Noise Shaping in Neural Populations with Global Delayed Feedback

O. Ávila Åkerberg; M. J. Chacron

Mathematical Modelling of Natural Phenomena (2010)

  • Volume: 5, Issue: 2, page 100-124
  • ISSN: 0973-5348

Abstract

top
The interplay between intrinsic and network dynamics has been the focus of many investigations. Here we use a combination of theoretical and numerical approaches to study the effects of delayed global feedback on the information transmission properties of neural networks. Specifically, we compare networks of neurons that display intrinsic interspike interval correlations (nonrenewal) to networks that do not (renewal). We find that excitatory and inhibitory delays can tune information transmission by single neurons but not by the entire network. Most surprisingly, addition of a delay can change the dependence of the information on the coupling strength for renewal neurons and not for nonrenewal neurons. Our results show that intrinsic ISI correlations can have nontrivial interactions with network-induced phenomena.

How to cite

top

Ávila Åkerberg, O., and Chacron, M. J.. "Noise Shaping in Neural Populations with Global Delayed Feedback." Mathematical Modelling of Natural Phenomena 5.2 (2010): 100-124. <http://eudml.org/doc/197625>.

@article{ÁvilaÅkerberg2010,
abstract = {The interplay between intrinsic and network dynamics has been the focus of many investigations. Here we use a combination of theoretical and numerical approaches to study the effects of delayed global feedback on the information transmission properties of neural networks. Specifically, we compare networks of neurons that display intrinsic interspike interval correlations (nonrenewal) to networks that do not (renewal). We find that excitatory and inhibitory delays can tune information transmission by single neurons but not by the entire network. Most surprisingly, addition of a delay can change the dependence of the information on the coupling strength for renewal neurons and not for nonrenewal neurons. Our results show that intrinsic ISI correlations can have nontrivial interactions with network-induced phenomena.},
author = {Ávila Åkerberg, O., Chacron, M. J.},
journal = {Mathematical Modelling of Natural Phenomena},
keywords = {information theory; neural networks; nonrenewal; delay},
language = {eng},
month = {3},
number = {2},
pages = {100-124},
publisher = {EDP Sciences},
title = {Noise Shaping in Neural Populations with Global Delayed Feedback},
url = {http://eudml.org/doc/197625},
volume = {5},
year = {2010},
}

TY - JOUR
AU - Ávila Åkerberg, O.
AU - Chacron, M. J.
TI - Noise Shaping in Neural Populations with Global Delayed Feedback
JO - Mathematical Modelling of Natural Phenomena
DA - 2010/3//
PB - EDP Sciences
VL - 5
IS - 2
SP - 100
EP - 124
AB - The interplay between intrinsic and network dynamics has been the focus of many investigations. Here we use a combination of theoretical and numerical approaches to study the effects of delayed global feedback on the information transmission properties of neural networks. Specifically, we compare networks of neurons that display intrinsic interspike interval correlations (nonrenewal) to networks that do not (renewal). We find that excitatory and inhibitory delays can tune information transmission by single neurons but not by the entire network. Most surprisingly, addition of a delay can change the dependence of the information on the coupling strength for renewal neurons and not for nonrenewal neurons. Our results show that intrinsic ISI correlations can have nontrivial interactions with network-induced phenomena.
LA - eng
KW - information theory; neural networks; nonrenewal; delay
UR - http://eudml.org/doc/197625
ER -

