Nonlinear multiplicative dendritic integration in neuron and network models

Frontiers in Computational Neuroscience

Volumn , Issue APR 2013, 2013, Pages

  Zhang, Danke ^a   Li, Yuanqing ^a   Rasch, Malte J ^a   Wu, Si ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX MORPHOLOGY; EXCITATORY SYNAPSIS; INHIBITORY NEURONS; INHIBITORY SYNAPSIS; MATHEMATICAL TREATMENTS; PERSISTENT ACTIVITIES; SPATIAL ARRANGEMENTS; VOLTAGE POTENTIALS;

COMPLEX NETWORKS; INTEGRATION; NEURONS;

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOPHYSICS; CELL INTERACTION; COMPARTMENT MODEL; DENDRITE; DYNAMICS; ELECTRIC POTENTIAL; MATHEMATICAL ANALYSIS; NERVE CELL EXCITABILITY; NERVE POTENTIAL; NONLINEAR SYSTEM; SYNAPTIC TRANSMISSION;

EID: 84876590797     PISSN: 16625188     EISSN: None     Source Type: Journal    
DOI: 10.3389/fncom.2013.00056     Document Type: Article

References (44)

1. L. Abbott. Firing-rate models for neural populations. Neural networks: From biology to high-energy physics, pages 179-196, 1991.
2. L. Abbott. Realistic synaptic inputs for model neural networks. Network: Computation in Neural Systems, 2(3):245-258, 1991.
3. D. J. Amit and N. Brunel. Dynamics of a recurrent network of spiking neurons before and following learning. Network: Computation in Neural Systems, 8(4):373-404, 1997.
4. D. J. Amit and N. Brunel. Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. Cerebral Cortex, 7(3):237-252, 1997.
5. J. Barrett and W. Crill. Specific membrane properties of cat motoneurones. The Journal of physiology, 239(2):301-324, 1974.
6. S. Blomfield. Arithmetical operations performed by nerve cells. Brain research, 69(1):115-124, 1974.
7. L. J. Borg-Graham, C. Monier, Y. Fregnac, et al. Visual input evokes transient and strong shunting inhibition in visual cortical neurons. Nature, 393(6683):369-372, 1998.
8. C. D. Brody, R. Romo, A. Kepecs, et al. Basic mechanisms for graded persistent activity: discrete attractors, continuous attractors, and dynamic representations. Current opinion in neurobiology, 13(2):204-211, 2003.
9. N. Brunel. Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. Journal of computational neuroscience, 8(3):183-208, 2000.
10. N. Brunel and X.-J. Wang. Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition. Journal of computational neuroscience, 11(1):63-85, 2001.
11. E. Cook and D. Johnston. Active dendrites reduce location-dependent variability of synaptic input trains. Journal of neurophysiology, 78(4):2116-2128, 1997.
12. A. V. Egorov, B. N. Hamam, E. Franśen, M. E. Hasselmo, and A. A. Alonso. Graded persistent activity in entorhinal cortex neurons. Nature, 420(6912):173-178, 2002.
13. W. Gerstner and W. M. Kistler. Spiking neuron models: Single neurons, populations, plasticity. Cambridge university press, 2002.
14. J. Gold and M. Shadlen. Neural computations that underlie decisions about sensory stimuli. Trends in cognitive sciences, 5(1):10-16, 2001.
15. N. L. Golding, T. J. Mickus, Y. Katz, W. L. Kath, and N. Spruston. Factors mediating powerful voltage attenuation along ca1 pyramidal neuron dendrites. The Journal of physiology, 568(1):69-82, 2005.
16. J. Hao, X. Wang, Y. Dan, M. Poo, and X. Zhang. An arithmetic rule for spatial summation of excitatory and inhibitory inputs in pyramidal neurons. Proceedings of the National Academy of Sciences, 106(51):21906-21911, 2009.
17. A. Herz, T. Gollisch, C. Machens, and D. Jaeger. Modeling single-neuron dynamics and computations: A balance of detail and abstraction. Science, 314:80-85, 2006.
18. J. J. Hopfield. Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings of the national academy of sciences, 81(10):3088-3092, 1984.
19. Z. Huang, G. Di Cristo, and F. Ango. Development of GABA innervation in the cerebral and cerebellar cortices. Nature Reviews Neuroscience, 8(9):673-686, 2007.
20. E. M. Izhikevich. Dynamical systems in neuroscience: the geometry of excitability and bursting. MIT press, 2006.
