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Noise Shaping in Neural Populations with Global Delayed Feedback

Published online by Cambridge University Press:  10 March 2010

O. Ávila Åkerberg  and
M. J. Chacron
O. Ávila Åkerberg
Affiliation:
Department of Physics, McGill University, Montreal, H3G 1Y6, Canada
M. J. Chacron*
Affiliation:
Department of Physics, McGill University, Montreal, H3G 1Y6, Canada Department of Physiology, McGill University, Montreal , H3G 1Y6, Canada
*
* Corresponding author. E-mail: maurice.chacron@mcgill.ca
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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.

Keywords

information theoryneural networksnonrenewaldelay
Type
Research Article
Information
Mathematical Modelling of Natural Phenomena , Volume 5 , Issue 2: Mathematics and neuroscience , 2010 , pp. 100 - 124
Copyright
© EDP Sciences, 2010

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References

Ávila Åkerberg, O. Chacron, M.J.. Noise shaping in neural populations . Phys. Rev. E, 79 (2009), 011904.CrossRefGoogle ScholarPubMed
Bahar, S., Kantelhardt, J.W., Neiman, A., Rego, H.H.A., Russell, D.F., Wilkens, L., Bunde, A. and Moss, F.. Long range temporal anti-correlations in paddlefish electroreceptors . Europhys. Lett., 56 (2001), 454–460.CrossRefGoogle Scholar
Borst, A. Theunissen, F.. Information theory and neural coding . Nat. Neurosci. 2 (1999), 947–957.CrossRefGoogle ScholarPubMed
V. Braitenberg, A. Schüz. Anatomy of the Cortex. Springer, Berlin, 1991.
Bulsara, A., Hänggi, P., Marchesoni, F., Moss, F. Shlesinger, M.. Special Issue for Proceedings of The Nato Advanced Research WorkshopStochastic Resonance in Physics and Biology . J. Stat. Phys., 70 (1993), 1–2.CrossRefGoogle Scholar
R.S. Cajal. Histologie du système nerveux de l’Homme et des vertébrés. Paris, Maloine, 1909.
Chacron, M.J., Longtin, A., St-Hilaire, M. Maler, L.. Suprathreshold stochastic firing dynamics with memory in P-type electroreceptors . Phys. Rev. Lett., 85 (2000), 1576–1579.CrossRefGoogle ScholarPubMed
Chacron, M.J., Maler, L. Bastian, J.. Electroreceptor neuron dynamics shape information transmission . Nat. Neurosci., 8 (2005), 673–678.CrossRefGoogle ScholarPubMed
Chacron, M.J., Longtin, A. Maler, L.. Negative interspike interval correlations increase the neuronal capacity for encoding time-dependent stimuli . J. Neurosci., 21 (2001), 5328–5343.CrossRefGoogle ScholarPubMed
Chacron, M.J., Lindner, B. Longtin, A.. Noise shaping by interval correlations increases information transfer . Phys. Rev. Lett., 92 (2004), 080601.CrossRefGoogle ScholarPubMed
Chacron, M.J., Lindner, B., Longtin, A. ISI Correlations and Information Transfer . Fluct. Noise Lett., 4 (2004) L195–L205. CrossRefGoogle Scholar
Chacron, M.J., Longtin, A. Maler, L.. Delayed excitatory and inhibitory feedback shape neural information transmission . Phys. Rev. E, 72 (2005), 051917.CrossRefGoogle ScholarPubMed
Chacron, M.J., Longtin, A. Maler, L.. The effects of spontaneous activity, background noise, and the stimulus ensemble on information transfer in neurons . Network, 14 (2003), 803–824.CrossRefGoogle ScholarPubMed
Chacron, M.J., Doiron, B., Maler, L., Longtin, A. Bastian, J.. Non-classical receptive field mediates switch in a sensory neuron’s frequency tuning . Nature, 423 (2003), 77–81.CrossRefGoogle Scholar
Chacron, M.J., Maler, L. Bastian, J.. Feedback and feedforward control of frequency tuning to naturalistic stimuli . J. Neurosci., 25 (2005), 5521–5532.CrossRefGoogle ScholarPubMed
Chacron, M.J.. Nonlinear information processing in a model sensory system . J. Neurophysiol., 95 (2006), 2933–2946.CrossRefGoogle Scholar
Chacron, M.J., Lindner, B. Longtin, A.. Threshold fatigue and information transfer . J. Comput. Neurosci., 23 (2007), 301–311.CrossRefGoogle ScholarPubMed
Chacron, M.J. Bastian, J.. Population coding by electrosensory neurons . J. Neurophysiol., 99 (2008), 1825–1835.CrossRefGoogle ScholarPubMed
