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Computational Models of Neural Circuitry in the Macaque Monkey Primary Visual Cortex

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Computational Models of Neural Circuitry in the Macaque Monkey Primary Visual Cortex Computational Models of Neural Circuitry in the Macaque Monkey Primary Visual Cortex Der Technischen Fakultat¨ der Universitat¨ Bielefeld vorgelegt von Ute Bauer zur Erlangung des akademischen Grades Doktor der Naturwissenschaften Oktober 1998 i Acknowledgements The interdisciplinary scope of this thesis is an example of how the collaboration be- tween experimental and theoretical disciplines contributes to the understanding of the functional structure of the brain. In the first place I want to thank my supervisors Prof. Klaus Obermayer, Prof. Jennifer S. Lund and Prof. Helge Ritter for introducing me to the field of ’Computational Neuro- science’. The work would not have been possible without the generous support of Prof. Jennifer S. Lund and her group, especially Dr. Jonathan B. Levitt, who introduced me to the anatomy and physiology of the macaque primary visual cortex. The close collaboration with these excellent experimenters was one of the major benefits of my work. I am very grateful to Prof. Klaus Obermayer for his guiding and careful advice which was the important foundation of my work. His continuous encouragement gave strong impetus and major benefits for my research. I appreciated the creative support of Prof. Helge Ritter who always had an open mind for my interdisciplinary work done in the Neu- roinformatics research group at the University of Bielefeld headed by him. I would also like to thank Dr. Michael Scholz who introduced me to my project and accompanied my work all the time with helpful discussions and critical comments not only in scientific re- spects. Since the ’team’ was distributed all over Germany (Bielefeld, Berlin) and Europe (London, U. K.) I am grateful to everyone for the excellent long-distance cooperation. During my work I was member of the Graduiertenkolleg ”Strukturbildungsprozesse” at the Forschungsschwerpunkt Mathematisierung headed by Prof. Andreas Dress and I want to thank him and the other members of the program for providing a pleasant working atmosphere. This work was made possible by a grant of the German Science Foundation. Additional thanks go to P´eter Adorj´an who was a stimulating colleague and many of our discussions have been fruitful for my work. Michael Scholz, Jonathan B. Levitt, J¨org Ontrup and Tim Nattkemper read all or parts of the manuscript and gave me valuable feedback. I want to express very special thanks to Heiko who accompanied my work during the last months with outstanding patience and whose help goes far beyond reading and com- menting on this manuscript. Finally I want to dedicate this work to my grandparents and parents, especially to my grandfather who always supported me on my way. iii Abstract The manuscript in hand is concerned with the functional architecture of the primary visual cortex (visual area V1, striate cortex) of the macaque monkey which serves as an excellent animal model for the human visual system. The first part of the thesis reviews the relevant anatomical and physiological findings. The early stages of visual processing in primates are characterized by two physiologically distinct pathways: the magnocellular (M) channel characterized by large receptive fields and high contrast sensitivity and the parvocellular (P) channel characterized by small receptive fields and low contrast sensitiv- ity. Both channels originate in the retina and relay via the lateral geniculate nucleus (LGN) to the and subdivision of layer 4C in the primary visual cortex. The physiologically distinct LGN-P and LGN-M inputs to layer 4C are transformed into three partially overlapping output channels shown to emerge from neurons at different depths of the layer. Physiological findings from more than one laboratory indicate that receptive field size and achromatic contrast sensitivity of cells in the upper () and lower ( ) half of layer 4C reflect the properties of the LGN-M and LGN-P afferents, however, there is a nonlinear gradient in these properties from top to bottom of the layer. It is the gradient in receptive field size and achromatic contrast sensitivity in depth of layer 4C that should be replicated by the modelling work presented in this thesis. In the second part of the manuscript computational models are developed which address the trans- formation of the afferent parvo- and magnocellular relays and the local excitatory and inhibitory circuitry of layer 4C. The models are calibrated as far as possible by known anatomical and phys- iological data. Characteristic to the modelling approach are realistic dendritic and axonal arbor spread and constant synaptic loads used to establish the connectivities between the connectionist model neurons. The