National Repository of Grey Literature 2 records found  Search took 0.00 seconds. 
Coding of pheromone signal by olfactory receptor neurons in Agrotis ipsilon
Kováčová, Kristýna ; Košťál, Lubomír (advisor) ; Pokora, Ondřej (referee)
i Abstract The main objective of the thesis is to describe differences in the activity of male A. ipsilon olfactory receptor neurons (ORNs) when stimulated by different temporal dynamics of the concentration of the conspecific female pheromone. First, under the artificial situation of constant pulse stimulation, and second, with a fluctuating signal resembling the natural situation. For this purpose, the experimental data were collected in the collaborating laboratory (Dr. P. Lucas, INRAe, Versailles, France) by employing a novel olfactometer system that enables precise temporal control of the pheromone delivery to individual sensilla. Using the R programming language, we analyzed various descriptors of the response reliability, randomness, and variability, as well as the information content of the evoked activity. The results are interpreted in the context of the classical efficient coding hypothesis, which states that sensory neurons are evolutionarily adapted to natural stimuli. The main finding is that although the response variability is widely spread across the ORN population, sometimes with no visible difference between the constant and fluctuating stimulation types, the fluctuating stimulus is usually encoded with systematically higher reliability, as revealed by the inspection of individual ORNs....
Information-theoretic properties of selected stochastic neuronal models
Bárta, Tomáš ; Košťál, Lubomír (advisor) ; Pokora, Ondřej (referee)
According to the classical efficient-coding hypothesis, biological neurons are naturally adapted to transmit and process information about the stimulus in an optimal way. Shannon's information theory provides methods to compute the fundamental limits on maximal information transfer by a general system. Understanding how these limits differ between different classes of neurons may help us to better understand how sensory and other information is processed in the brain. In this work we provide a brief review of information theory and its use in computational neuroscience. We use mathematical models of neuronal cells with stochastic input that realistically reproduce different activity patterns observed in real cortical neurons. By employing the neuronal input-output pro- perties we calculate several key information-theoretic characteristics, including the information capacity. In order to determine the information capacity we propose an iterative extension of the Blahut-Arimoto algorithm that generalizes to continuous input channels subjected to constraints. Finally, we compare the information optimality conditions among different models and parameter sets. 1

Interested in being notified about new results for this query?
Subscribe to the RSS feed.