Poisson-Like Spiking in Circuits with Probabilistic Synapses

PLoS Computational Biology

Volumn 10, Issue 7, 2014, Pages

  Moreno Bote, Rubén ^a,b  

^a PARC SANITARI SANT JOAN DE DÉU   (Spain)

^b CENTRO DE INVESTIGACIÓN BIOMÉDICA EN RED DE SALUD MENTAL CIBERSAM   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

NEURAL NETWORKS; NEURONS;

CORTEXES; FIRING RATES; NEURONAL ACTIVITIES; NEURONAL NETWORKS; ORDERS OF MAGNITUDE; PROBABILISTICS; PROPERTY; SOURCES OF VARIABILITY; SYNAPTIC TRANSMISSION; WITHOUT FINE-TUNING;

TIMING CIRCUITS;

ARTICLE; BRAIN CELL; BRAIN CORTEX; CONDUCTANCE; MEMBRANE POTENTIAL; NERVE CELL NETWORK; POISSON DISTRIBUTION; SIMULATION; SPIKE; SYNAPSE; SYNAPTIC TRANSMISSION; WHITE NOISE; ACTION POTENTIAL; BIOLOGICAL MODEL; ELECTRIC CONDUCTIVITY; NERVE CELL; PHYSIOLOGY; STATISTICAL MODEL;

ACTION POTENTIALS; CEREBRAL CORTEX; ELECTRIC CONDUCTIVITY; MEMBRANE POTENTIALS; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; NEURONS; POISSON DISTRIBUTION; SYNAPSES;

EID: 84905474725     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1003522     Document Type: Article

References (69)

