nach oben

Neuroinformatics

Erschienen in:

01.12.2008

KInNeSS: A Modular Framework for Computational Neuroscience

Erschienen in: Neuroinformatics | Ausgabe 4/2008

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biologically-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalability, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multi-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions or ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further development of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effectively collaborate using a modern neural simulation platform.

Vorheriger Artikel Learning Vector Quantization Neural Networks Improve Accuracy of Transcranial Color-coded Duplex Sonography in Detection of Middle Cerebral Artery Spasm—Preliminary Report

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

http://www.neuroml.org

KInNeSS is licensed under the GNU general public license; © Anatoli Gorchetchnikov.

http://www.kinness.net

http://www.kinness.net/Docs/SANNDRA/html/index.html

SANNDRA is licensed under the GNU general public license; © Anatoli Gorchetchnikov.

http://kinness.net/Docs/KInNeSS/examples/Example_Receptor_Kinness.rar

http://www.kinness.net/Docs/KInNeSS/manual/index.html

http://www.kinness.net/kinness_tutorial.wmv

http://www.w3.org/TR/xslt.html

The equations in “XML Support” section follow sign conventions found in the literature. Note that in the KInNeSS interface, this convention is reversed.

When loading spatial input patterns from a .png file, the yellow channel represents the alpha channel.

The membrane potential outputted by KInNeSS is shifted by an amount equal to the leak potential of the compartment. For example, when the user sets the compartmental leakage reverse potential to -60 mV, a value of zero in the output file corresponds to a membrane potential of -60 mV.

http://www.kde.org

KDE always list native KDE classes with K in the front. All names that start with K and are followed by a capital letter are inherited from KDE native classes.

http://kinness.net/

See either ModelDB (https://senselab.med.yale.edu/modeldb/ShowModel.asp?model=113939) or the documentation section of http://www.kinness.net for the code and data.

https://computation.llnl.gov/casc/sundials/main.html

Abarbanel, H. D. I., Huerta, R., & Rabinovich, M. I. (2002). Dynamical model of long-term synaptic plasticity. P. Natl. Acad. Sci., 99, 10132–10137. doi:10.1073/pnas.132651299.CrossRef

Abbott, L. F., Sen, K., Varela, J. A., & Nelson, S. B. (1997). Synaptic depression and cortical gain control. Science, 275, 220–222. doi:10.1126/science.275.5297.221.PubMedCrossRef

Berzhanskaya, J., Gorchetchnikov, A., & Schiff, S. J. (2007). Switching between gamma and theta: dynamic network control using subthreshold electric fields. Neurocomputing, 70(10–12), 2091–2095. doi:10.1016/j.neucom.2006.10.124.PubMedCrossRef

Bi, G. Q., & Poo, M. M. (2001). Synaptic modification by correlated activity: Hebb’s postulate revisited. Annual Review of Neuroscience, 24, 139–166. doi:10.1146/annurev.neuro.24.1.139.PubMedCrossRef

Bower, J. M., & Beeman, D. (1998). The Book of GENESIS: exploring realistic neural models with the GEneral NEural SImulation System (2nd ed.). New York: Springer.

Bray, T., Paoli, J., Sperberg-McQueen, C. M., Maler, E., & Yergeau, F. (2006). Extensible Markup Language (XML) 1.0 (Fourth Edition) http://www.w3.org/TR/2006/REC-xml-20060816/#sec-origin-goals

Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J. M., et al. (2007). Simulation of networks of spiking neurons: a review of tools and strategies. The Journal of Comparative Neurology, 23(3), 349–398.

Cannon, R. C., Hasselmo, M. E., & Koene, R. A. (2002). From biophysics to behavior: Catacomb2 and the design of biologically plausible models for spatial navigation. Neuroinformatics, 1(1), 3–42. doi:10.1385/NI:1:1:003.CrossRef

Cannon, R. C., Gewaltig, M. O., Gleeson, P., Bhalla, U. S., Hines, M. L., Howell, F. H., et al. (2007). Interoperability of neuroscience modeling software: current status and future directions. Neuroinformatics, 5(2), 127–138. doi:10.1007/s12021-007-0004-5.PubMedCrossRef

Carnevale, N. T., & Hines, M. L. (2006). The neuron book. Cambridge: Cambridge University Press.

