title: Hierarchical winner-take-all particle swarm optimization social network for neural model fitting.
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.
authors.affiliation: Weldon School of Biomedical Engineering, Purdue University, Purdue, USA.
authors.forename: Brandon S
authors.initials: BS
authors.lastname: Coventry
authors.affiliation: Weldon School of Biomedical Engineering, Purdue University, Purdue, USA.
authors.forename: Aravindakshan
authors.initials: A
authors.lastname: Parthasarathy
authors.affiliation: Weldon School of Biomedical Engineering, Purdue University, Purdue, USA.
authors.forename: Alexandra L
authors.initials: AL
authors.lastname: Sommer
authors.affiliation: Weldon School of Biomedical Engineering and the Department of Biological Sciences, Purdue University, Purdue, USA. ebartle@purdue.edu.
authors.forename: Edward L
authors.initials: EL
authors.lastname: Bartlett
mesh_terms: D000161:Acoustic Stimulation; D000465:Algorithms; D003198:Computer Simulation; D008959:Models, Neurological; D009474:Neurons; D012944:Social Support
keywords: Biological neural networks; Evolutionary computation; Model optimization; Particle swarm optimization