first model of LGN input to layer 4C has been used to test the functional hypothesis that feedforward convergence of P and M inputs onto layer 4C spiny stellate cells is sufficient to explain the observed gradual change in receptive field size and contrast sensitivity with rise in depth of the layer. Overlap of dendrites of postsynaptic neurons between M and P input zones proved to be sufficient to explain changes through the lower two-thirds of layer 4C, while the more rapid change in upper 4C was matched by proposing two different M inputs with partial overlap in the upper 4C. The second model of local intralaminar circuitry of layer 4C has been used to test the functional hypothesis that differences in the overall balance between recur- rent excitation and lateral inhibition from two different neuron types cause the rapid increase of receptive field size and contrast sensitivity in upper 4C. The numerical simulations show that the lateral excitatory inputs which are known to come from an increasingly wider range within the retinotopic map with rise in depth of the layer have to become substantially more effective towards the top of the layer to account for the increased receptive field size in upper 4C. The lateral so- matic inhibition which also arises from a wider range in upper 4C has to have a higher threshold and gain to result in a rapid increase of contrast sensitivity at the top of the layer. Both hypothe- ses are consistent with the available anatomical and physiological data. Based on the numerical simulation results, new experimental tests are proposed which may confirm, refute, or distinguish between the different functional hypotheses. The numerical simulation of brain functions known as ”Computational Neuroscience” plays an increasingly important role in revealing the basic principles of neural information processing. This thesis is a first step to systematically develop a ”transfer function” of layer 4C in the macaque striate cortex. Contents Acknowledgements i Abstract iii Table of Contents 1 1 Introduction 5 1.1 ScopeandGoals .................................. 5 1.2 PlanoftheManuscript ............................. 7 2 Neurobiological Background 9 2.1 Principles of Neural Information Processing . ........... 9 2.2 SingleNeuronModels .............................. 13 2.3 The Visual System of Primates: An Overview . ....... 18 2.4 Early Stages of Visual Information Processing . ........... 25 2.4.1 TheRetina ................................. 25 2.4.2 The Lateral Geniculate Nucleus . 29 2.4.3 ThePrimaryVisualCortex . 33 2.5 Summary ...................................... 41 3 The Depth-Dependence of Basic Response Properties of Cells in Layer 4C 43 3.1 Afferent and Efferent Connections of Layer 4C . ......... 43 3.2 Functional Gradient in Depth of Layer 4C . ....... 45 3.2.1 Physiological Properties of LGN-P and LGN-M Cells . ....... 45 3.2.2 Basic Response Properties of Cells in Layer 4C . ....... 49 3.3 Summary ...................................... 52 4 Anatomical and Physiological Findings: Thalamic Feedforward Connections 53 4.1 Overview of Relevant Anatomical Findings . ........ 53 4.1.1 ThalamicAxons .............................. 53 4.1.2 Local Spiny Stellate Cells . 56 4.2 Overview of Relevant Physiological Findings . .......... 57 4.2.1 Three Functional Groups of LGN Cells . 58 4.2.2 Response Latencies of Cells in Layer 4C . 60 4.3 Extrapolations from Comparison of Anatomical and Physiological Findings . 60 2 CONTENTS 5 A Feedforward Model for the Depth-Dependence of Basic Response Properties in Layer 4C 63 5.1 Anatomical and Physiological Parameters . ......... 63 5.1.1 Anatomical Parameters . 64 5.1.2 Physiological Parameters of LGN cells . ..... 66 5.1.3 Overview of the Parameter Space . 68 5.2 Methods....................................... 70 5.2.1 Neural Network Architecture . 70 5.2.2 Connectionist Model Neuron . 72 5.2.3 VisualStimulation . ...... ..... ...... ..... ...... 72 5.2.4 LGNNeurons................................ 74 5.2.5 CorticalNeurons .............................. 77 5.2.6 Implementation............................... 82 6 Results of the Feedforward Model 83 6.1 Results Model I: One LGN-M population . ..... 83 6.1.1 Percentage of P- vs. M-inputs as a Function of Depth . ........ 84 6.1.2 Threshold Dependence of Response Properties . ....... 85 6.1.3 Transfer Functions of Geniculate P-cells . ........ 86 6.1.4 Dendritic Arbor Size of Layer 4C Spiny Stellate Neurons......... 88 6.1.5 BestPredictions .............................. 89 6.1.6 Summary .................................. 90 6.2 Results Model II: Two LGN-M populations . ...... 91 6.2.1 Physiological Properties of M2 and M1 Subpopulations ......... 91 6.2.2 Percentage of M1- vs. M2-inputs as a Function of Depth . ........ 95 6.2.3 Effects of Receptive Field Size of M2 and M1 Neurons . ....... 96 6.2.4 Effects of Contrast Sensitivity
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