Relevant bibliographies by topics / NeuroElectronics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'NeuroElectronics'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "NeuroElectronics"

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Jastrzebska‐Perfect, Patricia, Shilpika Chowdhury, George D. Spyropoulos, Zifang Zhao, Claudia Cea, Jennifer N. Gelinas, and Dion Khodagholy. "Translational Neuroelectronics." Advanced Functional Materials 30, no. 29 (June 8, 2020): 1909165. http://dx.doi.org/10.1002/adfm.201909165.

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Waldrop, M. Mitchell. "Neuroelectronics: Smart connections." Nature 503, no. 7474 (November 2013): 22–24. http://dx.doi.org/10.1038/503022a.

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Krook-Magnuson, Esther, Jennifer N. Gelinas, Ivan Soltesz, and György Buzsáki. "Neuroelectronics and Biooptics." JAMA Neurology 72, no. 7 (July 1, 2015): 823. http://dx.doi.org/10.1001/jamaneurol.2015.0608.

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Go, Gyeong‐Tak, Yeongjun Lee, Dae‐Gyo Seo, and Tae‐Woo Lee. "Organic Neuroelectronics: From Neural Interfaces to Neuroprosthetics." Advanced Materials 35, no. 12 (March 2023): 2300758. http://dx.doi.org/10.1002/adma.202300758.

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Vitale, Flavia, and Raghav Garg. "Novel materials and fabrication strategies for multimodal neuroelectronics." Brain Stimulation 16, no. 1 (January 2023): 117. http://dx.doi.org/10.1016/j.brs.2023.01.014.

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Di Palma, Valerio, Andrea Pianalto, Michele Perego, Graziella Tallarida, Davide Codegoni, and Marco Fanciulli. "Plasma-Assisted Atomic Layer Deposition of IrO2 for Neuroelectronics." Nanomaterials 13, no. 6 (March 8, 2023): 976. http://dx.doi.org/10.3390/nano13060976.

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In vitro and in vivo stimulation and recording of neuron action potential is currently achieved with microelectrode arrays, either in planar or 3D geometries, adopting different materials and strategies. IrO2 is a conductive oxide known for its excellent biocompatibility, good adhesion on different substrates, and charge injection capabilities higher than noble metals. Atomic layer deposition (ALD) allows excellent conformal growth, which can be exploited on 3D nanoelectrode arrays. In this work, we disclose the growth of nanocrystalline rutile IrO2 at T = 150 °C adopting a new plasma-assisted ALD (PA-ALD) process. The morphological, structural, physical, chemical, and electrochemical properties of the IrO2 thin films are reported. To the best of our knowledge, the electrochemical characterization of the electrode/electrolyte interface in terms of charge injection capacity, charge storage capacity, and double-layer capacitance for IrO2 grown by PA-ALD was not reported yet. IrO2 grown on PtSi reveals a double-layer capacitance (Cdl) above 300 µF∙cm−2, and a charge injection capacity of 0.22 ± 0.01 mC∙cm−2 for an electrode of 1.0 cm2, confirming IrO2 grown by PA-ALD as an excellent material for neuroelectronic applications.
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Bourrier, Antoine, Anna Szarpak-Jankowska, Farida Veliev, Renato Olarte-Hernandez, Polina Shkorbatova, Marco Bonizzato, Elodie Rey, et al. "Introducing a biomimetic coating for graphene neuroelectronics: toward in-vivo applications." Biomedical Physics & Engineering Express 7, no. 1 (December 4, 2020): 015006. http://dx.doi.org/10.1088/2057-1976/ab42d6.

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Go, Gyeong‐Tak, Yeongjun Lee, Dae‐Gyo Seo, and Tae‐Woo Lee. "Organic Neuroelectronics: From Neural Interfaces to Neuroprosthetics (Adv. Mater. 45/2022)." Advanced Materials 34, no. 45 (November 2022): 2270311. http://dx.doi.org/10.1002/adma.202270311.

