How synaptic release probability shapes neuronal transmission: Information-theoretic analysis in a cerebellar granule cell

Neural Computation

Volumn 22, Issue 8, 2010, Pages 2031-2058

  Arleo, Angelo ^a,b   Nieus, Thierry ^c,d   Bezzi, Michele ^b   D'Errico, Anna ^d   D'Angelo, Egidio ^d   Coenen, Olivier J M D ^b  

^a Groupe Enzymologie Moléculaire et Fonctionnelle   (France)

^b SONY COMPUTER SCIENCE LABORATORY   (France)

^c ISTITUTO ITALIANO DI TECNOLOGIA   (Italy)

^d UNIVERSITY OF PAVIA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL; BIOLOGICAL MODEL; CEREBELLUM; LETTER; NERVE CELL; NERVE CELL PLASTICITY; PATCH CLAMP; PHYSIOLOGY; RAT; SYNAPTIC TRANSMISSION; WISTAR RAT; PATCH CLAMP TECHNIQUE;

ANIMALS; CEREBELLUM; MODELS, NEUROLOGICAL; NEURONAL PLASTICITY; NEURONS; PATCH-CLAMP TECHNIQUES; RATS; RATS, WISTAR; SYNAPTIC TRANSMISSION;

EID: 77955986730     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00006-Arleo     Document Type: Letter

References (88)