T. Cover, J. Thomas. Elements of Information Theory, Wiley, New-York, 1991.
Doiron, B., Chacron, M.J., Maler, L., Longtin, A. Bastian, J.. Inhibitory feedback required for network oscillatory responses to communication but not prey stimuli . Nature, 421 (2003), 539–543.CrossRefGoogle Scholar
Doiron, B., Lindner, B., Longtin, A., Maler, L. Bastian, J.. Oscillatory activity in electrosensory neurons increases with the spatial correlation of the stochastic input stimulus . Phys. Rev. Lett., 93 (2004), 048101.CrossRefGoogle ScholarPubMed
Ellis, L.D., Krahe, R., Bourque, C.M., Dunn, R.J. Chacron, M.J.. Muscarinic receptors control frequency tuning through the downregulation of an A-type potassium current . J. Neurophysiol., 98 (2007), 1526–1537.CrossRefGoogle ScholarPubMed
Engel, T.A., Helbig, B., Russell, D.F., Schimansky-Geier, L. Neiman, A.B.. Coherent stochastic oscillations enhance signal detection in spiking neurons . Phys. Rev. E, 80 (2009), 021919.CrossRefGoogle ScholarPubMed
Farkhooi, F., Strube-Bloss, M.F. Nawrot, M.P.. Serial correlation in neural spike trains: Experimental evidence, stochastic modeling, and single neuron variability . Phys. Rev. E, 79 (2009), 021905.CrossRefGoogle ScholarPubMed
Gammaitoni, L., Hänggi, P., Jung, P., Marchesoni, F.. Stochastic resonance . Rev. Mod. Phys., 70 (1998), 223–287.CrossRefGoogle Scholar
L. Glass, M.C. Mackey. From Clocks to Chaos. Princeton Univ. Press, Princeton, 1988.
Goense, J.B.M. Ratnam, R.. 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.CrossRefGoogle ScholarPubMed
Gray, C. Singer, W.. Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex . Proc. Natl Acad. Sci. USA, 86 (1989), 1698–1702.CrossRefGoogle ScholarPubMed
Janson, N.B. , Balanov, A.G., Schöll, E.. Delayed Feedback as a Means of Control of Noise-Induced Motion . Phys. Rev. Lett., 93 (2004), 010601. CrossRefGoogle ScholarPubMed