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Golabchi, Asiyeh, Kevin M. Woeppel, Xia Li, Carl F. Lagenaur, and X. Tracy Cui. "Neuroadhesive protein coating improves the chronic performance of neuroelectronics in mouse brain." Biosensors and Bioelectronics 155 (May 2020): 112096. http://dx.doi.org/10.1016/j.bios.2020.112096.

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Zhao, Zifang, Claudia Cea, Jennifer N. Gelinas, and Dion Khodagholy. "Responsive manipulation of neural circuit pathology by fully implantable, front-end multiplexed embedded neuroelectronics." Proceedings of the National Academy of Sciences 118, no. 20 (May 10, 2021): e2022659118. http://dx.doi.org/10.1073/pnas.2022659118.

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Responsive neurostimulation is increasingly required to probe neural circuit function and treat neuropsychiatric disorders. We introduce a multiplex-then-amplify (MTA) scheme that, in contrast to current approaches (which necessitate an equal number of amplifiers as number of channels), only requires one amplifier per multiplexer, significantly reducing the number of components and the size of electronics in multichannel acquisition systems. It also enables simultaneous stimulation of arbitrary waveforms on multiple independent channels. We validated the function of MTA by developing a fully implantable, responsive embedded system that merges the ability to acquire individual neural action potentials using conformable conducting polymer-based electrodes with real-time onboard processing, low-latency arbitrary waveform stimulation, and local data storage within a miniaturized physical footprint. We verified established responsive neurostimulation protocols and developed a network intervention to suppress pathological coupling between the hippocampus and cortex during interictal epileptiform discharges. The MTA design enables effective, self-contained, chronic neural network manipulation with translational relevance to the treatment of neuropsychiatric disease.
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Dissertations / Theses on the topic "NeuroElectronics"

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Rapoport, Benjamin Isaac. "Glucose-powered neuroelectronics." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66460.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 157-164).
A holy grail of bioelectronics is to engineer biologically implantable systems that can be embedded without disturbing their local environments, while harvesting from their surroundings all of the power they require. As implantable electronic devices become increasingly prevalent in scientific research and in the diagnosis, management, and treatment of human disease, there is correspondingly increasing demand for devices with unlimited functional lifetimes that integrate seamlessly with their hosts in these two ways. This thesis presents significant progress toward establishing the feasibility of one such system: A brain-machine interface powered by a bioimplantable fuel cell that harvests energy from extracellular glucose in the cerebrospinal fluid surrounding the brain. The first part of this thesis describes a set of biomimetic algorithms and low-power circuit architectures for decoding electrical signals from ensembles of neurons in the brain. The decoders are intended for use in the context of neural rehabilitation, to provide paralyzed or otherwise disabled patients with instantaneous, natural, thought-based control of robotic prosthetic limbs and other external devices. This thesis presents a detailed discussion of the decoding algorithms, descriptions of the low-power analog and digital circuit architectures used to implement the decoders, and results validating their performance when applied to decode real neural data. A major constraint on brain-implanted electronic devices is the requirement that they consume and dissipate very little power, so as not to damage surrounding brain tissue. The systems described here address that constraint, computing in the style of biological neural networks, and using arithmetic-free, purely logical primitives to establish universal computing architectures for neural decoding. The second part of this thesis describes the development of an implantable fuel cell powered by extracellular glucose at concentrations such as those found in the cerebrospinal fluid surrounding the brain. The theoretical foundations, details of design and fabrication, mechanical and electrochemical characterization, as well as in vitro performance data for the fuel cell are presented.
by Benjamin Isaac Rapoport.
Ph.D.
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Naughton, Jeffrey R. "Neuroelectronic and Nanophotonic Devices Based on Nanocoaxial Arrays." Thesis, Boston College, 2017. http://hdl.handle.net/2345/bc-ir:108037.