1. Faisal AA, Selen LP, Wolpert DM, (2008) Noise in the nervous system. Nat Rev Neurosci 9: 292-303.
2. Tolhurst DJ, Movshon JA, Dean AF, (1983) The statistical reliability of signals in single neurons in cat and monkey visual cortex. Vision Res 23: 775-785.
3. Shadlen MN, Newsome WT, (1998) The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J Neurosci 18: 3870-3896.
4. Softky WR, Koch C, (1993) The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J Neurosci 13: 334-350.
5. Carandini M, (2004) Amplification of trial-to-trial response variability by neurons in visual cortex. PLoS Biol 2: E264.
6. Gur M, Beylin A, Snodderly DM, (1997) Response variability of neurons in primary visual cortex (V1) of alert monkeys. J Neurosci 17: 2914-2920.
7. Geisler WS, Albrecht DG, (1997) Visual cortex neurons in monkeys and cats: detection, discrimination, and identification. Vis Neurosci 14: 897-919.
8. Shadlen MN, Britten KH, Newsome WT, Movshon JA, (1996) A computational analysis of the relationship between neuronal and behavioral responses to visual motion. The Journal of Neuroscience 16: 1486-1510.
9. van Vreeswijk C, Sompolinsky H, (1996) Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science 274: 1724-1726.
10. Amit DJ, Brunel N, (1997) Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. Cereb Cortex 7: 237-252.
11. Destexhe A, Rudolph M, Pare D, (2003) The high-conductance state of neocortical neurons in vivo. Nat Rev Neurosci 4: 739-751.
12. Renart A, Moreno-Bote R, Wang XJ, Parga N, (2007) Mean-driven and fluctuation-driven persistent activity in recurrent networks. Neural Comput 19: 1-46.
13. Barbieri F, Brunel N, (2007) Irregular persistent activity induced by synaptic excitatory feedback. Front Comput Neurosci 1: 5.
14. Roudi Y, Latham PE, (2007) A balanced memory network. PLoS Comput Biol 3: 1679-1700.
15. Hansel D, Mato G, (2013) Short-term plasticity explains irregular persistent activity in working memory tasks. J Neurosci 33: 133-149.
16. Compte A, Constantinidis C, Tegner J, Raghavachari S, Chafee MV, et al. (2003) Temporally irregular mnemonic persistent activity in prefrontal neurons of monkeys during a delayed response task. J Neurophysiol 90: 3441-3454.
17. Shinomoto S, Sakai Y, Funahashi S, (1999) The Ornstein-Uhlenbeck process does not reproduce spiking statistics of neurons in prefrontal cortex. Neural Comput 11: 935-951.
18. Lerchner A, Ursta C, Hertz J, Ahmadi M, Ruffiot P, et al. (2006) Response variability in balanced cortical networks. Neural Comput 18: 634-659.
19. Sestokas AK, Lehmkuhle S, (1988) Response variability of X- and Y-cells in the dorsal lateral geniculate nucleus of the cat. J Neurophysiol 59: 317-325.
20. Sadagopan S, Ferster D, (2012) Feedforward origins of response variability underlying contrast invariant orientation tuning in cat visual cortex. Neuron 74: 911-923.
21. Allen C, Stevens CF, (1994) An evaluation of causes for unreliability of synaptic transmission. Proc Natl Acad Sci U S A 91: 10380-10383.
22. Branco T, Staras K, (2009) The probability of neurotransmitter release: variability and feedback control at single synapses. Nat Rev Neurosci 10: 373-383.
23. Borst JG, (2010) The low synaptic release probability in vivo. Trends Neurosci 33: 259-266.
24. Dobrunz LE, Stevens CF, (1999) Response of hippocampal synapses to natural stimulation patterns. Neuron 22: 157-166.
25. Zucker RS, Regehr WG, (2002) Short-term synaptic plasticity. Annu Rev Physiol 64: 355-405.
26. Markram H, Tsodyks M, (1996) Redistribution of synaptic efficacy between neocortical pyramidal neurons. Nature 382: 807-810.
27. Abbott LF, Varela JA, Sen K, Nelson SB, (1997) Synaptic depression and cortical gain control. Science 275: 220-224.
28. Mainen ZF, Sejnowski TJ, (1995) Reliability of spike timing in neocortical neurons. Science 268: 1503-1506.
29. Stiefel KM, Englitz B, Sejnowski TJ, (2013) Origin of intrinsic irregular firing in cortical interneurons. Proc Natl Acad Sci U S A 110: 7886-7891.
30. Anderson JS, Lampl I, Gillespie DC, Ferster D, (2000) The contribution of noise to contrast invariance of orientation tuning in cat visual cortex. Science 290: 1968-1972.
31. Moreno R, de la Rocha J, Renart A, Parga N, (2002) Response of spiking neurons to correlated inputs. Physical Review Letters 89: 288101.
32. Lerchner A, Sterner G, Hertz J, Ahmadi M, (2006) Mean field theory for a balanced hypercolumn model of orientation selectivity in primary visual cortex. Network 17: 131-150.
33. Rajan K, Abbott LF, Sompolinsky H, (2010) Stimulus-dependent suppression of chaos in recurrent neural networks. Phys Rev E Stat Nonlin Soft Matter Phys 82: 011903.
34. Mason A, Nicoll A, Stratford K, (1991) Synaptic transmission between individual pyramidal neurons of the rat visual cortex in vitro. J Neurosci 11: 72-84.
35. Komatsu Y, Nakajima S, Toyama K, Fetz EE, (1988) Intracortical connectivity revealed by spike-triggered averaging in slice preparations of cat visual cortex. Brain Res 442: 359-362.
