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Neuronal coding and metabolic cost of information
Bárta, Tomáš ; Košťál, Lubomír (advisor) ; Martinez, Dominique (referee) ; Nowotny, Thomas (referee)
For most neurons, the information the neuron passes on is contained within the times of sending out electrical pulses - so-called action potentials. It is still not fully understood how to read this "neural code". The efficient coding hypothesis proposes that due to evolutionary pressures sensory systems evolved to transmit and process information in the most efficient way possible. However, the notion of efficiency seems to be different in different sensory systems. Cortical neurons keep their firing rates low to minimize metabolic expenses. So do insect olfactory receptor neurons (ORNs, the first layer of the olfactory system). Neurons in the insect antennal lobe (the second layer of the olfactory system), on the other hand fully use the space of possible firing rates to encode the maximum information about the odor. In my thesis, I studied how can single cortical neurons and their populations transmit and process information, while keeping metabolic expenses low, and also how the insect olfactory system encodes information about odors encountered in the air. In the part of my thesis about metabolically efficient information transmission I focused mainly on the role of inhibitory neurons in efficient information transmission. Through mathematical analysis and Monte Carlo simulations of spiking...
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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....
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Modeling of Binaural Hearing.
Tóth, Peter ; Maršálek, Petr (advisor) ; Košťál, Lubomír (referee) ; Hromádka, Tomáš (referee)
The central theme of this thesis is a description of information processing in the sound localization circuit of the auditory pathway. The focus is on principal neurons of the medial superior olive (MSO), the first major convergence point for binaural information. Selected properties and relations of MSO neurons are derived and expressed through models. In the thesis we present three modeling studies. The first one clarifies a relation- ship between biophysical parameters of the MSO neuron and its ability to detect coincidental spikes from the left and the right ear. The second study describes the statistical behavior of spike trains on the input and output of the MSO neuron. In the third work, we studied how interaural coherence could guide localization of sound sources in complex listening situations with multiple sound sources in reverberant environments. The main results are analytical and numerical models describing the aforemen- tioned relations and behaviors. Secondary results include that inhibitory input to the MSO neuron narrows and shifts the time range of coincidence detection, that ergodic assumption from statistical physics and circular statistics are beneficial in the description of spike trains in the auditory pathway, and that interaural level difference of parts of the signal with...
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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
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