Topologically Defined Neuronal Networks Controlled by Silicon Chips

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CHAPTER 1. INTRODUCTION Figure 1.1: Left: Piece of the human visual cortex with highly branched neurites and complex synaptic connection pattern, from [20]. Right: Schematic drawing of a simple, designed feed-forward network. Studying these systems, once they are established, requires long term control of network activity at the single cell level; action potentials must be evoked in and recorded from individual neurons. Standard electrophysiology, using impaled microelectrodes or patch clamp, is inappropriate because it harms the nerve cells, thereby limiting the recording time to a few hours before cells die. Furthermore, spatial constraints with micromanipulators used for positioning the pipettes considerably restrict the number of cells that can be monitored. Alternative techniques are needed that are non-invasive, as sketched in fig. 1.2, and also can be scaled up to large neuronal assemblies . Figure 1.2: Invasive (left) and non-invasive (right) recording and stimulation of individual neurons. The aim of this thesis is to implement and study small networks of living neurons with a defined synaptic connection pattern. This requires advances in network design and extracellular recording, and lays the foundations for systematic tests of fundamental concepts in neuroscience. 1.2 Defined networks and extracellular recording, state-of-the-art Since the ideas outlined above are not new, much work has already been done on network design and non-invasive recording. This paragraph gives a short overview of relevant technologies reported in the literature. A variety of methods exists for building topologically defined networks. Most of them use patterns of substrate-bound molecules that either promote or inhibit neurite outgrowth, e.g. silanes [56], poly-L-lysine [8] or proteins [81]. Neurons cultured on these substrates grow neurites that follow the permissive tracks while avoiding inhibitory areas. The patterns are made with techniques adapted from semiconductor processing, like photolithography [56], laser ablation [15] or microcontact printing [97]. Although they guide advancing neurites rather well, somata and already grown neurites are frequently 2
1.3. OUTLINE OF THIS THESIS displaced by pulling forces exerted by the growth-cones. In a preceding study [129], pillars processed from photoresist were used to mechanically immobilize the cell bodies on predefined sites. Much smaller topographic structures consisting of grooves or edges in the micrometer and nanometer range have been shown to affect neurite outgrowth, too [84]. Even though they have been proposed as a tool for designing networks, there are no reports about successful applications so far. Long term control of neuronal activity is only possible with techniques that contact neurons extracellularly. The simplest systems consist of metal spots processed onto planar substrates and connected to external amplifiers via insulated leads. Arrays of these electrodes, so-called MEAs (multi electrode arrays) are a common tool for monitoring network activity, for example in studies about the effects of chemicals on neuronal viability [76]. In 1972, Bergveld [2] used field-effect transistors with non-metal gates to make recordings from muscle fibers of guinea pigs and in 1990 Fromherz et al. were able to detect action potentials from neurons of the leech Hirudo medicinalis with similar devices [33]. Since then, the neuron-silicon interface has been studied in detail and recordings were made from rat hippocampal neurons (only with signal averaging to reduce noise) and nerve cells from the snail Lymnaea stagnalis [50, 114, 129]. 1.3 Outline of this thesis Building chip-controlled networks raises several questions and problems that can be divided into three parts: choosing the appropriate neuronal cell culture, growing topologically defined networks of synaptically connected neurons, and bidirectional interfacing of neurons to silicon chips. In the course of the experimental work, these parts were first addressed separately, though sometimes in parallel, and then joined at the end. The thesis roughly follows this order. The next chapter covers all aspects of network design, from cell culture to theoretical concepts of signal propagation in neurites. After introducing the mechanisms governing axon guidance in vivo, methods for controlling neurite outgrowth in vitro are reviewed. The cell culture is then described, and the technological details of protein patterns and topographic structures are given, followed by a theoretical section on neurons and synapses. Finally, examples of defined neural networks are shown, characterized and discussed. Once these networks can be reliably grown, their activity should be controlled non-invasively, and this is the subject of chapter 3. It illustrates the theoretical and technological aspects of buried channel fieldeffect transistors and describes relevant processing steps. Fundamental principles of the neuron-silicon interface are given in a further theoretical section. At the end, experimental results on stimulation and recording via the chip are presented and discussed. Chapter 4 combines these techniques to establish chip-controlled, defined neural networks. After a brief recapitulation of theoretical concepts, three examples of increasing complexity are shown. The final discussion focuses on the efficiency of the single steps involved in growing the networks and the overall yield, and on how it may be increased. The final chapter 5 summarizes the results and provides an outlook towards future experiments and applications. Technological details of the cell culture and semiconductor processing, as well as parameters for simulations are outsourced to appendices. 3
Page 1: Max-Planck-Institut für Biochemie
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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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