Design and Optimisation of Extracellular Microelectrodes

The work described in this thesis concerns the development and application of design methods for the optimisation of thin film metal microelectrodes, to be used for recording the electrical signals generated by neurons in culture

The Fifteenth Annual Conference YUCOMAT 2013: Programme and the Book of Abstracts

The First Conference on materials science and engineering, including physics, physical chemistry, condensed matter chemistry, and technology in general, was held in September 1995, in Herceg Novi. An initiative to establish Yugoslav Materials Research Society was born at the conference and, similar to other MR societies in the world, the programme was made and objectives determined. The Yugoslav Materials Research Society (Yu-MRS), a nongovernment and non-profit scientific association, was founded in 1997 to promote multidisciplinary goal-oriented research in materials science and engineering. The main task and objective of the Society has been to encourage creativity in materials research and engineering to reach a harmonic coordination between achievements in this field in our country and analogous activities in the world with an aim to include our country into global international projects. Until 2003, Conferences were held every second year and then they grew into Annual Conferences that were traditionally held in Herceg Novi in September of every year. In 2007 Yu-MRS formed two new MRS: MRS-Serbia (official successor of Yu-MRS) and MRS-Montenegro (in founding). In 2008, MRS – Serbia became a member of FEMS (Federation of European Materials Societies)

Ag-Ag2Sコアシェル型ナノ粒子を用いたリザーバーコンピューティングデバイスの作製

The performance of the von Neumann computer was greatly improved by miniaturizing transistors and increasing the density according to Moore\u27s Law. However, in recent years, the maximum permissible number of CPU transistors has remained constant, and further performance improvements have not been possible. In today\u27s nanoscale era, scaling to smaller sizes represents a major challenge in device manufacturing, circuit, and system design and integration. On the other hand, nanoscale technology has the potential to develop new materials and devices with unique properties. Memristors exhibit nonlinear current-voltage characteristics and have unique memory characteristics. That is, such a new nanoscale device whose current state depends on the past. It has the potential to create new computing paradigms for both non-linear and memory characteristics of Memristors. The purpose of this paper is to investigate the possibility of using wet chemical synthesis and Ag-Ag2S core-shell nanoparticles to develop a new computing paradigm called “Reservoir Computing” (RC) which belong to such a new paradigm. However, it differs from the traditional Recurrent Neural Network (RNN) method in that the pre-processor (ie, the reservoir) is composed of nonlinear elements that are randomly connected repeatedly. This greatly reduces the complexity of learning. In this thesis, we reported RC devices with low power consumption. The synthesis conditions of Ag-Ag2S core-shell nanoparticles operating at low voltage were searched. Next, synthesis parameters such as Ag / S molar ratio were examined, to control the particle size. We confirmed that the nanoparticle agglomerates have nonlinear electrical conductivity necessary for the development of RC computations, such as constantly exhibiting hysteresis in the current-voltage (I-V) curve, and investigated other conditions necessary for RC hardware. Since the linear regression of the output channel was trained to fit the target waveform, the potential of the nanoparticle-based RC device was shown.九州工業大学博士学位論文 学位記番号:生工博甲第359号 学位授与年月日:令和元年12月27日1 Introduction and Literature Review|2 Methodology|3 Effect of various synthesis procedure to electrical characteristics of the nanoparticles-based device|4 Effect of the Ag-Ag2S volume ratio to the electrical properties|5 Switching mechanism of Ag-Ag2S nanoparticles-based device and neuromorphic learning properties|6 Recurrent neural network properties of Ag-Ag2S nanoparticles-based device and its application as reservoir computing|7 Conclusions and Suggestions九州工業大学令和元年

Towards an integrated understanding of neural networks

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 123-136).Neural networks underpin both biological intelligence and modern Al systems, yet there is relatively little theory for how the observed behavior of these networks arises. Even the connectivity of neurons within the brain remains largely unknown, and popular deep learning algorithms lack theoretical justification or reliability guarantees. This thesis aims towards a more rigorous understanding of neural networks. We characterize and, where possible, prove essential properties of neural algorithms: expressivity, learning, and robustness. We show how observed emergent behavior can arise from network dynamics, and we develop algorithms for learning more about the network structure of the brain.by David Rolnick.Ph. D

Improved methods for functional neuronal imaging with genetically encoded voltage indicators

Voltage imaging has the potential to revolutionise neuronal physiology, enabling high temporal and spatial resolution monitoring of sub- and supra-threshold activity in genetically defined cell classes. Before this goal is reached a number of challenges must be overcome: novel optical, genetic, and experimental techniques must be combined to deal with voltage imaging’s unique difficulties. In this thesis three techniques are applied to genetically encoded voltage indicator (GEVI) imaging. First, I describe a multifocal two-photon microscope and present a novel source localisation control and reconstruction algorithm to increase scattering resistance in functional imaging. I apply this microscope to image population and single-cell voltage signals from voltage sensitive fluorescent proteins in the first demonstration of multifocal GEVI imaging. Second, I show that a recently described genetic technique that sparsely labels cortical pyramidal cells enables single-cell resolution imaging in a one-photon widefield imaging configuration. This genetic technique allows simple, high signal-to-noise optical access to the primary excitatory cells in the cerebral cortex. Third, I present the first application of lightfield microscopy to single cell resolution neuronal voltage imaging. This technique enables single-shot capture of dendritic arbours and resolves 3D localised somatic and dendritic voltage signals. These approaches are finally evaluated for their contribution to the improvement of voltage imaging for physiology.Open Acces

Applying automation and machine learning to scanning transmission electron microscopy

This work studies how the benefits of automation and machine learning can be applied to the creation, imaging and image analysis of scanning transmission electron microscopy (STEM) samples. Recrystallised tungsten tips are produced using a semi-automated multi-stage process for use as sample platforms in atomic electron tomography (AET). Two coating techniques are tested to see whether they may be viable methods of reducing sample oxidation. An automated microscope control software framework is presented and demonstrated in three different scenarios: the high-throughput acquisition of CdSe/CdS core-shell nanoparticles, the acquisition of CBED patterns of chiral tellurium nanoparticles and the search for candidate particles for alpha tomography. Finally, machine learning is used to classify the handedness of simulated chiral particles using stereopairs of simulated STEM projections. A 'weak labelling' approach is also demonstrated that takes advantage of the intrinsic nature of chirality to remove the need for manually labelling training datasets