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.

Practical Bayesian Inference in Neuroscience: Or How I Learned To Stop Worrying and Embrace the Distribution

“…The inferior colliculus (IC) is the major integrative center of the auditory pathway, receiving excitatory inputs from ventral and dorsal cochlear nuclei, excitatory and inhibitory inputs from the lateral and medial superior olivary complex(Kelly and Caspary, 2005) and inhibitory inputs from superior paraolivary nucleus and the dorsal and ventral nuclei of the lateral lemniscus(Cant and Benson, 2006; Loftus et al, 2010). The IC encodes auditory information through hierarchical processing of input synaptics with local IC circuitry(Caspary et al, 2002; Rabang et al, 2012; Grimsley et al, 2013; Coventry et al, 2017). Age-related changes in auditory processing primarily arise as deficits in temporal processing(Frisina and Frisina, 1997; Parthasarathy et al, 2010; Parthasarathy and Bartlett, 2012; Herrmann et al, 2017).…”

Spatially specific, closed-loop infrared thalamocortical deep brain stimulation

“…As single unit thalamocortical recordings elicited from INS have largely been unexplored, previous knowledge cannot be adequately constructed into a highly informative prior. However, our previous experience in thalamus and cortical recordings(4–10), the central auditory pathway (11–13), and in INS parameter selection(14, 15) gives prior information on potential variances of firing rate in cortex from thalamic stimulation. As such, we chose moderately informative distributions (see section 3) so as to not unduly influence the posterior and let the observed data fully inform the posterior.…”