Free Access
Math. Model. Nat. Phenom.
Volume 5, Number 2, 2010
Mathematics and neuroscience
Page(s) 100 - 124
Published online 10 March 2010
  1. O. Ávila ÅkerbergM.J. Chacron. Noise shaping in neural populations. Phys. Rev. E, 79 (2009), 011904. [CrossRef] [Google Scholar]
  2. S. Bahar, J.W. Kantelhardt, A. Neiman, H.H.A. Rego, D.F. Russell, L. Wilkens, A. Bunde and F. Moss. Long range temporal anti-correlations in paddlefish electroreceptors. Europhys. Lett., 56 (2001), 454–460. [CrossRef] [Google Scholar]
  3. A. BorstF. Theunissen. Information theory and neural coding. Nat. Neurosci. 2 (1999), 947–957. [Google Scholar]
  4. V. Braitenberg, A. Schüz. Anatomy of the Cortex. Springer, Berlin, 1991. [Google Scholar]
  5. A. Bulsara, P. Hänggi, F. Marchesoni, F. MossM. Shlesinger. Special Issue for Proceedings of The Nato Advanced Research WorkshopStochastic Resonance in Physics and Biology. J. Stat. Phys., 70 (1993), 1–2. [CrossRef] [Google Scholar]
  6. R.S. Cajal. Histologie du système nerveux de l’Homme et des vertébrés. Paris, Maloine, 1909. [Google Scholar]
  7. M.J. Chacron, A. Longtin, M. St-HilaireL. Maler. Suprathreshold stochastic firing dynamics with memory in P-type electroreceptors. Phys. Rev. Lett., 85 (2000), 1576–1579. [CrossRef] [PubMed] [Google Scholar]
  8. M.J. Chacron, L. MalerJ. Bastian. Electroreceptor neuron dynamics shape information transmission. Nat. Neurosci., 8 (2005), 673–678. [CrossRef] [PubMed] [Google Scholar]
  9. M.J. Chacron, A. LongtinL. Maler. Negative interspike interval correlations increase the neuronal capacity for encoding time-dependent stimuli. J. Neurosci., 21 (2001), 5328–5343. [PubMed] [Google Scholar]
  10. M.J. Chacron, B. LindnerA. Longtin. Noise shaping by interval correlations increases information transfer. Phys. Rev. Lett., 92 (2004), 080601. [Google Scholar]
  11. M.J. Chacron, B. Lindner, A. Longtin. ISI Correlations and Information Transfer. Fluct. Noise Lett., 4 (2004) L195–L205. [CrossRef] [Google Scholar]
  12. M.J. Chacron, A. LongtinL. Maler. Delayed excitatory and inhibitory feedback shape neural information transmission. Phys. Rev. E, 72 (2005), 051917. [Google Scholar]
  13. M.J. Chacron, A. LongtinL. Maler. The effects of spontaneous activity, background noise, and the stimulus ensemble on information transfer in neurons. Network, 14 (2003), 803–824. [CrossRef] [PubMed] [Google Scholar]
  14. M.J. Chacron, B. Doiron, L. Maler, A. LongtinJ. Bastian. Non-classical receptive field mediates switch in a sensory neuron’s frequency tuning. Nature, 423 (2003), 77–81. [CrossRef] [PubMed] [Google Scholar]
  15. M.J. Chacron, L. MalerJ. Bastian. Feedback and feedforward control of frequency tuning to naturalistic stimuli. J. Neurosci., 25 (2005), 5521–5532. [CrossRef] [PubMed] [Google Scholar]
  16. M.J. Chacron. Nonlinear information processing in a model sensory system. J. Neurophysiol., 95 (2006), 2933–2946. [CrossRef] [PubMed] [Google Scholar]
  17. M.J. Chacron, B. LindnerA. Longtin. Threshold fatigue and information transfer. J. Comput. Neurosci., 23 (2007), 301–311. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  18. M.J. ChacronJ. Bastian. Population coding by electrosensory neurons. J. Neurophysiol., 99 (2008), 1825–1835. [CrossRef] [PubMed] [Google Scholar]
  19. T. Cover, J. Thomas. Elements of Information Theory, Wiley, New-York, 1991. [Google Scholar]