1. Abbott, L. F., & Regehr, W. G. (2004). Synaptic computation. Nature, 431, 796-803.
2. Abeles, M., Vaadia, E., & Bergman, H. (1990). Firing patterns of single units in the prefrontal cortex and neural network models. Network, 1, 13-25.
3. Albus, J. S. (1971). A theory of cerebellar function. Math. Biosc, 10, 25-61.
4. Arenz, A., Silver, R. A., Schaefer, A. T., & Margrie, T. W. (2008). The contribution of single synapses to sensory representation in vivo. Science, 321, 977-980.
5. Baddeley, R., Abbott, L. F., Booth, M. C, Sengpiel, F., Freeman, T., Wakeman, E. A., et al. (1997). Responses of neurons in primary and inferior temporal visual cortices to natural scenes. Proc. Biol. Sci., 264, 1775-1783.
6. Barnes, C. A., McNaughton, B. L., Mizumori, S. J., Leonard, B. W., & Lin, L. H. (1990). Comparison of spatial and temporal characteristics of neuronal activity in sequential stages of hippocampal processing. Prog. Brain Res., 83, 287-300.
7. Bezzi, M., Nieus, T., Arleo, A., D'Angelo, E., & Coenen, O. J. M. (2004). Information transfer at the mossy fiber-granule cell synapse of the cerebellum. Society for Neuroscience Abstracts No. 827.5, San Diego.
8. Bialek, W., Rieke, F, de Ruyter van Steveninck, R. R., & Warland, D. (1991). Reading a neural code. Science, 252, 1854-1857.
9. Bliss, T. V., & Collingridge, G. L. (1993). A synaptic model of memory: Long-term potentiation in the hippocampus. Nature, 361, 31-39.
10. Bliss, T. V., & Lomo, T. (1973). Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. J. Physiol, 232, 331-356.
11. Borst, A., & Theunissen, F E. (1999). Information theory and neural coding. Nat. Neurosci., 2, 947-957.
12. Brenner, N, Bialek, W., & de Ruyter van Steveninck, R. (2000). Adaptive rescaling maximizes information transmission. Neuron, 26, 695-702.
13. Brenner, N, Strong, S. P., Koberle, R., Bialek, W., & de Ruyter van Steveninck, R. R. (2000). Synergy in a neural code. Neural. Comput., 12, 1531-1552.
14. Butts, D. A., & Goldman, M. S. (2006). Tuning curves, neuronal variability, and sensory coding. PLoS Biol., 4, e92.
15. Casado, M., Isope, P., & Ascher, P. (2002). Involvement of presynaptic N-methyl-D-aspartate receptors in cerebellar long-term depression. Neuron, 33, 123-130.
16. Chadderton, P., Margrie, T. W., & Hausser, M. (2004). Integration of quanta in cerebellar granule cells during sensory processing. Nature, 428, 856-860.
17. Coenen, O. J.-M. D., Arnold, M. P., Sejnowski, T. J., & Jabri, M. A. (2001). Parallel fiber coding in the cerebellum for life-long learning. Autonomous Robots, 11, 291-297.
18. Cover, T. M., & Thomas, J. A. (1991). Elements of information theory. New York: Wiley.
19. D'Angelo, E., De Filippi, G., Rossi, P., & Taglietti, V. (1995). Synaptic excitation of individual rat cerebellar granule cells in situ: Evidence for the role of NMDA receptors. J. Physiol., 484, 397-413.
20. D'Angelo, E., & De Zeeuw, C. I. (2009). Timing and plasticity in the cerebellum: Focus on the granular layer. Trends Neurosci., 32, 30-40.
21. D'Angelo, E., Nieus, T., Maffei, A., Armano, S., Rossi, P., Taglietti, V., et al. (2001). Theta-frequency bursting and resonance in cerebellar granule cells: Experimental evidence and modeling of a slow k+-dependent mechanism. J. Neurosci., 21, 759-770.
22. de la Rocha, J., Nevado, A., & Parga, N. (2002). Information transmission by stochastic synapses with short-term depression: Neural coding and optimization. Neurocomputing, 44-46, 85-90.
23. de la Rocha, J., & Parga, N. (2005). Short-term synaptic depression causes a nonmonotonic response to correlated stimuli. J. Neurosci., 25, 8416-8431.
24. de Ruyter van Steveninck, R. R., Lewen, G. D., Strong, S. P., Koberle, R., & Bialek, W. (1997). Reproducibility and variability in neural spike trains. Science, 275, 1805-1808.
25. DeWeese, M. R., & Meister, M. (1999). How to measure the information gained from one symbol. Network, 10, 325-340.
26. Dimitrov, A. G., Miller, J. P., Gedeon, T., Aldworth, Z., & Parker, A. E. (2003). Analysis of neural coding through quantization with an information-based distortion measure. Network, 14, 151-176.
27. Dobrunz, L. E., & Stevens, C. F. (1997). Heterogeneity of release probability, facilitation, and depletion at central synapses. Neuron, 18, 995-1008.
28. Dum, R. P., Li, C, & Strick, P. L. (2002). Motor and nonmotor domains in the monkey dentate. Ann. N. Y. Acad. Sci., 978, 289-301.
29. Eccles, J. C, Ito, M., & Szentagothai, J. (1967). The cerebellum as a neuronal machine. Berlin: Springer Verlag.
30. Forti, L., Cesana, E., Isope, P., Dieudonné, S., & D'Angelo, E. (2008). The mossy fiber input to Golgi cells in the cerebellum. FENS Abst., vol. 4, 221.5.
31. Forti, L., Cesana, E., Mapelli, J., & D'Angelo, E. (2006). Ionic mechanisms of au-torhythmic firing in rat cerebellar Golgi cells. J. Physiol., 574, 711-729.
32. Franco, L., Rolls, E. T., Aggelopoulos, N. C, & Jerez, J. M. (2007). Neuronal selectivity, population sparseness, and ergodicity in the inferior temporal visual cortex. Biol. Cybern., 96, 547-560.
