Research & Publications
I have focused on the spatiotemporal dynamics and functional consequences of these interactions, which are governed by mechanisms that span a wide range of temporal and spatial scales and are often highly nonlinear. Furthermore they are constrained by the complex architectures of neuronal networks, individual cells, and subcellular structures.
Extensive Research Description
Some of my recent accomplishments and ongoing projects include:
* Developing a theoretical basis for predicting the outcome of a proposed cellular mechanism of learning. Tsai, K.Y., Carnevale, N.T., and Brown, T.H. Hebbian learning is jointly controlled by electrotonic and input structure. Network 5:1-19, 1994.
* Creating and applying new approaches to the analysis and understanding of electrical signaling in real neurons.
One example is the electrotonic transformation, an intuitive, empirically-based method for deriving insight to the functional consequences of neuronal anatomy and biophysical properties.
Most recently we have applied the transformation to the study of the principal neurons in the rat hippocampus, and extended its use to the interpretation of electrotonic architecture in the context of the circuitry in which neurons are embedded
Tsai, K.Y., Carnevale, N.T., Claiborne, B.J., and Brown, T.H. Efficient mapping from neuroanatomical to electrotonic space. Network 5:21-46, 1994.
Carnevale, N.T., Tsai, K.Y., Claiborne, B.J., and Brown, T.H. The electrotonic transformation: a tool for relating neuronal form to function. In: Advances in Neural Information Processing Systems, vol. 7, edited by Tesauro, G., Touretzky, D.S., and Leen, T.K. Cambridge, MA, MIT Press, 1995, p. 69-76. Preprint viewable and downloadable.
O'Boyle, M.P., Carnevale, N.T., Claiborne, B.J., and Brown, T.H. A new graphical approach for visualizing the relationship between anatomical and electrotonic structure. In: Computational Neuroscience: Trends in Research 1995, edited by J.M. Bower. San Diego: Academic Press, 1996, p. 423-428. Preprint available.
Carnevale, N.T., Tsai, K.Y., Claiborne, B.J., and Brown, T.H. Comparative electrotonic analysis of three classes of rat hippocampal neurons. J. Neurophysiol. 78:703-720, 1997.
Carnevale, N.T., O'Boyle, M.P., and Claiborne, B.J. Dendritic branching compensates for synaptic attenuation in granule cells of the rat dentate gyrus. Neuroscience Society Abstracts 24:1814, 1998.
* Collaborating with Michael Hines and John Moore in the support and enhancement of NEURON:
One practical outcome of this collaboration has been porting this sophisticated simulation environment from UNIX / Xwindows to MS-Windows and the Macintosh.
This put the leading software package for empirically-based modeling of individual neurons and networks of neurons within reach of everyone who is engaged in neuroscience research and teaching.
Hines, M. and Carnevale, N.T. Computer modeling methods for neurons. In: The Handbook of Brain Theory and Neural Networks, edited by M.A. Arbib. Cambridge, MA: MIT Press, 1995, p. 226-230. Preprint available as arbib.ps.Z, (a compressed PostScript file for ghostview) and arbib.zip (a pkzipped pdf file for Adobe Acrobat).
Carnevale, N.T., Tsai, K.Y., and Hines, M.L. The Electrotonic Workbench Neuroscience Society Abstracts 22:1741, 1996. Poster viewable in HTML format. Hines, M.L. and Carnevale, N.T. The NEURON simulation environment. Neural Computation 9:1179-1209, 1997. Preprint viewable and downloadable.
* Producing and directing short courses on NEURON that have been presented at Yale, annual meetings of the Society for Neuroscience, and the San Diego Supercomputer Center. Plans are now under way for future offerings of these courses plus new courses on related topics--see http://www.neuron.yale.edu/neuron/courses.html
* Developing new courses for undergraduate and graduate students that use simulation exercises implemented with NEURON to facilitate learning the physiology of excitable cells (see http://www.neuron.yale.edu/neuron/edu/nrnlab.htm and http://www.neuron.yale.edu/neuron/classes/445b/announce.htm ).
* A project to create a published set of laboratory simulation exercises, with an accompanying manual, that will advance neuroscience education by providing an intensive, interactive exploration of the processes that underlie neuronal function. More information about this NSF-supported collaboration with David Jaffe and Michael Hines is posted at http://www.neuron.yale.edu/neuron/edu/nrnlab.htm
Cell Membrane Permeability; Membrane Potentials; Nervous System Physiological Phenomena; Neural Networks, Computer
- Interfacing a real-time dynamic clamp system with neuron simulation software in living cellsNowak M, Korbel L, Kane A, Panama B, Hines M, Carnevale N, Bett G, Rasmusson R. Interfacing a real-time dynamic clamp system with neuron simulation software in living cells Biophysical Journal 2022, 121: 391a. DOI: 10.1016/j.bpj.2021.11.818.
- Web of Trust Tool for Gateway User VettingYoshimoto K, Carnevale N, Sivagnanam S, Majumdar A, Miller M. Web of Trust Tool for Gateway User Vetting 2019, 26. DOI: 10.1145/3332186.3332221.
- The Neuroscience Gateway: Enabling Large Scale Modeling and Data Processing in NeuroscienceSivagnanam S, Yoshimoto K, Carnevale N, Majumdar A. The Neuroscience Gateway: Enabling Large Scale Modeling and Data Processing in Neuroscience 2018, 52. DOI: 10.1145/3219104.3219139.
- 2 The Neuron Simulation Environment in Epilepsy ResearchCarnevale N, Hines M. 2 The Neuron Simulation Environment in Epilepsy Research 2008, 18-33. DOI: 10.1016/b978-012373649-9.50005-3.
- The NEURON BookCarnevale N, Hines M. The NEURON Book 2006 DOI: 10.1017/cbo9780511541612.
- Discrete event simulation in the NEURON environmentHines M, Carnevale N. Discrete event simulation in the NEURON environment Neurocomputing 2004, 58: 1117-1122. DOI: 10.1016/j.neucom.2004.01.175.
- Machines That RememberCarnevale N, Claiborne B. Machines That Remember PsycCRITIQUES 1995, 40: 267-268. DOI: 10.1037/003505.
- Qualitative Electrotonic Comparison of Three Classes of Hippocampal Neurons in the RatCarnevale N, Tsai K, Claiborne B, Brown T. Qualitative Electrotonic Comparison of Three Classes of Hippocampal Neurons in the Rat 1995, 67-72. DOI: 10.1007/978-1-4615-2235-5_11.
- Hebbian learning is jointly controlled by electrotonic and input structureTsai K, Carnevale N, Brown T. Hebbian learning is jointly controlled by electrotonic and input structure Network Computation In Neural Systems 1994, 5: 1-19. DOI: 10.1088/0954-898x_5_1_001.
- Efficient mapping from neuroanatomical to electrotonic spaceTsai K, Carnevale N, Claiborne B, Brown T. Efficient mapping from neuroanatomical to electrotonic space Network Computation In Neural Systems 1994, 5: 21-46. DOI: 10.1088/0954-898x_5_1_002.
- Simulating neurons with SABERCarnevale N, Woolf T, Shepherd G. Simulating neurons with SABER 1988, 1508 vol.3. DOI: 10.1109/iembs.1988.95353.