Spike-timing prediction in cortical neurons with active dendrites

Frontiers in Computational Neuroscience

Volumn 8, Issue AUG, 2014, Pages

  Naud, Richard ^a   Bathellier, Brice ^b   Gerstner, Wulfram ^c  

^a UNIVERSITY OF OTTAWA   (Canada)

^b Unite de Neurosciences Integratives et Computationnelles   (France)

^c EPFL   (Switzerland)

Author keywords

Cortical neurons; Dendrites; Models; Neuron models; Spike train analysis; Theoretical

Indexed keywords

DENDRITES (METALLOGRAPHY); DIFFERENTIAL EQUATIONS; ELECTROPHYSIOLOGY; MODELS;

CORTICAL NEURONS; IMPULSE-RESPONSE FUNCTIONS; NEURON MODEL; NONLINEAR DIFFERENTIAL EQUATION; SINGLE-NEURON MODELS; SPIKE TRAIN ANALYSIS; THEORETICAL; TWO-COMPARTMENT MODELS;

NEURONS;

ACCURACY; ANALYTIC METHOD; ANIMAL CELL; ANIMAL TISSUE; ARTICLE; BRAIN CELL; BRAIN SLICE; CONTROLLED STUDY; DENDRITE; HODGKIN HUXLEY EQUATION; MATHEMATICAL ANALYSIS; NONHUMAN; PREDICTION; PYRAMIDAL NERVE CELL; RAT; SOMATOSENSORY CORTEX; SPIKE;

EID: 84940255644     PISSN: None     EISSN: 16625188     Source Type: Journal    
DOI: 10.3389/fncom.2014.00090     Document Type: Article

References (35)