A. V. Holden. Models of the Stochastic Activity of Neurons. Springer, Berlin, 1976.
Hollander, H.. 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
Hutcheon, B. Yarom, Y. Resonance, oscillation and the intrinsic frequency preferences of neurons . Trends Neurosci., 23 (2000), 216–222.CrossRefGoogle ScholarPubMed
Izhikevich, E.M. Neural Excitability, Spiking, and Bursting . Int. J. Bif. Chaos, 10 (2000), 1171–1266.CrossRefGoogle Scholar
Kashiwadani, H., Sasaki, Y.F., Uchida, N. Mori, K.. Synchronized oscillatory discharges of mitral/tufted cells with different molecular receptive ranges in the rabbit olfactory bulb . J. Neurophysiol., 82 (1999), 1786–1792.CrossRefGoogle ScholarPubMed
Kisvárday, Z.F., Martin, K.A., Freund, T.F., Maglóczky, Z., Whitteridge, D. Somogyi, P.. Synaptic targets of HRP-filled layer III pyramidal cells in the cat striate cortex . Exp. Brain. Res., 64 (1986), 541–552.CrossRefGoogle ScholarPubMed
Klemm, W.R. Sherry, C.J.. Entropy as an index of the informational state of neurons . Int. J. Neurosci., 15 (1981), 171–178.Google Scholar
Korn, H. Faure, P.. 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
Krahe, R., Bastian, J. Chacron, M.J.. Temporal processing across multiple topographic maps in the electrosensory system . J. Neurophysiol., 100 (2008), 852–867.CrossRefGoogle ScholarPubMed
Lebedev, M.A. Nelson, R.J.. 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.CrossRefGoogle ScholarPubMed
Lindner, B., Chacron, M.J. Longtin, A.. 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.CrossRefGoogle ScholarPubMed
Lindner, B., Doiron, B. Longtin, A.. Theory of oscillatory firing induced by spatially correlated noise and delayed inhibitory feedback . Phys. Rev. E, 72 (2005), 061919.CrossRefGoogle ScholarPubMed
Lindner, B., Gangloff, D., Longtin, A. Lewis, J.E.. Broadband Coding with Dynamic Synapses . J. Neurosci., 29 (2004), 2076–2087.CrossRefGoogle Scholar
Lowen, S.B. Teich, M.C.. 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.CrossRefGoogle Scholar
Lüdtke, N. Nelson, M.E.. Short-term synaptic plasticity can enhance weak signal detectability in nonrenewal spike trains . Neural Comput., 18 (2006), 2879–2916.CrossRefGoogle ScholarPubMed
MacLeod, K. Laurent, G.. Distinct mechanisms for synchronization and temporal patterning of odor-encoding neural assemblies . Science, 274 (1996), 976–979.CrossRefGoogle ScholarPubMed
Mainen, Z.F. Sejnowski, T. J.. Reliability of spike timing in neocortical neurons . Science, 268 (1995), 1503–1506.CrossRefGoogle ScholarPubMed
Maler, L. Mugnaini, E.. 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
Maler, L. Mugnaini, E.. Correlating gamma-aminobutyric circuits and sensory function in the electrosensory lateral line lobe of a gymnotiform fish . J. Comp. Neurol., 345 (1994), 224–252.CrossRefGoogle ScholarPubMed
Mar, D.J., Chow, C.C., Gerstner, W., Adams, R.W. Collins, J.J.. Noise shaping in populations of coupled model neurons . Proc. Natl. Acad. Sci., 96 (1999), 10450–10455.CrossRefGoogle ScholarPubMed
Marsat, G. Pollack, G.S.. Effect of the temporal pattern of contralateral inhibition on sound localization cues . J. Neurosci., 25 (2005), 6137–6144.CrossRefGoogle ScholarPubMed
Mattia, M. Del Giudice, P.. Finite-size dynamics of inhibitory and excitatory interacting spiking neurons . Phys. Rev. E, 70 (2004), 052903.CrossRefGoogle ScholarPubMed
Middleton, J.W., Chacron, M.J., Lindner, B. Longtin, A.. Firing statistics of a neuron model driven by long-range correlated noise . Phys. Rev. E, 68 (2003), 021920.CrossRefGoogle ScholarPubMed
McGuire, B.A., Hornung, J.P., Gilbert, C.D. Wiesel, T.N.. Patterns of synaptic input to layer 4 of cat striate cortex . J. Neurosci., 4 (1984), 3021–3033.CrossRefGoogle ScholarPubMed
Moss, F., Ward, L. Sannita, W.. Stochastic resonance and sensory information processing: a tutorial and review of application . Clin. Neurophysiol., 115 (2004), 267–281.CrossRefGoogle Scholar
Nelson, M.E. MacIver, M.A.. Prey capture in the weakly electric fish Apteronotus albifrons: sensory acquisition strategies and electrosensory consequences . J. Exp. Biol., 202 (1999), 1195–1203.Google ScholarPubMed
S.R. Norsworthy, R. Schreier, G. C. Temes. Delta-Sigma Data Converters. IEEE Press, Piscataway, 1997.