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Thesis advisor: Michael J. Naughton
Thesis advisor: Michael J. Burns
Recent progress in the study of the brain has been greatly facilitated by the development of new measurement tools capable of minimally-invasive, robust coupling to neuronal assemblies. Two prominent examples are the microelectrode array, which enables electrical signals from large numbers of neurons to be detected and spatiotemporally correlated, and optogenetics, which enables the electrical activity of cells to be controlled with light. In the former case, high spatial density is desirable but, as electrode arrays evolve toward higher density and thus smaller pitch, electrical crosstalk increases. In the latter, finer control over light input is desirable, to enable improved studies of neuroelectronic pathways emanating from specific cell stimulation. Herein, we introduce a coaxial electrode architecture that is uniquely suited to address these issues, as it can simultaneously be utilized as an optical waveguide and a shielded electrode in dense arrays
Thesis (PhD) — Boston College, 2017
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Physics
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Yuan, Xiaobo [Verfasser], Roger [Gutachter] Woerdenweber, and Berenike [Gutachter] Maier. "Tailoring neuroelectronic interfaces via combinations of oxides and molecular layers / Xiaobo Yuan ; Gutachter: Roger Woerdenweber, Berenike Maier." Köln : Universitäts- und Stadtbibliothek Köln, 2021. http://d-nb.info/1228071829/34.

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Boehler, Christian [Verfasser]. "Electroactive Coatings as a Strategy to Reduce Tissue Inflammation and Increase the Functional Lifetime of Neuroelectronic Devices / Christian Boehler." München : Verlag Dr. Hut, 2019. http://d-nb.info/1181516196/34.

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Böhler, Christian [Verfasser]. "Electroactive Coatings as a Strategy to Reduce Tissue Inflammation and Increase the Functional Lifetime of Neuroelectronic Devices / Christian Boehler." München : Verlag Dr. Hut, 2019. http://nbn-resolving.de/urn:nbn:de:101:1-2019032222464092796562.

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Wolf, Nikolaus Radja [Verfasser], Roger [Gutachter] Wördenweber, and Thomas [Gutachter] Michely. "Molecular Layer Functionalized Neuroelectronic Interfaces: From Sub-Nanometer Molecular Surface Functionalization to Improved Mechanical and Electronic Cell-Chip Coupling / Nikolaus Radja Wolf ; Gutachter: Roger Wördenweber, Thomas Michely." Köln : Universitäts- und Stadtbibliothek Köln, 2021. http://d-nb.info/122586352X/34.

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Thakore, Vaibhav. "Nonlinear dynamic modeling, simulation and characterization of the mesoscale neuron-electrode interface." Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5529.

