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Spatiotemporal Adaptation in the Corticogeniculate Loop

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Spatiotemporal Adaptation in the Corticogeniculate Loop Physik-Department Technische UniversitÄat MuncÄ hen Theoretische Physik Spatiotemporal Adaptation in the Corticogeniculate Loop Ulrich Hillenbrand VollstÄandiger Abdruck der von der FakultÄat furÄ Physik der Technischen UniversitÄat MuncÄ hen zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften genehmigten Dissertation. Vorsitzender: Univ.-Prof. Dr. M. Kleber PruferÄ der Dissertation: 1. Univ.-Prof. Dr. J. L. van Hemmen 2. Univ.-Prof. Dr. E. Sackmann Die Dissertation wurde am 29.02.2000 bei der Technischen UniversitÄat MuncÄ hen eingereicht und durch die FakultÄat furÄ Physik am 16.01.2001 angenommen. Acknowledgments It is my pleasure to thank Professor Dr. J. Leo van Hemmen for cultivating an environment at his institute for creative exploration and growth of new ideas. Open-mindedness and, at the same time, a strong sense for scien- ti¯c value are of particular importance and delicacy in a ¯eld at the interface between empirical biological diversity and mathematical rigor. Leo van Hem- men combines both in his attitude and has always promoted the according style of work. Moreover, I like to thank the individuals who populated this environment. They always were supportive in that each one of them showed sincere interest in the other's scienti¯c concerns. More subtilely, the distinguished sense of humor that we shared helped to overcome one or the other tense period. I am especially indebted to those of my colleagues who spent a signi¯cant amount of their time on maintaining our local computer network, most no- tably Armin Bartsch, Moritz Franosch, and Oliver Wenisch. Without their kind support, things would have got stuck in computer trouble more than once. Special thanks also go to Dr. Thomas JestÄadt. As a decent friend, he took some pains to comment on the manuscript from the valuable perspective of the intelligent non-specialist. I thank the Deutsche Forschungsgemeinschaft (DFG) for funding my work for its largest part (grant GRK 267), and my colleagues at the Graduiertenkol- leg \Sensorische Interaktion in biologischen und technischen Systemen" for interesting discussions and insights into various methods for studying the brain. Especially insightful was a visit to PD Dr. Esther Peterhans at the Uni- versity of Zurich. I very much appreciate several discussions on experimental data and on ideas about visual motion processing. At times it could be hard to tolerate life with a scientist, and perhaps the doctoral-researcher type is the worst company for every-day life. My wife must know about it. After all, emotion is more important than visual motion, and I am very grateful for Ania's patience and understanding during the period of my doctoral work. Life as a doctoral researcher, very unfortunately, is rarely blessed with economic prosperity. I owe special thanks to my parents who, when times got tough, o®ered plentiful support both morally and materially, far beyond what one could expect from parents. i ii Abstract The thalamus is the major gate to the cortex for almost all sensory sig- nals, for input from various subcortical sources such as the cerebellum and the mammillary bodies, and for reentrant cortical information. Thalamic nuclei do not merely relay information to the cortex but perform some op- eration on it while being modulated by various transmitter systems and in continuous interplay with their cortical target areas. Indeed, cortical feed- back to the thalamus is the anatomically dominant input to relay cells even in those thalamic nuclei that are directly driven by sensory systems. While it is well-established that the receptive ¯elds of cortical neurons are strongly influenced by convergent thalamic inputs of di®erent types, the modulation e®ected by cortical feedback in thalamic response has been di±cult to inter- pret. Experiments and theoretical considerations have pointed to a variety of operations of the visual cortex on the visual thalamus, the lateral genic- ulate nucleus (LGN), such as control of binocular disparity for stereopsis (Schmielau & Singer, 1977), attention-related gating of relay cells (Sherman & Koch, 1986), gain control of relay cells (Koch, 1987), synchronizing ¯ring of neighboring relay cells (Sillito et al., 1994; Singer, 1994), increasing visual information in relay cells' output (McClurkin et al., 1994), and switching relay cells from a detection to an analyzing mode (Godwin et al., 1996; Sher- man, 1996; Sherman & Guillery, 1996). Nonetheless, the evidence for any particular function is still sparse and rather indirect to date. Clearly, detailed concepts of the interdependency of thalamic and corti- cal operation could greatly advance our knowledge about complex sensory, and ultimately cognitive, processing. Here we present a novel view on the corticothalamic puzzle by proposing that control of velocity tuning of visual