References

top
  1. O. Ávila Åkerberg M.J. Chacron. Noise shaping in neural populations. Phys. Rev. E, 79 (2009), 011904. 
  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. 
  3. A. Borst F. Theunissen. Information theory and neural coding. Nat. Neurosci.2 (1999), 947–957. 
  4. V. Braitenberg, A. Schüz. Anatomy of the Cortex. Springer, Berlin, 1991.  
  5. A. Bulsara, P. Hänggi, F. Marchesoni, F. Moss M. Shlesinger. Special Issue for Proceedings of The Nato Advanced Research WorkshopStochastic Resonance in Physics and Biology. J. Stat. Phys., 70 (1993), 1–2. 
  6. R.S. Cajal. Histologie du système nerveux de l’Homme et des vertébrés. Paris, Maloine, 1909.  
  7. M.J. Chacron, A. Longtin, M. St-Hilaire L. Maler. Suprathreshold stochastic firing dynamics with memory in P-type electroreceptors. Phys. Rev. Lett., 85 (2000), 1576–1579. 
  8. M.J. Chacron, L. Maler J. Bastian. Electroreceptor neuron dynamics shape information transmission. Nat. Neurosci., 8 (2005), 673–678. 
  9. M.J. Chacron, A. Longtin L. Maler. Negative interspike interval correlations increase the neuronal capacity for encoding time-dependent stimuli. J. Neurosci., 21 (2001), 5328–5343. 
  10. M.J. Chacron, B. Lindner A. Longtin. Noise shaping by interval correlations increases information transfer. Phys. Rev. Lett., 92 (2004), 080601. 
  11. M.J. Chacron, B. Lindner, A. Longtin.ISI Correlations and Information Transfer. Fluct. Noise Lett., 4 (2004) L195–L205.  
  12. M.J. Chacron, A. Longtin L. Maler. Delayed excitatory and inhibitory feedback shape neural information transmission. Phys. Rev. E, 72 (2005), 051917. 
  13. M.J. Chacron, A. Longtin L. Maler. The effects of spontaneous activity, background noise, and the stimulus ensemble on information transfer in neurons. Network, 14 (2003), 803–824. 
  14. M.J. Chacron, B. Doiron, L. Maler, A. Longtin J. Bastian. Non-classical receptive field mediates switch in a sensory neuron’s frequency tuning. Nature, 423 (2003), 77–81. 
  15. M.J. Chacron, L. Maler J. Bastian. Feedback and feedforward control of frequency tuning to naturalistic stimuli. J. Neurosci., 25 (2005), 5521–5532. 
  16. M.J. Chacron. Nonlinear information processing in a model sensory system. J. Neurophysiol., 95 (2006), 2933–2946. 
  17. M.J. Chacron, B. Lindner A. Longtin. Threshold fatigue and information transfer. J. Comput. Neurosci., 23 (2007), 301–311. 
  18. M.J. Chacron J. Bastian. Population coding by electrosensory neurons. J. Neurophysiol., 99 (2008), 1825–1835. 
  19. T. Cover, J. Thomas. Elements of Information Theory, Wiley, New-York, 1991.  
  20. B. Doiron, M.J. Chacron, L. Maler, A. Longtin J. Bastian. Inhibitory feedback required for network oscillatory responses to communication but not prey stimuli. Nature, 421 (2003), 539–543. 
  21. B. Doiron, B. Lindner, A. Longtin, L. Maler J. Bastian. Oscillatory activity in electrosensory neurons increases with the spatial correlation of the stochastic input stimulus. Phys. Rev. Lett., 93 (2004), 048101. 
  22. L.D. Ellis, R. Krahe, C.M. Bourque, R.J. Dunn M.J. Chacron. Muscarinic receptors control frequency tuning through the downregulation of an A-type potassium current. J. Neurophysiol., 98 (2007), 1526–1537. 
  23. T.A. Engel, B. Helbig, D.F. Russell, L. Schimansky-Geier A.B. Neiman. Coherent stochastic oscillations enhance signal detection in spiking neurons. Phys. Rev. E, 80 (2009), 021919. 
  24. F. Farkhooi, M.F. Strube-Bloss M.P. Nawrot. Serial correlation in neural spike trains: Experimental evidence, stochastic modeling, and single neuron variability. Phys. Rev. E, 79 (2009), 021905. 
  25. L. Gammaitoni, P. Hänggi, P. Jung, F. Marchesoni. Stochastic resonance. Rev. Mod. Phys., 70 (1998), 223–287. 
  26. L. Glass, M.C. Mackey. From Clocks to Chaos. Princeton Univ. Press, Princeton, 1988.  
  27. J.B.M. Goense R. 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. 
  28. C. Gray W. Singer. Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. Proc. Natl Acad. Sci. USA, 86 (1989), 1698–1702. 
  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.  
  30. A. V. Holden. Models of the Stochastic Activity of Neurons. Springer, Berlin, 1976.  
  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. 
  32. B. Hutcheon Y. YaromResonance, oscillation and the intrinsic frequency preferences of neurons. Trends Neurosci., 23 (2000), 216–222. 
  33. E.M. IzhikevichNeural Excitability, Spiking, and Bursting. Int. J. Bif. Chaos, 10 (2000), 1171–1266. 
  34. H. Kashiwadani, Y.F. Sasaki, N. Uchida K. Mori. Synchronized oscillatory discharges of mitral/tufted cells with different molecular receptive ranges in the rabbit olfactory bulb. J. Neurophysiol., 82 (1999), 1786–1792. 
  35. Z.F. Kisvárday, K.A. Martin, T.F. Freund, Z. Maglóczky, D. Whitteridge P. Somogyi. Synaptic targets of HRP-filled layer III pyramidal cells in the cat striate cortex. Exp. Brain. Res., 64 (1986), 541–552. 
  36. W.R. Klemm C.J. Sherry. Entropy as an index of the informational state of neurons. Int. J. Neurosci., 15 (1981), 171–178. 
  37. H. Korn P. 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. 
  38. R. Krahe, J. Bastian M.J. Chacron. Temporal processing across multiple topographic maps in the electrosensory system. J. Neurophysiol., 100 (2008), 852–867. 
