Modelling small-patterned neuronal networks coupled to microelectrode arrays.
Identifieur interne : 001364 ( Main/Exploration ); précédent : 001363; suivant : 001365Modelling small-patterned neuronal networks coupled to microelectrode arrays.
Auteurs : Paolo Massobrio [Italie] ; Sergio MartinoiaSource :
- Journal of neural engineering [ 1741-2560 ] ; 2008.
English descriptors
- KwdEn :
- MESH :
- instrumentation : Electric Stimulation.
- methods : Electric Stimulation.
- physiology : Action Potentials, Nerve Net, Neurons.
- Animals, Cells, Cultured, Computer Simulation, Microelectrodes, Models, Neurological, Rats.
Abstract
Cultured neurons coupled to planar substrates which exhibit 'well-defined' two-dimensional network architectures can provide valuable insights into cell-to-cell communication, network dynamics versus topology, and basic mechanisms of synaptic plasticity and learning. In the literature several approaches were presented to drive neuronal growth, such as surface modification by silane chemistry, photolithographic techniques, microcontact printing, microfluidic channel flow patterning, microdrop patterning, etc. This work presents a computational model fit for reproducing and explaining the dynamics exhibited by small-patterned neuronal networks coupled to microelectrode arrays (MEAs). The model is based on the concept of meta-neuron, i.e., a small spatially confined number of actual neurons which perform single macroscopic functions. Each meta-neuron is characterized by a detailed morphology, and the membrane channels are modelled by simple Hodgkin-Huxley and passive kinetics. The two main findings that emerge from the simulations can be summarized as follows: (i) the increasing complexity of meta-neuron morphology reflects the variations of the network dynamics as a function of network development; (ii) the dynamics displayed by the patterned neuronal networks considered can be explained by hypothesizing the presence of several short- and a few long-term distance interactions among small assemblies of neurons (i.e., meta-neurons).
DOI: 10.1088/1741-2560/5/3/008
PubMed: 18756034
Affiliations:
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Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Modelling small-patterned neuronal networks coupled to microelectrode arrays.</title><author><name sortKey="Massobrio, Paolo" sort="Massobrio, Paolo" uniqKey="Massobrio P" first="Paolo" last="Massobrio">Paolo Massobrio</name><affiliation wicri:level="1"><nlm:affiliation>Neuroengineering and Bio-Nano Technology Group, Department of Biophysical and Electronic Engineering (DIBE), University of Genova, Via Opera Pia 11a, 16145 Genova, Italy. paolo.massobrio@unige.it</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>Neuroengineering and Bio-Nano Technology Group, Department of Biophysical and Electronic Engineering (DIBE), University of Genova, Via Opera Pia 11a, 16145 Genova</wicri:regionArea><wicri:noRegion>16145 Genova</wicri:noRegion></affiliation></author><author><name sortKey="Martinoia, Sergio" sort="Martinoia, Sergio" uniqKey="Martinoia S" first="Sergio" last="Martinoia">Sergio Martinoia</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2008">2008</date><idno type="doi">10.1088/1741-2560/5/3/008</idno><idno type="RBID">pubmed:18756034</idno><idno type="pmid">18756034</idno><idno type="wicri:Area/PubMed/Corpus">000420</idno><idno type="wicri:Area/PubMed/Curation">000420</idno><idno type="wicri:Area/PubMed/Checkpoint">000415</idno><idno type="wicri:Area/Ncbi/Merge">000551</idno><idno type="wicri:Area/Ncbi/Curation">000551</idno><idno type="wicri:Area/Ncbi/Checkpoint">000551</idno><idno type="wicri:doubleKey">1741-2560:2008:Massobrio P:modelling:small:patterned</idno><idno type="wicri:Area/Main/Merge">001381</idno><idno type="wicri:Area/Main/Curation">001364</idno><idno type="wicri:Area/Main/Exploration">001364</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Modelling small-patterned neuronal networks coupled to microelectrode arrays.</title><author><name sortKey="Massobrio, Paolo" sort="Massobrio, Paolo" uniqKey="Massobrio P" first="Paolo" last="Massobrio">Paolo Massobrio</name><affiliation wicri:level="1"><nlm:affiliation>Neuroengineering and Bio-Nano Technology Group, Department of Biophysical and Electronic Engineering (DIBE), University of Genova, Via Opera Pia 11a, 16145 Genova, Italy. paolo.massobrio@unige.it</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>Neuroengineering and Bio-Nano Technology Group, Department of Biophysical and Electronic Engineering (DIBE), University of Genova, Via Opera Pia 11a, 16145 Genova</wicri:regionArea><wicri:noRegion>16145 Genova</wicri:noRegion></affiliation></author><author><name sortKey="Martinoia, Sergio" sort="Martinoia, Sergio" uniqKey="Martinoia S" first="Sergio" last="Martinoia">Sergio Martinoia</name></author></analytic><series><title level="j">Journal of neural engineering</title><idno type="ISSN">1741-2560</idno><imprint><date when="2008" type="published">2008</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Action Potentials (physiology)</term><term>Animals</term><term>Cells, Cultured</term><term>Computer Simulation</term><term>Electric Stimulation (instrumentation)</term><term>Electric Stimulation (methods)</term><term>Microelectrodes</term><term>Models, Neurological</term><term>Nerve Net (physiology)</term><term>Neurons (physiology)</term><term>Rats</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="en"><term>Electric Stimulation</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Electric Stimulation</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Action Potentials</term><term>Nerve Net</term><term>Neurons</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Animals</term><term>Cells, Cultured</term><term>Computer Simulation</term><term>Microelectrodes</term><term>Models, Neurological</term><term>Rats</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Cultured neurons coupled to planar substrates which exhibit 'well-defined' two-dimensional network architectures can provide valuable insights into cell-to-cell communication, network dynamics versus topology, and basic mechanisms of synaptic plasticity and learning. In the literature several approaches were presented to drive neuronal growth, such as surface modification by silane chemistry, photolithographic techniques, microcontact printing, microfluidic channel flow patterning, microdrop patterning, etc. This work presents a computational model fit for reproducing and explaining the dynamics exhibited by small-patterned neuronal networks coupled to microelectrode arrays (MEAs). The model is based on the concept of meta-neuron, i.e., a small spatially confined number of actual neurons which perform single macroscopic functions. Each meta-neuron is characterized by a detailed morphology, and the membrane channels are modelled by simple Hodgkin-Huxley and passive kinetics. The two main findings that emerge from the simulations can be summarized as follows: (i) the increasing complexity of meta-neuron morphology reflects the variations of the network dynamics as a function of network development; (ii) the dynamics displayed by the patterned neuronal networks considered can be explained by hypothesizing the presence of several short- and a few long-term distance interactions among small assemblies of neurons (i.e., meta-neurons).</div></front></TEI><affiliations><list><country><li>Italie</li></country></list><tree><noCountry><name sortKey="Martinoia, Sergio" sort="Martinoia, Sergio" uniqKey="Martinoia S" first="Sergio" last="Martinoia">Sergio Martinoia</name></noCountry><country name="Italie"><noRegion><name sortKey="Massobrio, Paolo" sort="Massobrio, Paolo" uniqKey="Massobrio P" first="Paolo" last="Massobrio">Paolo Massobrio</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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