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Extracellular neuroelectronic interfacing has important applications in the fields of neural prosthetics, biological computation and whole-cell biosensing for drug screening and toxin detection. While the field of neuroelectronic interfacing holds great promise, the recording of high-fidelity signals from extracellular devices has long suffered from the problem of low signal-to-noise ratios and changes in signal shapes due to the presence of highly dispersive dielectric medium in the neuron-microelectrode cleft. This has made it difficult to correlate the extracellularly recorded signals with the intracellular signals recorded using conventional patch-clamp electrophysiology. For bringing about an improvement in the signal-to-noise ratio of the signals recorded on the extracellular microelectrodes and to explore strategies for engineering the neuron-electrode interface there exists a need to model, simulate and characterize the cell-sensor interface to better understand the mechanism of signal transduction across the interface. Efforts to date for modeling the neuron-electrode interface have primarily focused on the use of point or area contact linear equivalent circuit models for a description of the interface with an assumption of passive linearity for the dynamics of the interfacial medium in the cell-electrode cleft. In this dissertation, results are presented from a nonlinear dynamic characterization of the neuroelectronic junction based on Volterra-Wiener modeling which showed that the process of signal transduction at the interface may have nonlinear contributions from the interfacial medium. An optimization based study of linear equivalent circuit models for representing signals recorded at the neuron-electrode interface subsequently proved conclusively that the process of signal transduction across the interface is indeed nonlinear. Following this a theoretical framework for the extraction of the complex nonlinear material parameters of the interfacial medium like the dielectric permittivity, conductivity and diffusivity tensors based on dynamic nonlinear Volterra-Wiener modeling was developed. Within this framework, the use of Gaussian bandlimited white noise for nonlinear impedance spectroscopy was shown to offer considerable advantages over the use of sinusoidal inputs for nonlinear harmonic analysis currently employed in impedance characterization of nonlinear electrochemical systems. Signal transduction at the neuron-microelectrode interface is mediated by the interfacial medium confined to a thin cleft with thickness on the scale of 20-110 nm giving rise to Knudsen numbers (ratio of mean free path to characteristic system length) in the range of 0.015 and 0.003 for ionic electrodiffusion. At these Knudsen numbers, the continuum assumptions made in the use of Poisson-Nernst-Planck system of equations for modeling ionic electrodiffusion are not valid. Therefore, a lattice Boltzmann method (LBM) based multiphysics solver suitable for modeling ionic electrodiffusion at the mesoscale neuron-microelectrode interface was developed. Additionally, a molecular speed dependent relaxation time was proposed for use in the lattice Boltzmann equation. Such a relaxation time holds promise for enhancing the numerical stability of lattice Boltzmann algorithms as it helped recover a physically correct description of microscopic phenomena related to particle collisions governed by their local density on the lattice. Next, using this multiphysics solver simulations were carried out for the charge relaxation dynamics of an electrolytic nanocapacitor with the intention of ultimately employing it for a simulation of the capacitive coupling between the neuron and the planar microelectrode on a microelectrode array (MEA). Simulations of the charge relaxation dynamics for a step potential applied at t = 0 to the capacitor electrodes were carried out for varying conditions of electric double layer (EDL) overlap, solvent viscosity, electrode spacing and ratio of cation to anion diffusivity. For a large EDL overlap, an anomalous plasma-like collective behavior of oscillating ions at a frequency much lower than the plasma frequency of the electrolyte was observed and as such it appears to be purely an effect of nanoscale confinement. Results from these simulations are then discussed in the context of the dynamics of the interfacial medium in the neuron-microelectrode cleft. In conclusion, a synergistic approach to engineering the neuron-microelectrode interface is outlined through a use of the nonlinear dynamic modeling, simulation and characterization tools developed as part of this dissertation research.
Ph.D.
Doctorate
Physics
Sciences
Physics
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HUANG, WEI-CHIANG, and 黃韋強. "IrO2 Nanotube Arrays as Stimulation Electrodes for Implantable Neuroelectronics." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/66hfry.

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碩士
國立臺北科技大學
材料科學與工程研究所
106
We develop chemical bath deposition processes for conformal IrO2 depositions on TiO2 nanotube arrays. In addition, we develop an anodization process which can control tube diameters and densities of TiO2 nanotube arrays. These IrO2 nanotube arrays undergo electrochemical analysis in charge storage capacity (CSC) and electrochemical impedance to evaluate its potential as stimulation electrodes for implantable devices. Images from electron microscopes confirm the formation of uniform IrO2 on both internal and external surface of nanotubes. In addition, the cycling lifetime of IrO2 nanotube arrays is evaluated by performing CV scans for 1,000 cycles with a scan rate of 0.1 V/s. The IrO2 nanotube arrays reveal large CSC values and low electrochemical impedances which are attributed to hollow tubular nanostructure with IrO2 deposition.
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George, Jude Baby. "Neuro-electronic Hybrid Systems." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5091.