  20. B. Doiron, M.J. Chacron, L. Maler, A. LongtinJ. Bastian. Inhibitory feedback required for network oscillatory responses to communication but not prey stimuli. Nature, 421 (2003), 539–543. [CrossRef] [PubMed] [Google Scholar]
  21. B. Doiron, B. Lindner, A. Longtin, L. MalerJ. Bastian. Oscillatory activity in electrosensory neurons increases with the spatial correlation of the stochastic input stimulus. Phys. Rev. Lett., 93 (2004), 048101. [Google Scholar]
  22. L.D. Ellis, R. Krahe, C.M. Bourque, R.J. DunnM.J. Chacron. Muscarinic receptors control frequency tuning through the downregulation of an A-type potassium current. J. Neurophysiol., 98 (2007), 1526–1537. [CrossRef] [PubMed] [Google Scholar]
  23. T.A. Engel, B. Helbig, D.F. Russell, L. Schimansky-GeierA.B. Neiman. Coherent stochastic oscillations enhance signal detection in spiking neurons. Phys. Rev. E, 80 (2009), 021919. [CrossRef] [Google Scholar]
  24. F. Farkhooi, M.F. Strube-BlossM.P. Nawrot. Serial correlation in neural spike trains: Experimental evidence, stochastic modeling, and single neuron variability. Phys. Rev. E, 79 (2009), 021905. [Google Scholar]
  25. L. Gammaitoni, P.Hänggi, P. Jung, F. Marchesoni. Stochastic resonance. Rev. Mod. Phys., 70 (1998), 223–287. [Google Scholar]
  26. L. Glass, M.C. Mackey. From Clocks to Chaos. Princeton Univ. Press, Princeton, 1988. [Google Scholar]
  27. J.B.M. GoenseR. Ratnam. Continuous detection of weak sensory signals in afferent spike trains: the role of anti-correlated interspike intervals in detection performance. J. Comp. Physiol. A, 189 (2003), 741–759. [CrossRef] [Google Scholar]
  28. C. GrayW. Singer. Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. Proc. Natl Acad. Sci. USA, 86 (1989), 1698–1702. [Google Scholar]
  29. N.B. Janson, A.G. Balanov, E. Schöll. Delayed Feedback as a Means of Control of Noise-Induced Motion. Phys. Rev. Lett., 93 (2004), 010601. [Google Scholar]
  30. A. V. Holden. Models of the Stochastic Activity of Neurons. Springer, Berlin, 1976. [Google Scholar]
  31. H. Hollander. The projection from the visual cortex to the lateral geniculate body (LGB). An experimental study with silver impregnation methods in the cat. Exp. Brain Res., 10 (1990), 219–235. [Google Scholar]
  32. B. HutcheonY. Yarom Resonance, oscillation and the intrinsic frequency preferences of neurons. Trends Neurosci., 23 (2000), 216–222. [CrossRef] [PubMed] [Google Scholar]
  33. E.M. Izhikevich Neural Excitability, Spiking, and Bursting. Int. J. Bif. Chaos, 10 (2000), 1171–1266. [Google Scholar]
  34. H. Kashiwadani, Y.F. Sasaki, N. UchidaK. Mori. Synchronized oscillatory discharges of mitral/tufted cells with different molecular receptive ranges in the rabbit olfactory bulb. J. Neurophysiol., 82 (1999), 1786–1792. [PubMed] [Google Scholar]
  35. Z.F. Kisvárday, K.A. Martin, T.F. Freund, Z. Maglóczky, D. WhitteridgeP. Somogyi. Synaptic targets of HRP-filled layer III pyramidal cells in the cat striate cortex. Exp. Brain. Res., 64 (1986), 541–552. [CrossRef] [PubMed] [Google Scholar]
  36. W.R. KlemmC.J. Sherry. Entropy as an index of the informational state of neurons. Int. J. Neurosci., 15 (1981), 171–178. [CrossRef] [PubMed] [Google Scholar]
  37. H. KornP. Faure. Is there chaos in the brain? I. Concepts of nonlinear dynamics and methods of investigation. C. R. Acad. Sci. III, 324 (2003), 773–793. [Google Scholar]
  38. R. Krahe, J. BastianM.J. Chacron. Temporal processing across multiple topographic maps in the electrosensory system. J. Neurophysiol., 100 (2008), 852–867. [CrossRef] [PubMed] [Google Scholar]