  39. M.A. Lebedev R.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. 
  40. B. Lindner, M.J. Chacron A. 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. 
  41. B. Lindner, B. Doiron A. Longtin. Theory of oscillatory firing induced by spatially correlated noise and delayed inhibitory feedback. Phys. Rev. E, 72 (2005), 061919. 
  42. B. Lindner, D. Gangloff, A. Longtin J.E. Lewis. Broadband Coding with Dynamic Synapses. J. Neurosci., 29 (2004), 2076–2087. 
  43. S.B. Lowen M.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. 
  44. N. Lüdtke M.E. Nelson. Short-term synaptic plasticity can enhance weak signal detectability in nonrenewal spike trains. Neural Comput., 18 (2006), 2879–2916. 
  45. K. MacLeod G. Laurent. Distinct mechanisms for synchronization and temporal patterning of odor-encoding neural assemblies. Science, 274 (1996), 976–979. 
  46. Z.F. Mainen T. J. Sejnowski. Reliability of spike timing in neocortical neurons. Science, 268 (1995), 1503–1506. 
  47. L. Maler E. 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. 
  48. L. Maler E. 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. 
  49. D.J. Mar, C.C. Chow, W. Gerstner, R.W. Adams J.J. Collins. Noise shaping in populations of coupled model neurons. Proc. Natl. Acad. Sci., 96 (1999), 10450–10455. 
  50. G. Marsat G.S. Pollack. Effect of the temporal pattern of contralateral inhibition on sound localization cues. J. Neurosci., 25 (2005), 6137–6144. 
  51. M. Mattia P. Del Giudice. Finite-size dynamics of inhibitory and excitatory interacting spiking neurons. Phys. Rev. E, 70 (2004), 052903. 
  52. J.W. Middleton, M.J. Chacron, B. Lindner A. Longtin. Firing statistics of a neuron model driven by long-range correlated noise. Phys. Rev. E, 68 (2003), 021920. 
  53. B.A. McGuire, J.P. Hornung, C.D. Gilbert T.N. Wiesel. Patterns of synaptic input to layer 4 of cat striate cortex. J. Neurosci., 4 (1984), 3021–3033. 
  54. F. Moss, L. Ward W. Sannita. Stochastic resonance and sensory information processing: a tutorial and review of application. Clin. Neurophysiol., 115 (2004), 267–281. 
  55. M.E. Nelson M.A. MacIver. Prey capture in the weakly electric fish Apteronotus albifrons: sensory acquisition strategies and electrosensory consequences. J. Exp. Biol., 202 (1999), 1195–1203. 
  56. S.R. Norsworthy, R. Schreier, G. C. Temes. Delta-Sigma Data Converters. IEEE Press, Piscataway, 1997.  
  57. E.M. Ostapoff, D.K. Morest S.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. 
  58. C.L. Passaglia J.B. Troy. Information transmission rates of cat retinal ganglion cells. J. Neurophysiol., 91 (2004), 1217–1229. 
  59. A. Pototsky N. Janson. Excitable systems with noise and delay, with applications to control: Renewal theory approach. Phys. Rev. E, 77 (2008), 031113. 
  60. T. Prager, H.P. Lerch, L. Schimansky-Geier E. Schöll. Increase of coherence in excitable systems by delayed feedback. J. Phys. A , 40 (2007), 11045–11055. 
  61. R. Ratnam M.E. Nelson. Nonrenewal statistics of electrosensory afferent spike trains: implications for the detection of weak sensory signals. J. Neurosci., 20 (2000), 6672–6683. 
  62. F. Rieke, D. Warland, R.R. de Ruyter van Steveninck, W. Bialek. Spikes: Exploring the Neural Code. MIT press, Cambridge, MA, 1996.  
  63. H. Risken. The Fokker-Planck Equation. Springer, Berlin, 1996.  
  64. J.C. Roddey, B. Girish J.P. Miller. Assessing the performance of neural encoding models in the presence of noise. J. Comput. Neurosci., 8 (2000), 95–112. 
  65. S. Sadeghi, M.J. Chacron, M.C. Taylor K.E. Cullen. Neural variability, detection thresholds, and information transmission in the vestibular system. J. Neurosci., 27 (2007), 771–781. 
  66. A.M. Sillito, H.E. Jones, G.L. Gerstein D.C. West. Feature-linked synchronization of thalamic relay cell firing induced by feedback from the visual cortex. Nature, 369 (1994), 479–482. 
  67. R. Shannon. The mathematical theory of communication. Bell. Syst. Tech. J., 27 (1948), 379–423. 
  68. S.M. Sherman. Tonic and burst firing: dual modes of thalamocortical relay. TINS, 24 (2001), 122–126. 
  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.  
  70. J. Shin. Adaptation in spiking neurons based on the noise shaping neural coding hypothesis. Neural Networks, 14 (2001), 907–919. 
  71. N.G. Stocks. Suprathreshold stochastic resonance in multilevel threshold systems. Phys. Rev. Lett., 84 (2000), 2310–2313. 
  72. M. Stopfer, S. Bhagavan, B.H. Smith G. Laurent. Impaired odour discrimination on desynchronization of odour-encoding neural assemblies. Nature, 390 (1997), 70–74. 
  73. A.M. Yacomotti, M.C. Eguia, J. Aliaga, O.E. Martinez G.B. Mindlin. Interspike Time Distribution in Noise Driven Excitable Systems. Phys. Rev. Lett., 83 (1999), 292–295. 
  74. M.K.S. Yeung S.H. Strogatz. Time Delay in the Kuramoto Model of Coupled Oscillators. Phys. Rev. Lett., 82 (1999), 648–651. 
  75. K. Wiesenfeld I. Satija. Noise tolerance of frequency-locked dynamics. Phys. Rev. B, 36 (1987), 2483–2492. 

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

                
Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.