1. Archie, K., and Mel, B. (2000). A model for intradendritic computation of binocular disparity. Nat. Neurosci. 3, 54-63. doi: 10.1038/71125
2. Branco, T., Clark, B. A., and Michael, H. (2010). Dendritic discrimination of temporal input sequences in cortical neurons. Science 329, 1671-1675. doi: 10.1126/science.1189664
3. Gabbiani, F., Krapp, H. G., Koch, C., and Laurent, G. (2002). Multiplicative computation in a visual neuron sensitive to looming. Nature 420, 320-324. doi: 10.1038/nature01190
4. Gerstner, W., Kistler, W., Naud, R., and Paninski, L. (2014). Neuronal Dynamics. Cambridge, UK: Cambridge University Press.
5. Gerstner, W., and Naud, R. (2009). How good are neuron models? Science 326, 379-380. doi: 10.1126/science.1181936
6. Golding, N. L., and Spruston, N. (1988). Dendritic sodium spikes are variable triggers of axonal action potentials in hippocampal ca1 pyramidal neurons. Neuron 21, 1189-1200. doi: 10.1016/S0896-6273(00)80635-2
7. Jolivet, R., Kobayashi, R., Rauch, A., Naud, R., Shinomoto, S., and Gerstner, W. (2008a). A benchmark test for a quantitative assessment of simple neuron models. J. Neurosci. Methods 169, 417-424. doi: 10.1016/j.jneumeth.2007.11.006
8. Jolivet, R., Rauch, A., Lüscher, H., and Gerstner, W. (2006). Predicting spike timing of neocortical pyramidal neurons by simple threshold models. J. Comput. Neurosci. 21, 35-49. doi: 10.1007/s10827-006-7074-5
9. Jolivet, R., Schürmann, F., Berger, T. K., Naud, R., Gerstner, W., and Roth, A. (2008b). The quantitative single-neuron modeling competition. Biol. Cybern. 99, 417-426. doi: 10.1007/s00422-008-0261-x
10. Keat, J., Reinagel, P., Reid, R. C., and Meister, M. (2001). Predicting every spike a model for the responses of visual neurons. Neuron 30, 803-817. doi: 10.1016/S0896-6273(01)00322-1
11. Kistler, W., Gerstner, W., Hemmen, J. (1997). Reduction of the hodgkin-huxley equations to a single-variable threshold model. Neural Comput. 9, 1015-1045. doi: 10.1162/neco.1997.9.5.1015
12. Larkum, M. E., Nevian, T., Sandler, M., Polsky, A., and Schiller, J. (2009). Synaptic integration in tuft dendrites of layer 5 pyramidal neurons: a new unifying principle. Science 325, 756-760. doi: 10.1126/science.1171958
13. Larkum, M. E., Senn, W., and Luscher, H. R. (2004). Top-down dendritic input increases the gain of layer 5 pyramidal neurons. Cereb. Cortex 14, 1059-1070. doi: 10.1093/cercor/bhh065
14. Larkum, M. E., Zhu, J., and Sakmann, B. (1999). A new cellular mechanism for coupling inputs arriving at different cortical layers. Nature 398, 338-341. doi: 10.1038/18686
15. Larkum, M. E., Zhu, J. J., and Sakmann, B. (2001). Dendritic mechanisms underlying the coupling of the dendritic with the axonal action potential initiation zone of adult rat layer 5 pyramidal neurons. J. Physiol. (Lond.) 533, 447-466. doi: 10.1111/j.1469-7793.2001.0447a.x
16. Legenstein, R., and Maass, W. (2011). Branch-specific plasticity enables self-organization of nonlinear computation in single neurons. J. Neurosci. 31, 10787-10802. doi: 10.1523/JNEUROSCI.5684-10.2011
17. Llinas, R., and Sugimori, M. (1980). Electrophysiological properties of in vitro purkinje cell dendrites in mammalian cerebellar slices. J. Physiol. 305, 197-213.
18. Mensi, S., Naud, R., Avermann, M., Petersen, C. C. H., and Gerstner, W. (2012). Parameter extraction and classification of three neuron types reveals two different adaptation mechanisms. J. Neurophysiol. 107, 1756-1775. doi: 10.1152/jn.00408.2011
19. Naud, R., Berger, T., Bathellier, B., Carandini, M., and Gerstner, W. (2009). Quantitative single-neuron modeling: competition 2009. Front. Neur. Conference Abstract: Neuroinformatics 2009. doi: 10.3389/conf.neuro.11.2009.08.106
20. Naud, R., Gerhard, F., Mensi, S., and Gerstner, W. (2011). Improved similarity measures for small sets of spike trains. Neural Comput. 23, 3016-3069. doi: 10.1162/NECO-a-00208
21. Naud, R., and Gerstner, W. (2012). Spike Timing: Mechanisms and Function, Chapter Can We Predict Every Spike. Boca Raton, FL: CRC Press.
22. Paninski, L. (2004). Maximum likelihood estimation of cascade point-process neural encoding models. Network 15, 243-262. doi: 10.1088/0954-898X/15/4/002
23. Paninski, L., Pillow, J. W., and Simoncelli, E. (2005). Comparing integrate-and-fire models estimated using intracellular and extracellular data. Neurocomputing 65-66, 379-385. doi: 10.1016/j.neucom.2004.10.032
24. Pérez-Garci, E., Gassmann, M., Bettler, B., and Larkum, M. E. (2006). The gabab1b isoform mediates long-lasting inhibition of dendritic ca2+ spikes in layer 5 somatosensory pyramidal neurons. Neuron 50, 603-616. doi: 10.1016/j.neuron.2006.04.019
25. Pillow, J. W., Paninski, L., Uzzell, V. J., Simoncelli, E. P., and Chichilnisky, E. J. (2005). Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. J. Neurosci. 25, 11003-11013. doi: 10.1523/JNEUROSCI.3305-05.2005
26. Pinsky, P., and Rinzel, J. (1994). Intrinsic and network rhythmogenesis in a reduced traub model for ca3 neurons. J. Comput. Neurosci. 1, 39-60. doi: 10.1007/BF00962717
27. Polsky, A., Mel, B., and Schiller, J. (2004). Computational subunits in thin dendrites of pyramidal cells. Nat. Neurosci. 7, 621-627. doi: 10.1038/nn1253
28. Pozzorini, C., Naud, R., Mensi, S., and Gerstner, W. (2013). Temporal whitening by power-law adaptation in neocortical neurons. Nat. Neurosci. 16, 942-948. doi: 10.1038/nn.3431
29. Schaefer, A., Larkum, M. E., Sakmann, B., and Roth, A. (2003). Coincidence detection in pyramidal neurons is tuned by their dendritic branching pattern. J. Neurophysiol. 89, 3143-3154. doi: 10.1152/jn.00046.2003
30. Schiller, J., Major, G., Koester, H. J., and Schiller, Y. (2000). Nmda spikes in basal dendrites of cortical pyramidal neurons. Nature 404, 285-289. doi: 10.1038/35005094
31. Segev, I., Rall, W., and Rinzel, J. (1995). The Theoretical Foundation of Dendritic Function. Cambridge, MA: MIT Press.
32. Stuart, G., Spruston, N., and Häusser, M. (2007). Dendrites, 2nd Edn. Oxford: Oxford University Press. ISBN: 9780198566564 (alk. paper). doi: 10.1093/acprof:oso/9780198566564.001.0001
33. Taylor, W. R., He, S., Levick, W. R., and Vaney, D. I. (2000). Dendritic computation of direction selectivity by retinal ganglion cells. Science 289, 2347-2350. doi: 10.1126/science.289.5488.2347
34. Traub, R. D., Wong, R. K. S., Miles, R., and Michelson, H. (1991). A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. J. Neurophysiol. 66, 635-650.
35. Victor, J. D., and Purpura, K. (1996). Nature and precision of temporal coding in visual cortex: a metric-space analysis. J. Neurophysiol. 76, 1310-1326.