  39. M.A. LebedevR.J. Nelson. High-frequency vibratory sensitive neurons in monkey primary somatosensory cortex: entrained and nonentrained responses to vibration during the performance of vibratory-cued hand movements. Exp. Brain Res., 111 (1996), 313–325. [PubMed] [Google Scholar]
  40. B. Lindner, M.J. ChacronA. Longtin. Integrate-and-fire neurons with threshold noise: a tractable model of how interspike interval correlations affect neuronal signal transmission. Phys. Rev. E, 72 (2005), 021911. [Google Scholar]
  41. B. Lindner, B. DoironA. Longtin. Theory of oscillatory firing induced by spatially correlated noise and delayed inhibitory feedback. Phys. Rev. E, 72 (2005), 061919. [Google Scholar]
  42. B. Lindner, D. Gangloff, A. LongtinJ.E. Lewis. Broadband Coding with Dynamic Synapses. J. Neurosci., 29 (2004), 2076–2087. [CrossRef] [Google Scholar]
  43. S.B. LowenM.C. Teich. Auditory-nerve action potentials form a nonrenewal point process over short as well as long time scales. J. Accoust. Soc. Am., 92 (1992), 803–806. [CrossRef] [PubMed] [Google Scholar]
  44. N. LüdtkeM.E. Nelson. Short-term synaptic plasticity can enhance weak signal detectability in nonrenewal spike trains. Neural Comput., 18 (2006), 2879–2916. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  45. K. MacLeodG. Laurent. Distinct mechanisms for synchronization and temporal patterning of odor-encoding neural assemblies. Science, 274 (1996), 976–979. [CrossRef] [PubMed] [Google Scholar]
  46. Z.F. MainenT. J. Sejnowski. Reliability of spike timing in neocortical neurons. Science, 268 (1995), 1503–1506. [CrossRef] [PubMed] [Google Scholar]
  47. L. MalerE. Mugnaini. Organization and function of feedback to the electrosensory lateral line lobe of gymnotiform fish, with emphasis on a searchlight mechanism. J. Comp. Physiol. A, 173 (1993), 667–670. [Google Scholar]
  48. L. MalerE. Mugnaini. Correlating gamma-aminobutyric circuits and sensory function in the electrosensory lateral line lobe of a gymnotiform fish. J. Comp. Neurol., 345 (1994), 224–252. [CrossRef] [PubMed] [Google Scholar]
  49. D.J. Mar, C.C. Chow, W. Gerstner, R.W. AdamsJ.J. Collins. Noise shaping in populations of coupled model neurons. Proc. Natl. Acad. Sci., 96 (1999), 10450–10455. [CrossRef] [Google Scholar]
  50. G. MarsatG.S. Pollack. Effect of the temporal pattern of contralateral inhibition on sound localization cues. J. Neurosci., 25 (2005), 6137–6144. [CrossRef] [PubMed] [Google Scholar]
  51. M. MattiaP. Del Giudice. Finite-size dynamics of inhibitory and excitatory interacting spiking neurons. Phys. Rev. E, 70 (2004), 052903. [CrossRef] [Google Scholar]
  52. J.W. Middleton, M.J. Chacron, B. LindnerA. Longtin. Firing statistics of a neuron model driven by long-range correlated noise. Phys. Rev. E, 68 (2003), 021920. [CrossRef] [Google Scholar]
  53. B.A. McGuire, J.P. Hornung, C.D. GilbertT.N. Wiesel. Patterns of synaptic input to layer 4 of cat striate cortex. J. Neurosci., 4 (1984), 3021–3033. [PubMed] [Google Scholar]
  54. F. Moss, L. WardW. Sannita. Stochastic resonance and sensory information processing: a tutorial and review of application. Clin. Neurophysiol., 115 (2004), 267–281. [Google Scholar]
  55. M.E. NelsonM.A. MacIver. Prey capture in the weakly electric fish Apteronotus albifrons: sensory acquisition strategies and electrosensory consequences. J. Exp. Biol., 202 (1999), 1195–1203. [PubMed] [Google Scholar]