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Artificial neural networks have provided a powerful motif to implement extremely complex functions of high dimensional inputs like images and text. These are now being used to directly control robotic systems, to enable them to work in natural environments, with an aim to create human-like capabilities. However, even the most advanced state-of-the-art deep neural inference engine chip is still multiple orders of magnitude more energy inefficient compared to biological neurons. For instance, the active human brain consumes only about 15W of power and has about 85 billion neurons with an equivalent transistor count of about a trillion. In contrast, the latest silicon chip has about 15 billion transistors and consumes about 250 watts of power. The energy efficiency of modern electronics-based computing is at least 1000x to 100000x worse than biological computation and this gap will take several more decades to bridge. Given that neuronal networks in the brain have an incredible ability to do complex pattern recognition very energy efficiently, can we recruit them in for our computational systems – rather than trying to mimic their capabilities in silicon? This thesis explores and addresses some of the technological challenges to create such neuro-electronics hybrid systems. The nerve tissue as a computational element can be thought of a multi-input, multi-output information transformation unit and if this transformation characteristics can be understood, and be used stably and reliably, then this unit can be made part of a hybrid computational chain, consisting of a mix of biology and electronics. There are several challenges to overcome to make this possible: a) Growth and maintenance of nerve tissue for sufficiently long periods in an energy efficient manner to ensure their use for specific missions, b) The engineering of high dimensional stimulation and recording from this nerve tissue to allow it to be used in hybrid systems, c) Information encoding and decoding to and from the tissue, d) Understanding the tissues’ information transformation capability and exploring ways to modify/train this and e)Studying the tissue’s information transformation capabilities as a function of time (i.e. its stability, reliability etc). The thesis mainly explores items c) and d) above: namely information transformation to/from the nerve tissue from the external world, and experimental studies in training the nerve tissue to change its functional behavior, leading to some insights into how biological neuronal networks can be leveraged for neuro-electronic hybrid systems. First, we describe a new method for encoding external stimuli inputs to a neuronal system and decoding the tissue outputs to be interpretable by the external systems. We use the framework of Liquid State Machine to model and understand the computational ability of the nerve tissue. Our proposed encoding method is able to encode much more inputs in a systematic manner when compared to other previous works. Our output decoder is also much simpler and efficient when compared to other similar works. We then demonstrate a real-time closed loop system with the nerve tissue on a multi-electrode array (MEA), controlling an external toy robot to move around avoiding obstacles. This showed the consistency and stability of responses from the network and the ability of the decoding scheme to map noisy tissue outputs to stable control commands to the robot, over a period of time. In order to better understand the computational model for such a hybrid system, we next study context dependent computational capability of the neuronal network in the MEA. Our experiments show that the neuronal network has an inherent ability to do context dependent computation, in a robust way, by virtue of its random structure. Probabilistic connections of the network give rise to conjunctive neurons that results in emergent properties of robustly encoding the input stimuli and grouping of related inputs. An appropriate framework for modeling the computation performed by the tissue network is that of reservoir computing or liquid state machine. We perform computer simulations with such a model to show that the tissue’s transformation capabilities will be robust against loss of connections as well as neurons. We then study the efficacy of prior reported training protocols, based on theta burst stimuli, for a neuronal network in the MEA which attempt to change its stimulus response. We analyze the principal components of the high-dimensional MEA recording of spontaneous activity, pre and post this training stimuli. Using this technique, we determine that the network maintains homeostasis in its activity for data recorded over an 8 hr period. We also find that this homeostasis is temporarily disturbed by the theta burst stimuli but is again restored after some time post the removal of stimuli. However, some electrodes show a more permanent change in their response to specific input stimulus, indicating memory of the training event in the network along specific pathways. These experiments confirm that local plasticity can indeed be achieved via specific stimulus patterns as reported elsewhere, but the network overall tends to maintain homeostasis, indicating that creating large scale network wide changes through external stimulus via MEA is a difficult challenge. This leads us to conclude that the best way to train the hybrid system, given the limitations of current technology, is to not train the nerve tissue, but restrict the training to the output perceptron layer. With a large enough random tissue network, it will be able to hold a vast reservoir of functions, from which the desired function can be teased out via appropriate training of the output perceptron layer, as proposed by the reservoir computing model.
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Books on the topic "NeuroElectronics"

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Capadona, Jeffrey R., and Ulrich G. Hofmann, eds. Bridging the Gap in Neuroelectronic Interfaces. Frontiers Media SA, 2020. http://dx.doi.org/10.3389/978-2-88963-850-5.