Crook, S., Gleeson, P., Howell, F., Svitak, J., & Silver, R. A. (2007). MorphML: level 1 of the NeuroML standards for neuronal morphology data and model specification. Neuroinformatics, 5(2), 96–104. doi:10.1007/s12021-007-0003-6.PubMedCrossRef

Ermentrout, B. (2002). Simulating, analyzing, and animating dynamical systems: a guide to XPPAUT for researchers and students. SIAM.

Ermentrout, B., & Kopell, N. (1986). Parabolic bursting in an excitable system coupled with slow oscillation. SIAM Journal on Applied Mathematics, 46, 233–252. doi:10.1137/0146017.CrossRef

Gewaltig, M. O., & Diesmann, M. (2007). NEST. Scholaropedia, 2(4), 1430.

Goddard, N. H., Hucka, M., Howell, F., Cornelis, H., Shankar, K., & Beeman, D. (2001). Towards NeuroML: model description methods for collaborative modeling in neuroscience. Philos. T. Roy. Soc. B, 356, 1209–1228. doi:10.1098/rstb.2001.0910.CrossRef

Gorchetchnikov, A. (2000). An approach to a biologically realistic simulation of natural memory, Master Thesis, Middle Tennessee State University, Murfreesboro, TN.

Gorchetchnikov, A., & Grossberg, S. (2007). Space, time and learning in the hippocampus: how fine spatial and temporal scales are expanded into population codes for behavioral control. Neural Networks, 20(2), 182–193. doi:10.1016/j.neunet.2006.11.007.PubMedCrossRef

Gorchetchnikov, A., & Hasselmo, M. E. (2002). A model of hippocampal circuitry mediating goal-driven navigation in a familiar environment. Neurocomputing, 44–46, 424–427. doi:10.1016/S0925-2312(02)00395-8.

Gorchetchnikov, A., & Hasselmo, M. E. (2005). A biophysical implementation of a bidirectional graph search algorithm to solve multiple goal navigation tasks. Connection Science, 17(1–2), 145–166. doi:10.1080/09540090500140925.CrossRef

Gorchetchnikov, A., Versace, M., & Hasselmo, M. E. (2005). A model of STDP based on spatially and temporally local information: derivation and combination with gated decay. Neural Networks, 18, 458–466. doi:10.1016/j.neunet.2005.06.019.PubMedCrossRef

Grossberg, S. (1980). How does a brain build a cognitive code. Psychological Review, 87, 1–51. doi:10.1037/0033-295X.87.1.1.PubMedCrossRef

Grossberg, S., & Versace, M. (2008). Spikes, synchrony, and attentive learning by laminar thalamocortical circuits. Brain Research, 1218, 278–312.PubMedCrossRef

Hammarlund, P., & Ekeberg, Ö. (1998). Large neural network simulations on multiple hardware platforms. The Journal of Comparative Neurology, 5, 443–459.

Hasselmo, M. E. (1995). Neuromodulation and cortical function: modeling the physiological basis of behavior. Behavioural Brain Research, 67, 1–27. doi:10.1016/0166-4328(94)00113-T.PubMedCrossRef

Hines, M. (1989). A program for simulation of nerve equations with branching geometries. International Journal of Bio-Medical Computing, 24, 55–68. doi:10.1016/0020-7101(89)90007-X.PubMedCrossRef

Hines, M. (1993). In F. Eeckman (Ed.), NEURON: a program for simulation of nerve equations in Neural Systems: Analysis and Modeling (pp. 127–136). Kluwer.

Hines, M., & Carnevale, N. T. (1994). Computer simulation methods for neurons. In M. Arbib (Ed.), The handbook of brain theory and neural networks. Cambridge: MIT Press.

Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117, 500–544.PubMed

Humphrey, D. R. (1979). Extracellular, single-unit recording methods. In D. R. Humphrey (Ed.), Electrophysiological techniques pp. 199–259. Bethesda: Society for Neuroscience.

Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14, 1569–1572. doi:10.1109/TNN.2003.820440.PubMedCrossRef

Izhikevich, E. M. (2004). Which model to use for cortical spiking neurons. IEEE Transactions on Neural Networks, 15, 1063–1070. doi:10.1109/TNN.2004.832719.PubMedCrossRef

Kitajima, T., & Hara, K. (2000). Generalized Hebbian rule for activity-dependent synaptic modifications. Neural Networks, 13, 445–454. doi:10.1016/S0893-6080(00)00028-9.PubMedCrossRef

Köhn, J., & Wörgötter, F. (1998). Employing the Z-transform to optimize the calculation of the synaptic conductance of NMDA and other synaptic channels in network simulations. Neural Computation, 10, 1639–1651. doi:10.1162/089976698300017061.PubMedCrossRef

Leveille, J., Grossberg, S., Mingolla, E., & Versace, M. (2008). Collinear facilitation and visual grouping in the spiking LAMINART model. Vision Science Society Abstract (accepted for VSS 2008) Naples, FL.

Levy, W. B., & Steward, O. (1983). Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus. Neuroscience, 8(4), 791–797. doi:10.1016/0306-4522(83)90010-6.PubMedCrossRef

Maass, W., Natschläger, T., & Markram, H. (2002). Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Computation, 14(11), 2531–2560. doi:10.1162/089976602760407955.PubMedCrossRef

Markram, H., Lubke, J., Frotscher, M., & Sakmann, B. (1997). Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science, 275, 213–215. doi:10.1126/science.275.5297.213.PubMedCrossRef

Natschläger, T., Markram, H., & Maass, M. (2002). Computer models and analysis tools for neural microcircuits. In R. Kötter (Ed.), A practical guide to neuroscience databases and associated tools, chapter 9. Boston: Kluwer.

Prescott, S. A., Ratté, S., De Koninck, Y., & Sejnowski, T. J. (2006). Nonlinear interaction between shunting and adaptation controls a switch between integration and coincidence detection in pyramidal neurons. The Journal of Neuroscience, 26, 9084–9097. doi:10.1523/JNEUROSCI.1388-06.2006.PubMedCrossRef

Rall, W. (1964). Theoretical significance of dendritic trees for neuronal input–output relations. In R. F. Reiss (Ed.), Neural theory and modeling (pp. 73–97). Palo Alto: Stanford University Press.

Rudolph, M., & Destexhe, A. (2007). How much can we trust neural simulation strategies. Neurocomputing, 70, 1966–1969. doi:10.1016/j.neucom.2006.10.138.CrossRef

Segev, I., & Burke, R. E. (1998). Compartmental models of complex neurons. In Methods in neuronal modeling. From ions to networks. Cambridge: MIT Press.

Traub, R. D., & Miles, R. (1991). Neuronal networks of the hippocampus. Cambridge: Cambridge University Press.

Tsodyks, M., & Markram, H. (1997). The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. P. Natl. Acad. Sci., 94, 719–723. doi:10.1073/pnas.94.2.719.CrossRef

Vogels, T. P., & Abbott, L. F. (2005). Signal propagation and logic gating in networks of integrate-and-fire neurons. The Journal of Neuroscience, 25, 10786–10795. doi:10.1523/JNEUROSCI.3508-05.2005.PubMedCrossRef

Zador, A., Koch, C., & Brown, T. H. (1990). Biophysical model of a Hebbian synapse. P. Natl. Acad. Sci., 87, 6718–6722. doi:10.1073/pnas.87.17.6718.CrossRef

Titel: KInNeSS: A Modular Framework for Computational Neuroscience
Publikationsdatum: 01.12.2008
Erschienen in: Neuroinformatics / Ausgabe 4/2008
Print ISSN: 1539-2791
Elektronische ISSN: 1559-0089
DOI: https://doi.org/10.1007/s12021-008-9021-2

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"