Analysis of neurophysiological signals from the proprioceptor system of insects

• Ano de defesa: 2016
• Autor(a) principal: Lima, Daniel Rodrigues de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Delayed transfer entropy; Desert locusts; Gafanhotos; Neurônios sensores; Propriocepção; Proprioception; Sensory neurons; Simulação; Simulation; Spiking rates; Taxas de spiking; Transferência de entropia atrasada
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/18/18153/tde-18012017-112105/

Proprioception is the ability to sense body position necessary for coordinate precise movements. Despite the low complexity of insect neuronal systems, scientists are studying their motor control system. Researchers performed experiments in desert locusts by stimulating their apodeme and recording the neuronal response. Previous studies reported variations in neuronal spiking rates related to acceleration, velocity and position sensitivity. Their results led us to the assumption that either there are different kinds of sensory neurons, or there is only one type of neuron responding to various Physical quantities. Therefore, this research intends to investigate the different spiking rates. We also want to study the influence of apodemes excitations in sensory neurons with information theoretical measures. However, the way signals were recorded does not allow the calculation of delayed transfer entropy (DTE) between sensory neurons. To solve that problem we propose a method to estimate parameters of connections in such scenarios. Our analysis will model the time spent between spikes with survival functions. The influence of excitation in the neuronal response will be analyzed with DTE, which will also be used to validate the methods of simulation. Results show that there is evidence to support the assumption of different spiking rates among sensory neurons. DTE suggests the existence of intermediate processing nodes between excitation and some sensory neurons. A further simulation joining the methods proposed and neuronal signals showed that models considering intermediate pathways present a good fit to the data. We suggest that the different responses of sensory neurons are not due to various types of neurons, but to a preprocessing layer.