Topologically Defined Neuronal Networks Controlled by Silicon Chips

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CONTENTS 3.3.2 Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.3.3 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.4 Measurement setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.4.1 Chip amplifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.4.2 Hard and software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 3.4.3 Microscope and electrophysiology . . . . . . . . . . . . . . . . . . . . . . . . 65 3.4.4 A typical experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 3.5 Theory part III: Neuron-silicon interface . . . . . . . . . . . . . . . . . . . . . . . . . 67 3.5.1 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 3.5.2 Capacitive stimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.5.3 Extracellular recording . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 3.5.4 What do transistors measure? . . . . . . . . . . . . . . . . . . . . . . . . . . 72 3.6 Neuron-silicon interface: Experimental results . . . . . . . . . . . . . . . . . . . . . . 73 3.6.1 Capacitive Stimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.6.2 Extracellular recording: A gallery of signals . . . . . . . . . . . . . . . . . . . 74 3.6.3 Bidirectional junction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 3.6.4 Dynamics of action potentials and transistor signals . . . . . . . . . . . . . . . 77 3.6.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4 Defined networks on chips 83 4.1 Theory part IV: Chip-controlled networks . . . . . . . . . . . . . . . . . . . . . . . . 83 4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 4.2.1 Basic silicon-neuron-neuron-silicon loop . . . . . . . . . . . . . . . . . . . . 84 4.2.2 Partially controlled three-neuron network . . . . . . . . . . . . . . . . . . . . 87 4.2.3 Defined network of four neurons . . . . . . . . . . . . . . . . . . . . . . . . . 89 4.3 Final discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 4.3.1 Controlling neuronal activity from the chip . . . . . . . . . . . . . . . . . . . 93 4.3.2 Defined networks in topographic structures . . . . . . . . . . . . . . . . . . . 94 4.3.3 Stability of the chips . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.3.4 Yield, the decisive factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 4.3.5 Next steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 5 Conclusion and outlook 97 A Cell culture protocols and recipes 99 B Chip processing and characterization 102 C Simulation of extracellular signals 106 D Abbreviations 107 Bibliography 109 ii
Chapter 1 Introduction This first chapter outlines the ideas and questions that led to the present thesis and briefly reviews relevant technologies. An overview of the contents of the other chapters concludes the introduction. 1.1 Why build topologically defined hybrid networks? In vivo neural networks are highly complicated systems comprising up to 10 11 neurons with a complex pattern of synaptic connections. While recent advances in functional magnetic resonance imaging and positron emission tomography provide tools for monitoring the activity of whole brains, their temporal and spatial resolution is still far too low for studies at the single cell level. Despite these technological deficits, results from rather simple experiments, with impaled microelectrodes, inspired fundamental theoretical concepts like Hebbian learning or associative memory. Artificial systems have been developed, e.g. Hopfield networks, that resemble the natural ones to a smaller or larger extent. Such artificial systems can perform difficult tasks of information processing and are even used for commercial applications. However, most of them are implemented in special software running on ordinary computers and can therefore not profit from the massive parallel computing capabilities of the living neural networks they were adapted from. There are only a few examples where these ideas have been realized in hardware, namely custom made silicon chips. The high number of synaptic connections restricts network size on these chips to about 1000 neurons or less, even in the VLSI (very large scale integration; silicon processing technology) era [96]. In consequence there is a large technological gap between theory on the one hand and the real world on the other. Eventually this gap could be bridged with a bottom up approach, the controlled assembly of well organized networks from living neurons in vitro. These networks would provide a unique tool for studying signal processing in biological nervous systems and their parallel computing capabilities could lay the foundations for novel computer systems. Nerve cell cultures are a common technique in neuroscience. They considerably reduce the number of neurons, compared to living brains and restrict the network to the 2D surface of the culture dish, thereby giving easy access to all neurons. Yet, synapses are still numerous and their connections remain unknown, obstructing any attempt to establish a theoretical model of the system. The problem could be overcome if it were possible to grow networks with defined synaptic connection pattern, e.g. the two layer perceptron in fig. 1.1. 1
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3.3. THE TRANSISTOR-STIMULATOR CHIP
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Silicon dioxide 3.3. THE TRANSISTOR
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A C B D 3.3. THE TRANSISTOR-STIMULA
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3.4. MEASUREMENT SETUP Figure 3.11:
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3.4. MEASUREMENT SETUP onboard memo
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3.5. THEORY PART III: NEURON-SILICO
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characterizing the dynamics of the
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3.5. THEORY PART III: NEURON-SILICO
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3.6. NEURON-SILICON INTERFACE: EXPE
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demonstrated by the examples in fig
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Chapter 4 Defined networks on chips
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4.2. RESULTS Figure 4.2: A: Microgr
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4.2. RESULTS 2 postsynaptic action
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4.2. RESULTS Since neuron 1 could a
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4.2. RESULTS Figure 4.5: A: Transis
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4.3. FINAL DISCUSSION Data in diagr
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4.3. FINAL DISCUSSION Some very har
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Chapter 5 Conclusion and outlook Th
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Appendix A Cell culture protocols a
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ethanol and 30% water during UV-ste
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For two-layer structures, steps 3 t
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The flatband voltage VF B and elect
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Appendix D Abbreviations ABS : anti
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Bibliography [1] R. W. Aldrich, P.
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BIBLIOGRAPHY [32] P. Fromherz. Neur
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BIBLIOGRAPHY [69] C. D. McCaig, A.
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BIBLIOGRAPHY [105] N. I. Syed, A. G
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Ich danke PROF. DR. P. FROMHERZ fü
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Topologically Defined Neuronal Networks Controlled by Silicon Chips

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