33. Fuhrmann, G., Segev, I., Markram, H., & Tsodyks, M. (2002). Coding of temporal information by activity-dependent synapses. J. Neurophysiol, 87, 140-148.
34. Goldman, M. S. (2004). Enhancement of information transmission efficiency by synaptic failures. Neural Comput., 16, 1137-1162.
35. Hansel, C, Linden, D. J., & D'Angelo, E. (2001). Beyond parallel fiber LTD: The diversity of synaptic and non-synaptic plasticity in the cerebellum. Nat. Neurosci., 4, 467-475.
36. Hartmann, M. J., & Bower, J. M. (1998). Oscillatory activity in the cerebellar hemispheres of unrestrained rats. J. Neurophysiol, 80, 1598-1604.
37. Jakab, R. L., & Hamori, J. (1988). Quantitative morphology and synaptology of cerebellar glomeruli in the rat. Anat. Embryol. (Berl.), 179, 81-88.
38. Jorntell, H., & Ekerot, C. F. (2006). Properties of somatosensory synaptic integration in cerebellar granule cells in vivo. J. Neurosci., 26, 11786-11797.
39. Kistler, W. M., & De Zeeuw, C. I. (2003). Time windows and reverberating loops: A reverse-engineering approach to cerebellar function. Cerebellum, 2, 44-54.
40. Koch, C, & Segev, I. (2000). The role of single neurons in information processing. Nat. Neurosci., 3 (Suppl.), 1171-1177.
41. Koester, H. J., & Johnston, D. (2005). Target cell-dependent normalization of transmitter release at neocortical synapses. Science, 308, 863-866.
42. London, M., Schreibman, A., Hausser, M., Larkum, M. E., & Segev, I. (2002). The information efficacy of a synapse. Nat. Neurosci., 5, 332-340.
43. Lu, T., & Wang, X. (2004). Information content of auditory cortical responses to time-varying acoustic stimuli. J. Neurophysiol., 91, 301-313.
44. Maex, R., & Schutter, E. D. (1998). Synchronization of Golgi and granule cell firing in a detailed network model of the cerebellar granule cell layer. J. Neurophysiol., 80, 2521-2537.
45. Magistretti, J., Castelli, L., Forti, L., & D'Angelo, E. (2006). Kinetic and functional analysis of transient, persistent, and resurgent sodium currents in rat cerebellar granule cells in situ: An electrophysiological and modelling study. J. Physiol., 573, 83-106.
46. Malenka, R. C, & Bear, M. F. (2004). LTP and LTD: An embarrassment of riches. Neuron, 44, 5-21.
47. Manwani, A., & Koch, C. (2001). Detecting and estimating signals over noisy and unreliable synapses: Information-theoretic analysis. Neural Comput., 13, 1-33.
48. Manwani, A., Steinmetz, P. N., & Koch, C. (2002). The impact of spike timing variability on the signal-encoding performance of neural spiking models. Neural Comput., 14, 347-367.
49. Mapelli, J., & D'Angelo, E. (2007). The spatial organization of long-term synaptic plasticity at the input stage of cerebellum. J. Neurosci., 27, 1285-1296.
50. Mapelli, L., Rossi, P., Nieus, T., & D'Angelo, E. (2009). Tonic activation of GABA-B receptors reduces release probability at inhibitory connections in the cerebellar glomerulus. J. Neurophysiol., 101(6), 3089-3099.
51. Mitchell, S. J., & Silver, R. A. (2003). Shunting inhibition modulates neuronal gain during synaptic excitation. Neuron, 38(3), 433-445.
52. Moreno-Bote, R., & Parga, N. (2004). Role of synaptic filtering on the firing response of simple model neurons. Phys. Rev. Lett., 92, 028102.
53. Natschlager, T., & Maass, W. (2001). Computing the optimally fitted spike train for a synapse. Neural Comput., 13, 2477-2494.
54. Nemenman, I., Bialek, W., & de Ruyter van Steveninck, R. R. (2004). Entropy and information in neural spike trains: Progress on the sampling problem. Phys. Rev. E, 69, 056111.
55. Nielsen, T. A., DiGregorio, D. A., & Silver, R. A. (2004). Modulation of glutamate mobility reveals the mechanism underlying slow-rising AMPAR EPSCs and the diffusion coefficient in the synaptic cleft. Neuron, 42, 757-771.
56. Nieus, T., Sola, E., Mapelli, J., Saftenku, E., Rossi, P., & D'Angelo, E. (2006). LTP regulates burst initiation and frequency at mossy fiber-granule cell synapses of rat cerebellum: Experimental observations and theoretical predictions. J. Neurophysiol., 95, 686-699.
57. Paninski, L. (2003). Estimation of entropy and mutual information. Neural Comput., 15, 1191-1254.
58. Philipona, D., & Coenen, O. J.-M. D. (2004). Model of granular layer encoding of the cerebellum. Neurocomputing, 58-60, 575-580.
59. Quian Quiroga, R., & Panzeri, S. (2009). Extracting information from neuronal populations: Information theory and decoding approaches. Nat. Rev Neurosci., 10, 173-185.
60. Rancz, E. A., Ishikawa, T., Duguid, I., Chadderton, P., Mahon, S., & Hausser, M. (2007). High-fidelity transmission of sensory information by single cerebellar mossy fibre boutons. Nature, 450, 1245-1248.
61. Roddey, J. C, & Jacobs, G. A. (1996). Information theoretic analysis of dynamical encoding by filiform mechanoreceptors in the cricket cercal system. J. Neurophysiol., 75, 1365-1376.