  56. S.R. Norsworthy, R. Schreier, G. C. Temes. Delta-Sigma Data Converters. IEEE Press, Piscataway, 1997. [Google Scholar]
  57. E.M. Ostapoff, D.K. MorestS.J. Potashner. Uptake and retrograde transport of [ 3H]GABA from the cochlear nucleus to the superior olive in the guinea pig. J. Chem. Neuroanat., 3 (1990), 285–295. [PubMed] [Google Scholar]
  58. C.L. PassagliaJ.B. Troy. Information transmission rates of cat retinal ganglion cells. J. Neurophysiol., 91 (2004), 1217–1229. [PubMed] [Google Scholar]
  59. A. PototskyN. Janson. Excitable systems with noise and delay, with applications to control: Renewal theory approach. Phys. Rev. E, 77 (2008), 031113. [CrossRef] [MathSciNet] [Google Scholar]
  60. T. Prager, H.P. Lerch, L. Schimansky-GeierE. Schöll. Increase of coherence in excitable systems by delayed feedback. J. Phys. A , 40 (2007), 11045–11055. [CrossRef] [MathSciNet] [Google Scholar]
  61. R. RatnamM.E. Nelson. Nonrenewal statistics of electrosensory afferent spike trains: implications for the detection of weak sensory signals. J. Neurosci., 20 (2000), 6672–6683. [PubMed] [Google Scholar]
  62. F. Rieke, D. Warland, R.R. de Ruyter van Steveninck, W. Bialek. Spikes: Exploring the Neural Code. MIT press, Cambridge, MA, 1996. [Google Scholar]
  63. H. Risken. The Fokker-Planck Equation. Springer, Berlin, 1996. [Google Scholar]
  64. J.C. Roddey, B. GirishJ.P. Miller. Assessing the performance of neural encoding models in the presence of noise. J. Comput. Neurosci., 8 (2000), 95–112. [CrossRef] [PubMed] [Google Scholar]
  65. S. Sadeghi, M.J. Chacron, M.C. TaylorK.E. Cullen. Neural variability, detection thresholds, and information transmission in the vestibular system. J. Neurosci., 27 (2007), 771–781. [CrossRef] [PubMed] [Google Scholar]
  66. A.M. Sillito, H.E. Jones, G.L. GersteinD.C. West. Feature-linked synchronization of thalamic relay cell firing induced by feedback from the visual cortex. Nature, 369 (1994), 479–482. [CrossRef] [PubMed] [Google Scholar]
  67. R. Shannon. The mathematical theory of communication. Bell. Syst. Tech. J., 27 (1948), 379–423. [Google Scholar]
  68. S.M. Sherman. Tonic and burst firing: dual modes of thalamocortical relay. TINS, 24 (2001), 122–126. [Google Scholar]
  69. S.M. Sherman, R.W. Guillery. The role of the thalamus in the flow of information to the cortex. Philos. Trans. R. Soc. Lond. B Biol. Sci., 357 (2002), 1695–1708. [CrossRef] [PubMed] [Google Scholar]
  70. J. Shin. Adaptation in spiking neurons based on the noise shaping neural coding hypothesis. Neural Networks, 14 (2001), 907–919. [CrossRef] [Google Scholar]
  71. N.G. Stocks. Suprathreshold stochastic resonance in multilevel threshold systems. Phys. Rev. Lett., 84 (2000), 2310–2313. [Google Scholar]
  72. M. Stopfer, S. Bhagavan, B.H. SmithG. Laurent. Impaired odour discrimination on desynchronization of odour-encoding neural assemblies. Nature, 390 (1997), 70–74. [CrossRef] [PubMed] [Google Scholar]
  73. A.M. Yacomotti, M.C. Eguia, J. Aliaga, O.E. MartinezG.B. Mindlin. Interspike Time Distribution in Noise Driven Excitable Systems. Phys. Rev. Lett., 83 (1999), 292–295. [CrossRef] [Google Scholar]
  74. M.K.S. YeungS.H. Strogatz. Time Delay in the Kuramoto Model of Coupled Oscillators. Phys. Rev. Lett., 82 (1999), 648–651. [Google Scholar]
  75. K. WiesenfeldI. Satija. Noise tolerance of frequency-locked dynamics. Phys. Rev. B, 36 (1987), 2483–2492. [CrossRef] [Google Scholar]

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