cortical neurons may be an eminent function of corticogeniculate processing. The hypothesis is advanced by studying a model of the primary visual pathway in extensive computer simulations. At the heart of the model is a biophysical account of the electrical membrane properties of thalamic relay neurons (Huguenard & McCormick, 1992; McCormick & Huguenard, 1992) that includes 12 ionic conductances. Among the di®erent e®ects that cor- ticogeniculate feedback may have on relay cells, we focus on the modulation of their relay mode (between tonic and burst mode) by control of their rest- ing membrane potential. Employing two distinct temporal-response types of geniculate relay neurons (lagged and nonlagged), we ¯nd that shifts in membrane potential a®ect the temporal response properties of relay cells in a way that alters the tuning of cortical cells for speed. iii Given the loop of information from the LGN to cortical layer 4, via a variable number of synapses to layer 6, and back to the LGN, the question arises, what are likely implications of adaptive speed tuning for visual infor- mation processing? Based on some fairly general considerations concerning the nature of motion information, we devise a simple model of the cortico- geniculate loop that utilizes adaptive speed tuning for the fundamental task of segmentation of objects in motion. A detailed mathematical analysis of the model's behavior is presented. Treating visual stimulation as a stochastic process that drives the adaptation dynamics, we prove the model's object- segmentation capabilities and reveal some non-intended properties, such as oscillatory responses, that are consequences of its basic design. Several as- pects of the dynamics in the loop are discussed in relation to experimental data. iv Contents 1 The Thalamus 3 1.1 A Hierarchy of Thalamocortical Processing . 5 1.2 Thalamic Relay Modes . 12 1.3 Functional Implications . 15 2 Dynamic Cortical Velocity Tuning 19 2.1 Geniculate Input to Cortical Simple Cells . 20 2.1.1 Spatiotemporal Receptive Fields and Velocity Tuning . 20 2.1.2 Lagged and Nonlagged Relay Neurons . 22 2.2 A Model of the Primary Visual Pathway . 29 2.2.1 Neuron and Network Models . 31 2.2.2 Lagged and Nonlagged Responses . 34 2.2.3 Spatial Layout of Receptive Fields . 35 2.2.4 Geniculate-Perigeniculate Loops . 36 2.2.5 Retinal Input . 36 2.2.6 Cortical Feedback . 38 2.2.7 Data Analysis . 40 2.2.8 Numerics . 42 2.3 Results . 43 2.3.1 Lagged and Nonlagged Relay Neurons . 43 2.3.2 Total Geniculate Input to the Cortex . 47 2.4 Discussion . 57 2.4.1 Relation to Cortical Velocity Tuning . 57 2.4.2 Role of Relay Modes . 59 2.4.3 Role of the Perigeniculate Nucleus . 60 2.4.4 Variability of Geniculate Response Strength . 60 2.4.5 Other Types of Corticogeniculate Feedback . 61 3 Object Segmentation by Adaptive Velocity Tuning 62 3.1 On Monkeys, Leopards, and other Objects in Motion . 62 3.2 Data Reduction and Object Segmentation by Motion Processing 64 1 CONTENTS 3.3 A Model of the Corticogeniculate Loop . 67 3.3.1 Dynamic Velocity Tuning . 70 3.3.2 Control of Velocity Tuning . 71 3.3.3 Complete System Dynamics . 72 3.3.4 Analytical Treatment . 74 3.3.5 Computer Simulations . 75 3.4 Analysis and Results . 75 3.4.1 Integral Equations for the Moments of Response Time Di®erences . 75 3.4.2 Markov Formulation of the Dynamics of Response Time Di®erences . 78 3.4.3 Mean Adaptation Dynamics . 81 3.4.4 Variance of Adaptation Dynamics . 87 3.4.5 Stationary States and Di®usion-Sustained Oscillations . 92 3.4.6 Correlation of Adaptation Dynamics Between Cell Classes 99 3.4.7 Crossing of Response Time Di®erences: Disruption of Adaptation . 103 3.4.8 Corticogeniculate Delays . 110 3.5 Discussion . 117 3.5.1 Spatiotemporal Patterns of Cortical Activity . 119 3.5.2 Figure-Ground Segregation . 120 3.5.3 Fixational Eye Movements . 124 3.5.4 Encoding Stimulus Speed . 126 3.5.5 Unaddressed Issues and Possible Model Extensions . 128 A Biophysical Neuron Models 131 B The Spike-Response Model of a Neuron 137 C Model of Thalamic Relay Neurons 139 D Stimulus Parameters for Poissonian Stimulus Events 144 E Proof of Two Assertions on Crossing of Mean Response Time Di®erences 146 F Computer Simulations of Corticogeniculate Feedback 150 G Computer Simulations of the Corticogeniculate Loop 152 H List of Abbreviations 155 2 Chapter 1 The Thalamus The dorsal thalamus, which forms the main mass of the diencephalon in most mammals, is the principal source of subcortical input to the cerebral cortex, and all that the cortex can do necessarily depends on this input. By way of the dorsal thalamus the cortex is informed of all sensory input, with the olfactory system as the only exception, and of activity in subcortical brain structures such as the cerebellum, the mammillary bodies, and the globus pallidus; see Jones (1985) for a summary. It is thus most evident that uncovering the relay functions of the dorsal thalamus is crucial for a deeper understanding of all sensory and cognitive processing.
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