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Nicholas T. Carnevale
Researcher at Yale University
Publications - 47
Citations - 6837
Nicholas T. Carnevale is an academic researcher from Yale University. The author has contributed to research in topics: Dendritic spine & Biology. The author has an hindex of 25, co-authored 41 publications receiving 6230 citations. Previous affiliations of Nicholas T. Carnevale include Santa Clara Valley Medical Center & Duke University.
Papers
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Journal ArticleDOI
Effects of low frequency electric fields on synaptic integration in hippocampal CA1 pyramidal neurons: implications for power line emissions.
Francesco Cavarretta,Francesco Cavarretta,Nicholas T. Carnevale,Domenico Tegolo,Michele Migliore +4 more
TL;DR: The results show that individual neuronal morphology, ion channel dendritic distribution, and alignment with the electric field are major determinants of overall effects, and provide a physiologically plausible explanation of why experimental findings can appear to be small and difficult to reproduce, yet deserve serious consideration.
Book ChapterDOI
Model Structure Analysis in NEURON
TL;DR: How ModelView contributes to the understanding of models is illustrated and its utility as a neuroinformatics tool for analyzing models in online databases and as a means for facilitating interoperability among simulators in computational neuroscience is discussed.
Journal ArticleDOI
Modernizing the NEURON Simulator for Sustainability, Portability, and Performance
Omar Awile,Pramod Kumbhar,Nicolas Cornu,Salvador Dura-Bernal,James G. King,Olli Lupton,Ioannis Magkanaris,Robert A. McDougal,Adam J. H. Newton,Fernando Pereira,Alexandru Florin Savulescu,Nicholas T. Carnevale,William W. Lytton,Michael L. Hines,Felix Schürmann +14 more
TL;DR: These efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.
Journal ArticleDOI
Hebbian learning is jointly controlled by electrotonic and input structure
TL;DR: It is shown that the synaptic weight vector M converges toward the principal eigenvector of the matrix [〈xj(xk*ν˜kj)〉], where xj and xk represent presynaptic activity, and * is the convolution operator.
Journal ArticleDOI
Kinetics of diffusion in a spherical cell. II. Solute buffering included
TL;DR: It is demonstrated that a few compartments designed on the basis of diffusion alone (Carnevale and Rosenthal, 1992) may be a satisfactory framework for a model that includes bimolecular buffering, i.e., approximating it by a unimolecular non-saturating process that immobilizes solute.