36. Boudreau CE, Ferster D, (2005) Short-term depression in thalamocortical synapses of cat primary visual cortex. J Neurosci 25: 7179-7190.
37. Brunel N, (2000) Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J Comput Neurosci 8: 183-208.
38. Faisal AA, White JA, Laughlin SB, (2005) Ion-channel noise places limits on the miniaturization of the brain's wiring. Curr Biol 15: 1143-1149.
39. Faisal AA, Laughlin SB, (2007) Stochastic simulations on the reliability of action potential propagation in thin axons. PLoS Comput Biol 3: e79.
40. Mattia M, Del Giudice P, (2002) Mean-field population dynamics of spiking neurons with random synaptic delays. Artificial Neural Networks- ICANN 2002: 111-116.
41. Moreno-Bote R, Parga N, (2005) Membrane potential and response properties of populations of cortical neurons in the high conductance state. Physical Review Letters 94: 088103.
42. Borg-Graham LJ, Monier C, Fregnac Y, (1998) Visual input evokes transient and strong shunting inhibition in visual cortical neurons. Nature 393: 369-373.
43. Kohn A, Smith MA, (2005) Stimulus dependence of neuronal correlation in primary visual cortex of the macaque. J Neurosci 25: 3661-3673.
44. Britten KH, Shadlen MN, Newsome WT, Movshon JA, (1992) The analysis of visual motion: a comparison of neuronal and psychophysical performance. J Neurosci 12: 4745-4765.
45. Baylor DA, Lamb TD, Yau KW, (1979) Responses of retinal rods to single photons. J Physiol 288: 613-634.
46. Cohen MR, Maunsell JH, (2009) Attention improves performance primarily by reducing interneuronal correlations. Nat Neurosci 12: 1594-1600.
47. Mitchell JF, Sundberg KA, Reynolds JH, (2009) Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4. Neuron 63: 879-888.
48. London M, Roth A, Beeren L, Hausser M, Latham PE, (2010) Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex. Nature 466: 123-127.
49. Stevens CF, Zador AM, (1998) Input synchrony and the irregular firing of cortical neurons. Nat Neurosci 1: 210-217.
50. de la Rocha J, Moreno R, Parga N, (2004) Correlations modulate the non-monotonic response of a neuron with short-term plasticity. Neurocomputing 58: 313-319.
51. de la Rocha J, Parga N, (2005) Short-term synaptic depression causes a non-monotonic response to correlated stimuli. J Neurosci 25: 8416-8431.
52. Goldman MS, Maldonado P, Abbott LF, (2002) Redundancy reduction and sustained firing with stochastic depressing synapses. J Neurosci 22: 584-591.
53. Li C, Zheng Q, (2010) Synchronization of the small-world neuronal network with unreliable synapses. Phys Biol 7: 036010.
54. Mejias JF, Kappen HJ, Torres JJ, (2010) Irregular dynamics in up and down cortical states. PLoS One 5: e13651.
55. Rosenbaum R, Josic K, (2011) Mechanisms that modulate the transfer of spiking correlations. Neural Comput 23: 1261-1305.
56. Reich S, Rosenbaum R, (2013) The impact of short term synaptic depression and stochastic vesicle dynamics on neuronal variability. J Comput Neurosci 35: 39-53.
57. Rosenbaum R, Rubin JE, Doiron B, (2013) Short-term synaptic depression and stochastic vesicle dynamics reduce and shape neuronal correlations. J Neurophysiol 109: 475-484.
58. Churchland MM, Yu BM, Cunningham JP, Sugrue LP, Cohen MR, et al. (2010) Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nat Neurosci 13: 369-378.
59. Litwin-Kumar A, Doiron B, (2012) Slow dynamics and high variability in balanced cortical networks with clustered connections. Nat Neurosci 15: 1498-1505.
60. Deco G, Hugues E, (2012) Neural network mechanisms underlying stimulus driven variability reduction. PLoS Comput Biol 8: e1002395.
61. Riani M, Simonotto E, (1994) Stochastic resonance in the perceptual interpretation of ambiguous figures: A neural network model. Physical Review Letters 72: 3120-3123.
62. Seung HS, (2003) Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron 40: 1063-1073.
63. Fusi S, Drew PJ, Abbott LF, (2005) Cascade models of synaptically stored memories. Neuron 45: 599-611.
64. Ma WJ, Beck JM, Latham PE, Pouget A, (2006) Bayesian inference with probabilistic population codes. Nat Neurosci 9: 1432-1438.
65. Moreno-Bote R, Knill DC, Pouget A, (2011) Bayesian sampling in visual perception. Proc Natl Acad Sci U S A 108: 12491-12496.
66. Brunel N, Wang XJ, (2003) What determines the frequency of fast network oscillations with irregular neural discharges? I. Synaptic dynamics and excitation-inhibition balance. J Neurophysiol 90: 415-430.
67. Kriener B, Tetzlaff T, Aertsen A, Diesmann M, Rotter S, (2008) Correlations and population dynamics in cortical networks. Neural Comput 20: 2185-2226.
68. Vogels TP, Abbott LF, (2009) Gating multiple signals through detailed balance of excitation and inhibition in spiking networks. Nat Neurosci 12: 483-491.
69. Reyes AD, (2011) Synaptic short-term plasticity in auditory cortical circuits. Hear Res 279: 60-66.