The impact of short term synaptic depression and stochastic vesicle dynamics on neuronal variability

Journal of Computational Neuroscience

Volumn 35, Issue 1, 2013, Pages 39-53

  Reich, Steven ^a   Rosenbaum, Robert ^a  

^a UNIVERSITY OF PITTSBURGH   (United States)

Author keywords

Fano factor; Short term depression; Synaptic variability

Indexed keywords

CONTINUOUS TIME SYSTEMS; ELECTROPHYSIOLOGY; MARKOV PROCESSES; NEURAL NETWORKS; NEURONS; POISSON DISTRIBUTION; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

ACTION-POTENTIALS; FANO FACTOR; PRESYNAPTIC; SHORT TERM; SHORT TERM DEPRESSION; STOCHASTICS; SYNAPTIC DEPRESSION; SYNAPTIC VARIABILITY; TIME WINDOWS; VESICLE DYNAMICS;

DYNAMICS;

ALGORITHM; ARTICLE; BINOMIAL DISTRIBUTION; CALCULATION; NEUROTRANSMITTER RELEASE; POISSON DISTRIBUTION; POSTSYNAPTIC POTENTIAL; PRESYNAPTIC NERVE; PRESYNAPTIC POTENTIAL; PRIORITY JOURNAL; PROBABILITY; SHORT TERM SYNAPTIC DEPRESSION; SPIKE WAVE; STATISTICAL ANALYSIS; STEADY STATE; STOCHASTIC MODEL; SYNAPTIC TRANSMISSION;

EID: 84880204473     PISSN: 09295313     EISSN: 15736873     Source Type: Journal    
DOI: 10.1007/s10827-012-0438-0     Document Type: Article

References (72)

