FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2910554039> ?p ?o ?g. }

• W2910554039 abstract "Event Abstract Back to Event Quantitative investigation of geometrical axon guidance via PDMS microstructures for small-scale well-defined neural networks. Csaba Forro1*, Stephan Ihle1, Sean Weaver1, Greta Thompson-Steckel1, Janos Voros1 and Serge Weydert1 1 Institut für Biomedizinische Technik, ETH Zürich, Switzerland In the search of unveiling the fundamental mechanisms of brain function and brain computation, we believe that the right scale to look at is the small network scale. Indeed, elementary and identifiable computations are performed by only a handful of neurons, for example time-differentiation of signals to detect moving objects, etc. Furthermore, complex behavior can be built from only 300 neurons as shown by the C.Elegans worm, which has some form of memory for up to 48 hours. Current microelectrode technologies paired with in-vitro neuroscience offer an appealing solution. Indeed, advances in microelectrode arrays allow to probe neural activity with a temporal resolution of tens of microseconds but also with a spatial resolution of tens of micrometers. In order to disentangle the immense complexity and connectedness of neural structures in-vivo, in-vitro neuroscience provides tools to build neural networks from the bottom up in a controlled fashion. This is of paramount importance to eliminate sample-to-sample variance and thus irreproducibility of the experiments. Microstructures made of Polydimethyilsiloxane (PDMS) provide considerable advantages as a neural network shaping tool over microelectrode arrays. The physical compartmentalization of cell bodies is solved by making holes in a thin PDMS film. In order to form a network, these holes can be connected by tunnels (microchannels) which are less than 10 microns in height to allow only neurites and axons to grow into them. This type of structure has gained interest because of the ability of the microchannel to favor axon growth in a given direction depending on its geometrical shape. However, there hasn't been a thorough investigation and statistical evaluation of the guiding power of such microchannels. We have investigated 10 different types of microchannels inspired by various axon guiding principles. Large PDMS structures containing various repetitions of a channel type were placed on Wilco Dishes coated with Poly-D-Lysine. Cells were seeded in compartments connected by one type of channel, and we sequentially delivered red and green fluorescent protein encoding adeno-associated viruses in the sequential compartments. This allowed to distinguish axons growing forward from axons growing backward in the channels. Green-Red pairs of compartments were imaged with a Confocal Laser Scanning Microscope (CLSM). We imaged more than 250 such pairs per channel type for each Day In Vitro (DIV) 6, 12 and 18. The images were segmented by training a Deep Convolutional Neural Network. We present a channel shape which provides over 92% forward guidance even after DIV 18. A small scale circular network was built with 4 nodes and connected in an anti-clockwise direction. Keywords: PDMS, Microstructures, neural networks, CLSM., axon guidance Conference: MEA Meeting 2018 | 11th International Meeting on Substrate Integrated Microelectrode Arrays, Reutlingen, Germany, 4 Jul - 6 Jul, 2018. Presentation Type: Poster Presentation Topic: Neural Networks Citation: Forro C, Ihle S, Weaver S, Thompson-Steckel G, Voros J and Weydert S (2019). Quantitative investigation of geometrical axon guidance via PDMS microstructures for small-scale well-defined neural networks.. Conference Abstract: MEA Meeting 2018 | 11th International Meeting on Substrate Integrated Microelectrode Arrays. doi: 10.3389/conf.fncel.2018.38.00076 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 18 Mar 2018; Published Online: 17 Jan 2019. * Correspondence: Mr. Csaba Forro, Institut für Biomedizinische Technik, ETH Zürich, Gloriastrasse, Switzerland, forro@biomed.ee.ethz.ch Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Csaba Forro Stephan Ihle Sean Weaver Greta Thompson-Steckel Janos Voros Serge Weydert Google Csaba Forro Stephan Ihle Sean Weaver Greta Thompson-Steckel Janos Voros Serge Weydert Google Scholar Csaba Forro Stephan Ihle Sean Weaver Greta Thompson-Steckel Janos Voros Serge Weydert PubMed Csaba Forro Stephan Ihle Sean Weaver Greta Thompson-Steckel Janos Voros Serge Weydert Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page." @default.