21. M. Jadi, A. Polsky, J. Schiller, and B. Mel. Location-dependent effects of inhibition on local spiking in pyramidal neuron dendrites. PLoS Computational Biology, 8(6):e1002550, 2012.
22. C. Koch. Biophysics of computation: information processing in single neurons. Oxford University Press, USA, 2004.
23. C. Koch, T. Poggio, and V. Torre. Nonlinear interactions in a dendritic tree: Localization, timing, and role in information processing. Proceedings of the National Academy of Sciences, 80(9):2799-2802, 1983.
24. C. Koch, T. Poggio, V. Torres, C. Koch, T. Poggio, and V. Torres. Retinal ganglion cells: a functional interpretation of dendritic morphology. Philosophical Transactions of the Royal Society of London. B, Biological Sciences, 298(1090):227-263, 1982.
25. H. Lai and L. Jan. The distribution and targeting of neuronal voltage-gated ion channels. Nature Reviews Neuroscience, 7(7):548-562, 2006.
26. M. London and M. Ḧausser. Dendritic computation. Annu. Rev. Neurosci., 28:503-532, 2005.
27. C. K. Machens, R. Romo, and C. D. Brody. Flexible control of mutual inhibition: a neural model of two-interval discrimination. Science, 307(5712):1121-1124, 2005.
28. Z. Mainen and T. Sejnowski. Influence of dendritic structure on firing pattern in model neocortical neurons. Nature, 382(6589):363-366, 1996.
29. H. Markram, M. Toledo-Rodriguez, Y. Wang, A. Gupta, G. Silberberg, and C. Wu. Interneurons of the neocortical inhibitory system. Nature Reviews Neuroscience, 5(10):793-807, 2004.
30. D. McLaughlin, R. Shapley, M. Shelley, and D. Wielaard. A neuronal network model of macaque primary visual cortex (v1): Orientation selectivity and dynamics in the input layer 4ca. Proceedings of the National Academy of Sciences, 97(14):8087-8092, 2000.
31. G. Mongillo, O. Barak, and M. Tsodyks. Synaptic theory of working memory. Science Signalling, 319(5869):1543, 2008.
32. T. Omori, T. Aonishi, H. Miyakawa, M. Inoue, and M. Okada. Estimated distribution of specific membrane resistance in hippocampal ca1 pyramidal neuron. Brain research, 1125(1):199-208, 2006.
33. T. Omori, T. Aonishi, H. Miyakawa, M. Inoue, and M. Okada. Steep decrease in the specific membrane resistance in the apical dendrites of hippocampal ca1 pyramidal neurons. Neuroscience research, 64(1):83-95, 2009.
34. W. Rall. Core conductor theory and cable properties of neurons. Wiley Online Library, 1977.
35. M. J. Rasch, K. Schuch, N. K. Logothetis, andW.Maass. Statistical comparison of spike responses to natural stimuli in monkey area v1 with simulated responses of a detailed laminar network model for a patch of v1. Journal of neurophysiology, 105(2):757-778, 2011.
36. A. Renart, P. Song, X.-J. Wang, et al. Robust spatial working memory through homeostatic synaptic scaling in heterogeneous cortical networks. Neuron, 38(3):473-486, 2003.
37. T. P. Vogels and L. Abbott. Gating multiple signals through detailed balance of excitation and inhibition in spiking networks. Nature neuroscience, 12(4):483-491, 2009.
38. E. Vu and F. Krasne. Evidence for a computational distinction between proximal and distal neuronal inhibition. Science (New York, NY), 255(5052):1710-1712, 1992.
39. X. Wang. Synaptic reverberation underlying mnemonic persistent activity. Trends in neurosciences, 24(8):455-463, 2001.
40. X. Wang. Probabilistic decision making by slow reverberation in cortical circuits. Neuron, 36(5):955-968, 2002.
41. H. R. Wilson and J. D. Cowan. Excitatory and inhibitory interactions in localized populations of model neurons. Biophysical journal, 12(1):1-24, 1972.
42. K.-F. Wong and X.-J. Wang. A recurrent network mechanism of time integration in perceptual decisions. The Journal of neuroscience, 26(4):1314-1328, 2006.
43. D. Zhang, Y. Cui, Y. Li, and S. Wu. Simple models for synaptic information integration. In Neural Information Processing, pages 210-216. Springer, 2011.
44. D. Zhou, S. Li, X.-h. Zhang, and D. Cai. Phenomenological incorporation of nonlinear dendritic integration using integrate-and-fire neuronal frameworks. PloS one, 8(1):e53508, 2013.