Ostapoff, E.M., Morest, D.K. Potashner, S.J.. 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.Google Scholar
Passaglia, C.L. Troy, J.B.. Information transmission rates of cat retinal ganglion cells . J. Neurophysiol., 91 (2004), 1217–1229.CrossRefGoogle ScholarPubMed
Pototsky, A. Janson, N.. Excitable systems with noise and delay, with applications to control: Renewal theory approach . Phys. Rev. E, 77 (2008), 031113.CrossRefGoogle ScholarPubMed
Prager, T., Lerch, H.P., Schimansky-Geier, L. Schöll, E.. Increase of coherence in excitable systems by delayed feedback . J. Phys. A , 40 (2007), 11045–11055.CrossRefGoogle Scholar
Ratnam, R. Nelson, M.E.. Nonrenewal statistics of electrosensory afferent spike trains: implications for the detection of weak sensory signals . J. Neurosci., 20 (2000), 6672–6683.CrossRefGoogle ScholarPubMed
F. Rieke, D. Warland, R.R. de Ruyter van Steveninck, W. Bialek. Spikes: Exploring the Neural Code. MIT press, Cambridge, MA, 1996.
H. Risken. The Fokker-Planck Equation. Springer, Berlin, 1996.
Roddey, J.C., Girish, B. Miller, J.P.. Assessing the performance of neural encoding models in the presence of noise . J. Comput. Neurosci., 8 (2000), 95–112.CrossRefGoogle ScholarPubMed
Sadeghi, S., Chacron, M.J., Taylor, M.C. Cullen, K.E.. Neural variability, detection thresholds, and information transmission in the vestibular system . J. Neurosci., 27 (2007), 771–781.CrossRefGoogle ScholarPubMed
Sillito, A.M., Jones, H.E., Gerstein, G.L. West, D.C.. Feature-linked synchronization of thalamic relay cell firing induced by feedback from the visual cortex . Nature, 369 (1994), 479–482.CrossRefGoogle ScholarPubMed
Shannon, R.. The mathematical theory of communication . Bell. Syst. Tech. J., 27 (1948), 379–423.CrossRefGoogle Scholar
Sherman, S.M.. Tonic and burst firing: dual modes of thalamocortical relay . TINS, 24 (2001), 122–126.Google ScholarPubMed
Sherman, S.M., Guillery, R.W.. 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. CrossRefGoogle ScholarPubMed
Shin, J.. Adaptation in spiking neurons based on the noise shaping neural coding hypothesis . Neural Networks, 14 (2001), 907–919.CrossRefGoogle ScholarPubMed
Stocks, N.G.. Suprathreshold stochastic resonance in multilevel threshold systems . Phys. Rev. Lett., 84 (2000), 2310–2313.CrossRefGoogle ScholarPubMed
Stopfer, M., Bhagavan, S., Smith, B.H. Laurent, G.. Impaired odour discrimination on desynchronization of odour-encoding neural assemblies . Nature, 390 (1997), 70–74.CrossRefGoogle ScholarPubMed
Yacomotti, A.M., Eguia, M.C., Aliaga, J., Martinez, O.E. Mindlin, G.B.. Interspike Time Distribution in Noise Driven Excitable Systems . Phys. Rev. Lett., 83 (1999), 292–295.CrossRefGoogle Scholar
Yeung, M.K.S. Strogatz, S.H.. Time Delay in the Kuramoto Model of Coupled Oscillators . Phys. Rev. Lett., 82 (1999), 648–651.CrossRefGoogle Scholar
Wiesenfeld, K. Satija, I.. Noise tolerance of frequency-locked dynamics . Phys. Rev. B, 36 (1987), 2483–2492.CrossRefGoogle ScholarPubMed