1. Abbott, L.F.; Regehr, W.G. (2004). Synaptic computation. Nature, 431(7010), 796-803.
2. Abbott, L.F.; Varela, J.A.; Sen, K.; Nelson, S.B. (1997). Synaptic depression and cortical gain control. Science, 275(5297), 220-224.
3. Baddeley, R.; Abbott, L.; Booth, M.; Sengpiel, F.; Freeman, T.; Wakeman, E.; et al. (1997). Responses of neurons in primary and inferior temporal visual cortices to natural scenes. Proceedings of the Royal Society of London Series B: Biological Sciences, 264(1389), 1775-1783.
4. Bair, W.; Koch, C.; Newsome, W.; Britten, K. (1994). Power spectrum analysis of bursting cells in area mt in the behaving monkey. The Journal of Neuroscience, 14(5), 2870-2892.
5. Barbieri, F.; Brunel, N. (2008). Can attractor network models account for the statistics of firing during persistent activity in prefrontal cortex? Front in Neuroscience, 2(1), 114.
6. Branco, T.; Staras, K. (2009). The probability of neurotransmitter release: variability and feedback control at single synapses. Nature Reviews Neuroscience, 10(5), 373-383.
7. Bressloff, P.C. (1999). Mean-field theory of globally coupled integrate-and-fire neural oscillators with dynamic synapses. Physical Review E, 60, 2160-2170. doi: 10.1103/PhysRevE.60.2160. http://link.aps.org/doi/10.1103/ PhysRevE.60.2160.
8. Britten, K.; Shadlen, M.; Newsome, W.; Movshon, J. (1993). Responses of neurons in macaque mt to stochastic motion signals. Visual Neuroscience, 10, 1157-1157.
9. Buracas, G.; Zador, A.; DeWeese, M.; Albright, T. (1998). Efficient discrimination of temporal patterns by motion-sensitive neurons in primate visual cortex. Neuron, 20(5), 959-969.
10. Chance, F.; Nelson, S.; Abbott, L. (1998). Synaptic depression and the temporal response characteristics of v1 cells. Journal of Neuroscience, 18(12), 4785.
11. Churchland, M.; Byron, M.; Cunningham, J.; Sugrue, L.; Cohen, M.; Corrado, G.; et. al. (2010). Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nature Neuroscience, 13(3), 369-378.
12. Cook, D.L.; Schwindt, P.C.; Grande, L.A.; Spain, W.J. (2003). Synaptic depression in the localization of sound. Nature, 421(6918), 66-70.
13. Cox, D. (1962). Renewal Theory. London: Methuen and Co.
14. Cox, D.; Isham, V. (1980). Point processes. London: Chapman and Hall.
15. Dan, Y.; Atick, J.; Reid, R. (1996). Efficient coding of natural scenes in the lateral geniculate nucleus: experimental test of a computational theory. The Journal of Neuroscience, 16(10), 3351-3362.
16. Dayan, P.; Abbott, L. (2001). Theoretical Neurosci: Computational and mathematical modeling of neural systems. New York: Taylor & Francis.
17. de la Rocha, J.; Moreno, R. (2004). Correlations modulate the non-monotonic response of a neuron with short-term plasticity. Neurocomputing, 58, 313-319.
18. de la Rocha, J.; Nevado, A. (2002). Information transmission by stochastic synapses with short-term depression: neural coding and optimization. Neurocomputing, 44, 85-90.
19. de la Rocha, J.; Parga, N. (2005). Short-term synaptic depression causes a non-monotonic response to correlated stimuli. Journal of Neuroscience, 25(37), 8416-8431.
20. de la Rocha, J.; Parga, N. (2008). Thalamocortical transformations of periodic stimuli: the effect of stimulus velocity and synaptic short-term depression in the vibrissa-barrel system. Journal of Computational Neuroscience, 25(1), 122-140.
21. Faisal, A.; Selen, L.; Wolpert, D. (2008). Noise in the nervous system. Nature Reviews Neuroscience, 9(4), 292-303.
22. Fuhrmann, G.; Segev, I.; Markram, H.; Tsodyks, M. (2002). Coding of temporal information by activity-dependent synapses. Journal of Neurophysiology, 87(1), 140.
23. Galarreta, M.; Hestrin, S. (1998). Frequency-dependent synaptic depression and the balance of excitation and inhibition in the neocortex. Nature Neuroscience, 1, 587-594.
24. Goldman, M. (2004). Enhancement of information transmission efficiency by synaptic failures. Neural Computation, 16(6), 1137-1162.
25. Goldman, M.; Nelson, S.; Abbott, L. (1999). Decorrelation of spike trains by synaptic depression. Neurocomputing, 26, 147-153.
26. Goldman, M.; Maldonado, P.; Abbott, L. (2002). Redundancy reduction and sustained firing with stochastic depressing synapses. The Journal of Neuroscience, 22(2), 584-591.
27. Grande, L.A.; Spain, W.J. (2005). Synaptic depression as a timing device. Journal of Physiology, 20, 201-210.
28. Hanson, J.E.; Jaeger, D. (2002). Short-term plasticity shapes the response to simulated normal and parkinsonian input patterns in the globus pallidus. Journal of Neuroscience, 22(12), 5164-5172.
29. Harsch, A.; Robinson, H. (2000). Postsynaptic variability of firing in rat cortical neurons: the roles of input synchronization and synaptic nmda receptor conductance. The Journal of Neuroscience, 20(16), 6181-6192.
30. Kara, P.; Reinagel, P.; Reid, R. (2000). Low response variability in simultaneously recorded retinal, thalamic, and cortical neurons. Neuron, 27(3), 635-646.
31. Karlin, S.; Taylor, H. (1975). A first course in stochastic processes. New York: Academic Press.
32. Kuhn, A.; Aertsen, A.; Rotter, S. (2003). Higher-order statistics of input ensembles and the response of simple model neurons. Neural Computation, 15(1), 67-101.
33. Lindner, B.; Gangloff, D.; Longtin, A.; Lewis, J.E. (2009). Broadband coding with dynamic synapses. Journal of Neuroscience, 29(7), 2076-2087.
34. Litwin-Kumar, A.; Doiron, B. (2012). Slow dynamics and high variability in balanced cortical networks with clustered connections. Nature Neuroscience, 15, 1498-1505.
35. Maass, W.; Zador, A. (1999). Dynamic stochastic synapses as computational units. Neural Computation, 11(4), 903-917.
36. Markram, H.; Wang, Y.; Tsodyks, M. (1998). Differential signaling via the same axon of neocortical pyramidal neurons. Proceedings of the National Academy of Sciences of the United States of America, 95(9), 5323.
