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Seamless Integration of Neurons with CMOS Microele.. (NeuroCMOS)
Seamless Integration of Neurons with CMOS Microelectronics
(NeuroCMOS)
Date du début: 1 juin 2011,
Date de fin: 31 mai 2016
PROJET
TERMINÉ
We propose to seamlessly integrate advanced microelectronics and living neuronal cells in a comprehensive and interdisciplinary approach to significantly advance the understanding of neuronal behaviour. The project includes (a) the development of a novel multifunctional microelectronics chip platform in complementary metal oxide semiconductor (CMOS) technology, which serves to enable (b) key neurobiological and neuromedical research on network dynamics and plasticity of rodent neuronal networks and visual encoding in retinae, and (c) the necessary concurrent development of algorithms and models to efficiently process and maximally harness the unprecedented quality of the obtained data.Neuronal or retinal preparations, such as acute and organotypic brain slices (retinae) or primary cultured, dissociated cells, will be directly placed or grown atop dedicated CMOS microelectronics chips. The chips will feature multiple functions, since neurons carry and pass signals to each other using electro-chemical mechanisms: electrophysiological recording & stimulation, in closed loop & real time, as well as highly spatially resolved impedance measurements and detection of neuroactive chemical compounds. The chips will be capable of delivering any of these functions to arbitrarily selectable individual cells or even subcellular units, and, at the same time, of interacting with a multitude of cells or complete neuronal networks. Along with imaging (light, fluorescence), pharmacological, and/or genetic methods, the developed chip platform will be used to study neuronal network dynamics, synaptic and axonal plasticity, relevant for many brain diseases, as well as visual encoding in the retina. Efficient data handling and spike sorting algorithms will be developed to facilitate these investigations. The multidimensional data will then be used to establish detailed models of neurons and neuronal networks.
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