A new approach to detect the coding rule of the cortical spiking model in the information transmission. (March 2018)

• Record Type: Journal Article
• Authors: Nazari, Soheila; Faez, Karim; Janahmadi, Mahyar
• Is Part Of: Neural networks. Volume 99(2018)
• Page Start: 68
• Page End: 78
• Publication Date: 2018-03
• Subjects: Cortical spiking network -- AMPA and GABA current -- PSWF -- Power spectrum of LFP -- Information coding; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2017.12.009
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Investigation of the role of the local field potential (LFP) fluctuations in encoding the received sensory information by the nervous system remains largely unknown. On the other hand, transmission of these translation rules in information transmission between the structure of sensory stimuli and the cortical oscillations to the bio-inspired artificial neural networks operating at the efficiency of the nervous system is still a vague puzzle. In order to move towards this important goal, computational neuroscience tools can be useful so, we simulated a large-scale network of excitatory and inhibitory spiking neurons with synaptic connections consisting of AMPA and GABA currents as a model of cortical populations. Spiking network was equipped with spike-based unsupervised weight optimization based on the dynamical behavior of the excitatory (AMPA) and inhibitory (GABA) synapses using Spike Timing Dependent Plasticity (STDP) on the MNIST benchmark and we specified how the generated LFP by the network contained information about input patterns. The main result of this article is that the calculated coefficients of Prolate spheroidal wave functions (PSWF) from the input pattern with mean square error (MSE) criterion and power spectrum of LFP with maximum correntropy criterion (MCC) are equal. The more important result is that 82.3% of PSWF coefficients are the same as the connecting weights of the cortical neurons to the classifying neurons after the completion of the training process. Higher compliance percentage of coefficients with synaptic weights (82.3%) gives the expectance us that this coding rule will be able to extend to biological systems. Eventually, we introduced the cortical spiking network as an information channel, which transmits the information of the input pattern in the form of PSWF coefficients to the power spectrum of the output generated LFP. Highlights: LFP fluctuations and digits contain considerable mutual information. LFP power spectrum is a proper feature in the classification of the input digits. We introduce the spheroidal information coding in the cortical spiking network. Synaptic weights of the cortical network have high matching with PSWF coefficients.