• W2910554039 created "2019-01-25" @default.
• W2910554039 creator A5019091239 @default.
• W2910554039 creator A5020392838 @default.
• W2910554039 creator A5035701091 @default.
• W2910554039 creator A5051062804 @default.
• W2910554039 creator A5067888809 @default.
• W2910554039 creator A5073920618 @default.
• W2910554039 date "2018-01-01" @default.
• W2910554039 modified "2023-10-18" @default.
• W2910554039 title "Quantitative investigation of geometrical axon guidance via PDMS microstructures for small-scale well-defined neural networks." @default.
• W2910554039 doi "https://doi.org/10.3389/conf.fncel.2018.38.00076" @default.
• W2910554039 hasPublicationYear "2018" @default.
• W2910554039 type Work @default.
• W2910554039 sameAs 2910554039 @default.
• W2910554039 citedByCount "0" @default.
• W2910554039 crossrefType "journal-article" @default.
• W2910554039 hasAuthorship W2910554039A5019091239 @default.
• W2910554039 hasAuthorship W2910554039A5020392838 @default.
• W2910554039 hasAuthorship W2910554039A5035701091 @default.
• W2910554039 hasAuthorship W2910554039A5051062804 @default.
• W2910554039 hasAuthorship W2910554039A5067888809 @default.
• W2910554039 hasAuthorship W2910554039A5073920618 @default.
• W2910554039 hasBestOaLocation W29105540391 @default.
• W2910554039 hasConcept C111670793 @default.
• W2910554039 hasConcept C118403218 @default.
• W2910554039 hasConcept C121332964 @default.
• W2910554039 hasConcept C154945302 @default.
• W2910554039 hasConcept C169760540 @default.
• W2910554039 hasConcept C17525397 @default.
• W2910554039 hasConcept C2778755073 @default.
• W2910554039 hasConcept C3832189 @default.
• W2910554039 hasConcept C41008148 @default.
• W2910554039 hasConcept C50644808 @default.
• W2910554039 hasConcept C62520636 @default.
• W2910554039 hasConcept C86803240 @default.
• W2910554039 hasConcept C95967509 @default.
• W2910554039 hasConceptScore W2910554039C111670793 @default.
• W2910554039 hasConceptScore W2910554039C118403218 @default.
• W2910554039 hasConceptScore W2910554039C121332964 @default.
• W2910554039 hasConceptScore W2910554039C154945302 @default.
• W2910554039 hasConceptScore W2910554039C169760540 @default.
• W2910554039 hasConceptScore W2910554039C17525397 @default.
• W2910554039 hasConceptScore W2910554039C2778755073 @default.
• W2910554039 hasConceptScore W2910554039C3832189 @default.
• W2910554039 hasConceptScore W2910554039C41008148 @default.
• W2910554039 hasConceptScore W2910554039C50644808 @default.
• W2910554039 hasConceptScore W2910554039C62520636 @default.
• W2910554039 hasConceptScore W2910554039C86803240 @default.
• W2910554039 hasConceptScore W2910554039C95967509 @default.
• W2910554039 hasLocation W29105540391 @default.
• W2910554039 hasOpenAccess W2910554039 @default.
• W2910554039 hasPrimaryLocation W29105540391 @default.
• W2910554039 hasRelatedWork W1884938962 @default.
• W2910554039 hasRelatedWork W1977610025 @default.
• W2910554039 hasRelatedWork W1978975607 @default.
• W2910554039 hasRelatedWork W1988145163 @default.
• W2910554039 hasRelatedWork W2000884802 @default.
• W2910554039 hasRelatedWork W2034634037 @default.
• W2910554039 hasRelatedWork W2120046534 @default.
• W2910554039 hasRelatedWork W3022880777 @default.
• W2910554039 hasRelatedWork W4223923841 @default.
• W2910554039 hasRelatedWork W4283785092 @default.
• W2910554039 hasVolume "12" @default.
• W2910554039 isParatext "false" @default.
• W2910554039 isRetracted "false" @default.
• W2910554039 magId "2910554039" @default.
• W2910554039 workType "article" @default.