De Schutter,et al.  Neurofitter: a parameter tuning package for a wide range of electrophysiological neuron models , 2007, BMC Neuroscience.

[16]  A. Berrebi,et al.  Connections of the superior paraolivary nucleus of the rat: projections to the inferior colliculus , 2009, Neuroscience.

[17]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[18]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[19]  Donald E. Grierson,et al.  Comparison among five evolutionary-based optimization algorithms , 2005, Adv. Eng. Informatics.

[20]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[21]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[22]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[23]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[24]  Hasan Al-Nashash,et al.  Automatic Parametrization of Somatosensory Evoked Potentials With Chirp Modeling , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[25]  Brian Birge,et al.  PSOt - a particle swarm optimization toolbox for use with Matlab , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[26]  Harri Lipsanen,et al.  Fitness function and nonunique solutions in x-ray reflectivity curve fitting: crosserror between surface roughness and mass density , 2007 .

[27]  M. A. Styblinski,et al.  Experiments in nonconvex optimization: Stochastic approximation with function smoothing and simulated annealing , 1990, Neural Networks.

[28]  Li Shi,et al.  Effect of the small-world structure on encoding performance in the primary visual cortex: an electrophysiological and modeling analysis , 2015, Journal of Comparative Physiology A.

[29]  T. Sejnowski,et al.  Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons , 2001, Neuroscience.

[30]  Heinz Mühlenbein,et al.  The parallel genetic algorithm as function optimizer , 1991, Parallel Comput..

[31]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[32]  A. Griewank.  Generalized descent for global optimization , 1981 .

[33]  J. Salerno,et al.  Using the particle swarm optimization technique to train a recurrent neural model , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.

[34]  C D Salzman,et al.  Neural mechanisms for forming a perceptual decision. , 1994, Science.

[35]  L. Devroye.  Non-Uniform Random Variate Generation , 1986 .

[36]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[37]  Carson C. Chow,et al.  It's a small world , 1998, Nature.

[38]  Russell C. Eberhart,et al.  Multiobjective optimization using dynamic neighborhood particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[39]  D. Ackley.  A connectionist machine for genetic hillclimbing , 1987 .

[40]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[41]  A. Postma,et al.  Spatial summation in macaque parietal area 7a follows a winner-take-all rule. , 2011, Journal of neurophysiology.

[42]  Mathieu Bastian,et al.  Gephi: An Open Source Software for Exploring and Manipulating Networks , 2009, ICWSM.

[43]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[44]  Karl F. Stock,et al.  A COMPUTATIONAL MODEL , 2011 .

[45]  James Kennedy,et al.  Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[46]  Stephanie M. Gardner,et al.  A Computational Model of Inferior Colliculus Responses to Amplitude Modulated Sounds in Young and Aged Rats , 2012, Front. Neural Circuits.

[47]  Nikolaos V. Kantartzis,et al.  Modern EMC Analysis Techniques Volume I: Time-Domain Computational Schemes , 2008, Modern EMC Analysis Techniques Volume I.

[48]  Tomaso Poggio,et al.  Intracellular measurements of spatial integration and the MAX operation in complex cells of the cat primary visual cortex. , 2004, Journal of neurophysiology.