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Book chapters on the topic "NeuroElectronics"

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Samsonovich, A. V. "Molecular-Level Neuroelectronics." In Topics in Molecular Organization and Engineering, 227–66. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3392-0_26.

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Keiper, Adam. "The Age of Neuroelectronics." In Nanotechnology, the Brain, and the Future, 115–46. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-1787-9_7.

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Rosahl, S. K. "Neuroelectronic interfaces with the central nervous systems – ethical issues." In IFMBE Proceedings, 48–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03889-1_13.

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Yao, Dickson R., and Dion Khodagholy. "Translational Neuroelectronics." In Introduction to Bioelectronics, 1–32. AIP Publishing, 2022. http://dx.doi.org/10.1063/9780735424470_007.

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Keiper, Adam. "The Age of Neuroelectronics." In Advances in Neurotechnology: Ethical, Legal, and Social Issues, 143–74. CRC Press, 2012. http://dx.doi.org/10.1201/b11861-11.

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Conference papers on the topic "NeuroElectronics"

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Khodagholy, Dion. "Translational Neuroelectronics." In Neural Interfaces and Artificial Senses. València: Fundació Scito, 2021. http://dx.doi.org/10.29363/nanoge.nias.2021.006.

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Khodagholy, Dion. "Translational Neuroelectronics." In nanoGe Spring Meeting 2022. València: Fundació Scito, 2022. http://dx.doi.org/10.29363/nanoge.nsm.2022.228.

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Whitchurch, Ashwin, and Vijay K. Varadan. "Neuroelectronics and neurosurgery." In Smart Structures and Materials, edited by Vijay K. Varadan. SPIE, 2006. http://dx.doi.org/10.1117/12.668749.

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Khodagholy, Dion. "Translational neuroelectronics (Conference Presentation)." In Organic and Hybrid Sensors and Bioelectronics XV, edited by Ruth Shinar, Ioannis Kymissis, and Emil J. List-Kratochvil. SPIE, 2022. http://dx.doi.org/10.1117/12.2642281.

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Di Lauro, Michele, Elena Zucchini, Anna De Salvo, Emanuela Delfino, Michele Bianchi, Mauro Murgia, Stefano Carli, Fabio Biscarini, and Luciano Fadiga. "Technological Innovations and Translational Perspectives of Organic Neuroelectronics." In Organic Bioelectronics Conference 2022. València: Fundació Scito, 2022. http://dx.doi.org/10.29363/nanoge.obe.2022.014.

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Chintakuntla, Ritesh R., Jose K. Abraham, and Vijay K. Varadan. "Neuroelectronics and modeling of electrical signals for monitoring and control of Parkinson's disease." In SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, edited by Vijay K. Varadan. SPIE, 2009. http://dx.doi.org/10.1117/12.829927.

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Barille, R., S. Ahmadi Kandjani, S. Dabos-Seignon, J. M. Nunzi, F. Letournel, E. Ortyl, and S. Kucharski. "Neuron growth engineering on a photoinduced surface relief grating: a tool for plastic neuroelectronics." In Photonics Europe, edited by Romualda Grzymala and Olivier Haeberle. SPIE, 2006. http://dx.doi.org/10.1117/12.663527.

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Ham, Donhee. "Neuroelectronic interface and neuromorphic engineering." In Neuromorphic Materials, Devices, Circuits and Systems. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2023. http://dx.doi.org/10.29363/nanoge.neumatdecas.2023.058.

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Slaughter, Gymama, Matthew Robinson, Joel Tyson, and Chen J. Zhang. "Neuroelectronic device process development and challenge." In SPIE Advanced Lithography, edited by Andreas Erdmann and Jongwook Kye. SPIE, 2017. http://dx.doi.org/10.1117/12.2256297.

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Abraham, Jose K., Ritesh Chintakuntla, Hargsoon Yoon, and Vijay K. Varadan. "Nanowire Integrated Microelectrode Arrays for Neuroelectronic Applications." In 2007 IEEE Region 5 Technical Conference. IEEE, 2007. http://dx.doi.org/10.1109/tpsd.2007.4380378.

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