Modeling of an Insect Proprioceptor System based on Different Neuron Response Times

Abstract

This paper analyzes neuronal spiking signals from the Desert Locust Femorotibial Chordotonal Organ (FeCO). The data comes from records of the insect neuronal response due to external stimulation. We measured the Inter-Spike Interval (ISI) and calculated Transfer Entropy for investigate different FeCO responses. ISI is a technique that measures the time between two spikes; and transfer entropy is a theoretical information measure used to find dependencies and causal relationships. We also use survival functions to assemble FeCO models. Furthermore, this work uses and compares results of two approaches, one with transfer entropy and other with ISI measures. The results indicate evidence to support the existence of more than one type of FeCO neuron.

Resilience and vulnerability of power distribution systems: approaches for dynamic features and extreme weather scenarios

“…The assumption of the time-to-failure as a random variable is also used to model different phenomena, as survival and recurrency time of patiants with a specific disease (MAZUCHELI; ACHCAR, 2011), and the time-to-occurrence of biological processess as neuronal spiking (LIMA et al, 2016), this type of application is called time-to-event, or Life Time Data, analysis (HOSMER; LEMESHOW; MAY, 2008). In the case of time-tofailure, the model is called as Reliability Function R(t), which describes the probability of a correct functioning during a time interval t. It is represented as follows:…”

Delayed Transfer Entropy applied to Big Data

“…The idea behind this study emerged as a necessity brought by other Signal Processing Laboratory (LPS) projects. In particular, a currently going project on insect motor neurons network (SANTOS;MACIEL;NEWLAND, 2017;LIMA et al, 2016;ENDO et al, 2015) had a DTE performance demand to increase analysis confidence. Other LPS projects such as multi-scenarios Monte Carlo simulations (BESSANI et al, 2016) and optimization of large-scale systems reconfiguration (CAMILLO et al, 2016) also would be benefited from cluster configuration and usage know-how.…”