Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-154
Main Title: Dynamics and representation in the primary visual cortex
Author(s): Adorjan, Peter
Advisor(s): Obermayer, Klaus
Granting Institution: Technische Universität Berlin, Fakultät IV - Elektrotechnik und Informatik
Type: Doctoral Thesis
Language: English
Language Code: en
Abstract: Bestehende Modelle für Orientierungsselektivität einfacher Zellen in V1 nehmen an, dass durch die Muster der axonalen Projektionen vom Kniekörper zu V1 bereits eine schwache Orientierungsselektivität gegeben ist, die dann intrakortikal verstärkt wird. Als Alternative wurde ein mit experimentellenDaten konsistentes Modell zu der Hypothese konstruiert, dass beides, Verstärkung und Initiierung orientierungsselektiver neuronaler Antworten, ein intrakortikales Phänomen ist. Die Möglichkeit eines intrakortikalen Ursprungs der Orientierungsselektivität konnte mit der Annahme eines anisotropen Verschaltungsmusters der intrakortikalen Verbindungen demonstriert werden. Die durch eine Simulation des Modells prognostizierten Dynamiken der Aktionspotentiale und des Membranpotentials einfacher Zellen in V1 sind sogar konsistent mit experimentellen Daten, die zuvor widersprüchlich schienen. Um die Mechanismen und die Funktion von Kontrastadaption in V1 zu klären, wurde ein Modell entwickelt, mit dem Kontrastadaption über eine änderung der Freisetzungswahrscheinlichkeit von Transmittermolekülen an den Synapsen zwischen dem Kniekörper und V1 erklärt werden kann. Die im Modell verwendete kontrastabhängige Adaption dieser Freisetzungswahrscheinlichkeiten wurde abgeleitet von der funktionalen Forderung, dass einfache Zellen in V1 ihre Antworteigenschaften hinsichtlich maximaler Informationsübertragung optimieren sollen. Die vom Modell vorhergesagten neuronalen Antworten stimmen überraschend gut mit experimentellen Daten überein und legen deshalb nahe, dass Nervenzellen sich an wechselnde visuelle Umgebungen schnell anpassen und so als adaptive informationsverarbeitende Einheiten betrachtet werden können. Um die Frage der Funktion einer kortikalen Verarbeitung visueller Reize in V1 zu beantworten, wurde in übereinstimmung mit experimentellen Daten postuliert, dass die stark rückgekoppelten kortikalen Netze effektiv eine nichtlineare Abbildung implementieren, bei der einfache Zellen mit unterschiedlicher Orientierungsselektivität, jedoch mit dem selben rezeptiven Feld, miteinander im Wettbewerb stehen. Mit der Annahme einer solchen Nichtlinearität konnte gezeigt werden, dass visuelle Informationen schnell und robust weitergeleitet werden können, wenn der induzierte Wettbewerb zwischen einfachen Zellen nicht statisch sondern dynamisch ist. Als neuronale Mechanismen für diese Modulierung des intrakortikalen Wettbewerbs'' wurden synaptische Mechanismen vorgeschlagen.
We investigate the processing and representation of static visual patterns in the early visual system of mammals (especially cats and primates). We demonstrate that neurophysiological and anatomical findings can motivate theoretical considerations about the neural processing and vice versa. We explore How?'' and Why?'' questions in a close connection to each other. Methodologically this means using biologically detailed bottom-up'' computational models and abstract top-down'' models in parallel or in combination. Specifically, we focus on the contrast- and orientation-processing in the primary visual cortex (V1) with a strong emphasis on the dynamics of the neural activity and synapses. We consider neural dynamics on three different time scales: (i) the fast time evolution of the cortical activity with a time constant of 16-20 msec; (ii) the intermediate modulation of the recurrent cortical competition strength with a time constant in the order of 100-200 msec (the approximate length of a fixation period); (iii) contrast adaptation by the slow modulation of the dynamic nature of the synaptic transmission with a time constant of 5-10 sec. Firstly, we explore how orientation selectivity could be generated in the primary visual cortex (V1). Orientation selectivity is a remarkable and well-explored feature of the simple cells in V1. However, there is still considerable debate about the neurophysiological and anatomical origin of the highly feature selective response of these cells. The major question concerns the extent to which the simple cell properties are determined by the structure of their feed-forward connectivity versus the recurrent projections. In contrast to previous models, in which the initial orientation bias is generated by convergent geniculate (feed-forward) input to the simple cells, and subsequently sharpened by the lateral circuits, our approach is based on anisotropic intracortical excitatory connections. We study the hypothesis that these recurrent projections provide both the initial orientation bias and its subsequent amplification and therefore orientation selectivity is generated purely intracortically. Our computational study shows that indeed the intracortical hypothesis'' is a plausible alternative to the other existing hypotheses. The model predicts that the dynamics of the orientation tuning could be indicative of the underlying neural mechanism. Therefore we investigate recurrent dynamics in a cortical orientation hypercolumn in a more biologically detailed statistical neural field model. Secondly, we study why the recurrent cortical re-processing of the feed-forward input is important for the representation of the image projected on the retina. We propose that the recurrent lateral connections implement competition between orientation selective simple cells with overlapping receptive fields. Then, we introduce the concept of dynamic coding'', and investigate the short term dynamics of the recurrent competition in the primary visual cortex in terms of information processing. We find that information transfer is optimal in any increasing time window after stimulus onset if the recurrent cortical amplification decreases. In the model, the initially strong cortical competition decreases, and the role of the geniculate origin feed-forward projections becomes more important. These geniculo-cortical projections carry a topographic representation of the image projected to the retina. Motivated by information theory, our results offer a compromise between the feed-forward'' and the recurrent'' hypotheses for orientation selectivity. We suggest that both are valid, however, in different phases of the cortical processing during a fixation period. In the initial phase of processing, the recurrent competition is strong, and the salient orientation is signaled in a winner-take-all fashion. In the second phase, cortical competition becomes weaker, allowing the detection of multiple orientations. A detailed computational model provides experimentally testable predictions about the dynamics of cortical response to multiple orientations. Thirdly, we study how and why contrast adaptation occurs in V1. We find that the adaptation of the transmitter release probability accounts well for all the puzzling experimental data that is available about the neurophysiology of contrast adaptation. The good match between our simulation results and the experimental data originates from the fact that the dynamic nature of the synaptic transmission depends on the transmitter release probability. The adaptation rule for the transmitter release probability is derived from the assumed functional objective of contrast adaptation. We propose that contrast adaptation reduces the redundancy in the cortical response by matching the activation function of single cortical neurons to the second-order signal statistics. We also show that increasing the release probability in a low-contrast environment has the functional advantage that it induces a cortical neuron to detect synchrony in its presynaptic spike trains, rather than the presynaptic firing rates. This synchrony detection mode may be proper for noise filtering if the contrast level is decreased because synchronous geniculate firing events are more likely to be stimulus related.
URI: urn:nbn:de:kobv:83-opus-566
http://depositonce.tu-berlin.de/handle/11303/451
http://dx.doi.org/10.14279/depositonce-154
Exam Date: 14-Dec-2000
Issue Date: 26-Jan-2001
Date Available: 26-Jan-2001
DDC Class: 500 Naturwissenschaften und Mathematik
Subject(s): Neural information processing
Neural network model
Visual cortex
Usage rights: Terms of German Copyright Law
Appears in Collections:Technische Universität Berlin » Fakultäten & Zentralinstitute » Fakultät 4 Elektrotechnik und Informatik » Publications

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