62. Rolls, E. T., & Deco, G. (2002). Computational neuroscience of vision. Oxford: Oxford University Press.
63. Rothman, J. S., Cathala, L., Steuber, V., & Silver, R. A. (2009). Synaptic depression enables neuronal gain control. Nature, 457, 1015-1018.
64. Sargent, P. B., Saviane, C, Nielsen, T. A., DiGregorio, D. A., & Silver, R. A. (2005). Rapid vesicular release, quantal variability, and spillover contribute to the precision and reliability of transmission at a glomerular synapse. J. Neurosci., 25, 8173-8187.
65. Saviane, C, & Silver, R. A. (2006). Fast vesicle reloading and a large pool sustain high bandwidth transmission at a central synapse. Nature, 439, 983-987.
66. Schreiber, S., Fellous, J. M., Tiesinga, P., & Sejnowski, T. J. (2004). Influence of ionic conductances on spike timing reliability of cortical neurons for suprathreshold rhythmic inputs. J. Neurophysiol., 91, 194-205.
67. Schweighofer, N, Doya, K., & Lay, F. (2001). Unsupervised learning of granule cell sparse codes enhances cerebellar adaptive control. Neuroscience, 103, 35-50.
68. Shannon, C. E. (1948). The mathematical theory of communication. Bell Syst. Tech. J., 27, 379-423.
69. Sharpee, T. O, Sugihara, H., Kurgansky, A. V., Rebrik, S. P., Stryker, M. P., & Miller, K. D. (2006). Adaptive filtering enhances information transmission in visual cortex. Nature, 439, 936-942.
70. Silver, R. A., Traynelis, S. F., & Cull-Candy, S. G. (1992). Rapid-time-course miniature and evoked excitatory currents at cerebellar synapses in situ. Nature, 355, 163-166.
71. Smith, E. C, & Lewicki, M. S. (2006). Efficient auditory coding. Nature, 439, 978-982.
72. Sola, E., Prestori, F., Rossi, P., Taglietti, V., & D'Angelo, E. (2004). Increased neurotransmitter release during long-term potentiation at mossy fibre-granule cell synapses in rat cerebellum. J. Physiol., 557, 843-861.
73. Solinas, S., Forti, L., Cesana, E., Mapelli, J., De Schutter, E., & D'Angelo, E. (2007a). Computational reconstruction of pacemaking and intrinsic electroresponsiveness in cerebellar Golgi cells. Front Cell Neurosci., 1, 2.
74. Solinas, S., Forti, L., Cesana, E., Mapelli, J., De Schutter, E., & D'Angelo, E. (2007b). Fast-reset of pacemaking and theta-frequency resonance patterns in cerebellar Golgi cells: Simulations of their impact in vivo. Front Cell Neurosci., 1, 4.
75. Strong, S. P., Koberle, R., de Ruyter van Steveninck, R. R., & Bialek, W. (1998). Entropy and information in neural spike trains. Phys. Rev. Lett., 80(1), 197-200.
76. Sun, H. Y, Lyons, S. A., & Dobrunz, L. E. (2005). Mechanisms of target-cell specific short-term plasticity at Schaffer collateral synapses onto interneurones versus pyramidal cells in juvenile rats. J. Physiol, 568, 815-840.
77. Theunissen, F E., & Miller, J. P. (1991). Representation of sensory information in the cricket cercal sensory system. II. Information theoretic calculation of system accuracy and optimal tuning-curve widths of four primary interneurons. J. Neurophysiol., 66, 1690-1703.
78. Theunissen, F, Roddey, J. C, Stufflebeam, S., Clague, H., & Miller, J. P. (1996). Information theoretic analysis of dynamical encoding by four identified primary sensory interneurons in the cricket cercal system. J. Neurophysiol., 75, 1345-1364.
79. Tiesinga, P. H. (2001). Information transmission and recovery in neural communication channels revisited. Phys. Rev. E Stat. Nonlin. Soft Matter Phys., 64, 012901.
80. Tiesinga, P. H., Fellous, J. M., Jose, J. V., & Sejnowski, T. J. (2002). Information transfer in entrained cortical neurons. Network, 13, 41-66.
81. Tiesinga, P. H., & Toups, J. V. (2005). The possible role of spike patterns in cortical information processing. J. Comput. Neurosci., 18, 275-286.
82. Treves, A., & Panzeri, S. (1995). The upward bias in measures of information derived from limited data samples. Neural Comput., 7, 399-407.
83. Treves, A., Panzeri, S., Rolls, E. T., Booth, M., & Wakeman, E. A. (1999). Firing rate distributions and efficiency of information transmission of inferior temporal cortex neurons to natural visual stimuli. Neural Comput., 11, 601-632.
84. Treves, A., & Rolls, E. T. (1991). What determines the capacity of autoassociative memories in the brain? Network, 2, 371-398.
85. Tsodyks, M. V., & Markram, H. (1997). The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proc. Natl. Acad. Sci. USA, 94, 719-723.
86. Vos, B. P., Wijnants, M., Taeymans, S., & de Schutter, E. (1999). Miniature carrier with six independently moveable electrodes for recording of multiple single-units in the cerebellar cortex of awake rats. J. Neurosci. Methods, 94, 19-26.
87. Wan, Y. H., Jian, Z., Wen, Z. H., Wang, Y. Y, Han, S., Duan, Y. B., et al. (2004). Synaptic transmission of chaotic spike trains between primary afferent fiber and spinal dorsal horn neuron in the rat. Neuroscience, 125, 1051-1060.
88. Zador, A. (1998). Impact of synaptic unreliability on the information transmitted by spiking neurons. J. Neurophysiol., 79, 1219-1229.