37. Matveev, V.; Wang, X. (2000a). Differential short-term synaptic plasticity and transmission of complex spike trains: to depress or to facilitate. Cerebral Cortex, 10(11), 1143-1153.
38. Matveev, V.; Wang, X. (2000b). Implications of all-or-none synaptic transmission and short-term depression beyond vesicle depletion: a computational study. Journal of Neuroscience, 20(4), 1575-1588.
39. McAdams, C.; Maunsell, J. (1999). Effects of attention on the reliability of individual neurons in monkey visual cortex. Neuron, 23(4), 765-773.
40. Melkonian, D.; Kostopoulos, G. (1996). Stochastic particle formulation of the vesicle hypothesis. Relevance to short-term phenomena. NeuroReport, 7(4), 937-942.
41. Merkel, M.; Lindner, B. (2010). Synaptic filtering of rate-coded information. Physical Review E, 81(4).
42. Moreno-Bote, R.; Parga, N. (2006). Auto-and crosscorrelograms for the spike response of leaky integrate-and-fire neurons with slow synapses. Physical Review Letters, 96(2), 101.
43. Moreno-Bote, R.; Parga, N. (2010). Response of integrate-and-fire neurons to noisy inputs filtered by synapses with arbitrary timescales: Firing rate and correlations. Neural Computation, 22(6), 1528-1572.
44. Moreno-Bote, R.; Renart, A.; Parga, N. (2008). Theory of input spike auto- and cross-correlations and their effect on the response of spiking neurons. Neural Computation, 20(7), 1651-1705.
45. Oswald, A.M.M.; Urban, N.N. (2012). Interactions between behaviorally relevant rhythms and synaptic plasticity alter coding in the piriform cortex. The Journal of Neuroscience, 32(18), 6092-6104.
46. Rav-Acha, M.; Sagiv, N.; Segev, I.; Bergman, H.; Yarom, Y. (2005). Dynamic and spatial features of the inhibitory pallidal GABAergic synapses. Journal of Neuroscience, 135(3), 791-802.
47. Rosenbaum, R. (2011). The transfer and propagation of correlated neuronal activity. Ph.D. thesis, University of Houston.
48. Rosenbaum, R.; Rubin, J.; Doiron, B. (2012). Short term synaptic depression imposes a frequency dependent filter on synaptic information transfer. PLoS Computational Biology, 8(6), e1002557.
49. Rothman, J.S.; Cathala, L.; Steuber, V.; Silver, R.A. (2009). Synaptic depression enables neuronal gain control. Nature, 457(7232), 1015-1018.
50. Rotman, Z.; Deng, P.Y.; Klyachko, V.A. (2011). Short-term plasticity optimizes synaptic information transmission. Journal of Neuroscience, 31(41), 14800-14809.
51. Salinas, E.; Sejnowski, T. (2000). Impact of correlated synaptic input on output firing rate and variability in simple neuronal models. Journal of Neuroscience, 20(16), 6193.
52. Salinas, E.; Sejnowski, T. (2002). Integrate-and-fire neurons driven by correlated stochastic input. Neural computation, 14(9), 2111-2155.
53. Senn, W.; Markram, H.; Tsodyks, M. (2001). An algorithm for modifying neurotransmitter release probability based on pre- and postsynaptic spike timing. Neural Computation, 13(1), 35-67.
54. Shadlen, M.; Newsome, W. (1998a). Noise, neural codes and cortical organization. Findings and Current Opinion in Cognitive Neuroscience, 4, 569-579.
55. Shadlen, M.; Newsome, W. (1998b). The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. Journal of Neuroscience, 18(10), 3870-3896.
56. Softky, W.; Koch, C. (1993). The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. Journal of Neuroscience, 13(1), 334-350.
57. Stevens, C.; Zador, A. (1998). Input synchrony and the irregular firing of cortical neurons. Nature Neuroscience, 1(3), 210-217.
58. Stratonovich, R.; Silverman, R. (1967). Topics in the theory of random noise (Vol. 2). New York: Gordon and Breach.
59. Stratton, P.; Wiles, J. (2007). Comparing kurtosis score to traditional statistical metrics for characterizing the structure in neural ensemble activity. In Dynamic brain-from neural spikes to behaviors, (pp. 115-122). New York: Springer-Verlag.
60. Tetzlaff, T.; Rotter, S.; Stark, E.; Abeles, M.; Aertsen, A.; Diesmann, M. (2008). Dependence of neuronal correlations on filter characteristics and marginal spike train statistics. Neural Computation, 20(9), 2133-2184.
61. Tolhurst, D.; Movshon, J.; Dean, A. (1983). The statistical reliability of signals in single neurons in cat and monkey visual cortex. Vision Research, 23(8), 775-785.
62. Tsodyks, M.; Pawelzik, K.; Markram, H. (1998). Neural networks with dynamic synapses. Neural Computation, 10(4), 821-835.
63. Tsodyks, M.; Uziel, A.; Markram, H. (2000). Synchrony generation in recurrent networks with frequency-dependent synapses. Journal Neuroscience, 20(1), 825-835.
64. Tsodyks, M.V.; Markram, H. (1997). The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proceedings of the National Academy of Sciences of the United States of America, 94(2), 719-723.
65. Van Vreeswijk, C.; Sompolinsky, H. (1996). Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science, 274(5293), 1724-1726.
66. Varela, J.A.; Sen, K.; Gibson, J.; Fost, J.; Abbott, L.F.; Nelson, S.B. (1997). A quantitative description of short-term plasticity at excitatory synapses in layer 2/3 of rat primary visual cortex. Journal of Neuroscience, 17(20), 7926-7940.
67. Vere-Jones, D. (1966). Simple stochastic models for the release of quanta of transmitter from a nerve terminal. Australian & New Zealand Journal of Statistics, 8(2), 53-63.
68. Vreeswijk, C.; Sompolinsky, H. (1998). Chaotic balanced state in a model of cortical circuits. Neural Computation, 10(6), 1321-1371.
69. Wang, X.J. (1999). Fast burst firing and short-term synaptic plasticity: a model of neocortical chattering neurons. Journal of Neuroscience, 89(2), 347-362.
70. Zador, A. (1998). Impact of synaptic unreliability on the information transmitted by spiking neurons. Journal of Neurophysiology, 79(3), 1219.
71. Zohary, E.; Shadlen, M.; Newsome, W. (1994). Correlated neuronal discharge rate and its implications for psychophysical performance. Nature, 370(6485), 140-143. doi: 10.1038/370140a0.
72. Zucker, R.; Regehr, W. (2002). Short-term synaptic plasticity. Annual Review of Physiology, 64(1), 355-405.