Urdapilleta, Eugenio (2012) Efectos de las corrientes de adaptación en el procesamiento neuronal. / Effects of adaptation currents on neuronal processing. Tesis Doctoral en Física, Universidad Nacional de Cuyo, Instituto Balseiro.
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Resumen en español
Diferentes procesos de adaptación influencian la representación de las señales incidentes a una neurona. Entre ellos, las corrientes de adaptación conforman un importante mecanismo de ajuste de las respuestas neuronales. El efecto más notorio de estas corrientes es su influencia central en la definición del fenómeno conocido como adaptación de la frecuencia de disparo, el cual involucra un re-escaleo de la ganancia de la neurona. Sin embargo, la presencia de las mismas impactan sobre otros aspectos de la representación neuronal: la modificación del procesamiento temporal de las señales, la redefinición de la respuesta lineal en el dominio frecuencial, la generación de correlaciones entre disparos en condiciones estacionarias y la reducción en la incerteza de la representación de señales estáticas. En este trabajo estudiamos estos fenómenos mediante descripciones adecuadas de la respuesta neuronal. En particular, en base a la utilización de modelos probabilistas, analizamos las características del procesamiento temporal y espectral en presencia de mecanismos de adaptación en neuronas aisladas, mientras que con la definición de apropiados modelos dinámicos estocásticos, que incluyen una corriente genérica de adaptación evocada por disparos, estudiamos los aspectos más fundamentales del tren de disparos resultante: la generación de correlaciones entre eventos y la reducción de la variabilidad asociada al proceso de conteo que define a un código de tasas.
Resumen en inglés
Different adaptation processes influence the representation of the incoming signals to a neuron. Among them, the adaptation currents are an important mechanism to adjust the neural responses. The most prominent effect of these currents is their fundamental influence on the phenomenon known as spike-frequency adaptation, which involves a rescaling of the neuronal gain. However, their presence also impact on other aspects of the neuronal representation: a modification of the signal processing temporal characteristics, a reshaping of the linear response in the frequency domain, the introduction of correlations between spikes in stationary conditions, and a reduction in the uncertainty of the static signals representation. In this work, based on appropriate descriptions of the neuronal response, we study these effects in detail. In particular, in a probabilistic framework of neural responses, we analyze the characteristics of the temporal and spectral processing in the presence of adaptation mechanisms in single neurons, whereas based on the definition of adequate dynamical stochastic models, which include a general spike-based adaptation current, we study the most fundamental features of the resulting non renewal spike train: the origin of correlations between events and the reduction in the variability associated to the counting process that defines a rate code.
| Tipo de objeto: | Tesis (Tesis Doctoral en Física) |
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| Palabras Clave: | Neurons; Neuronas; Adaptation; Adaptación; Spike train;Tren de disparos; First-passage-time; Tiempo de primer pasaje; Variability; Variabilidad |
| Referencias: | [1] Rieke, F., Warland, D., de Ruyter van Steveninck, R., Bialek, W. Spikes: Exploring the neural code. The MIT Press, 1997. [2] Dayan, P., Abbott, L. F. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. The MIT Press, 2001. [3] Harris, K. D., Thiele, A. Cortical state and attention. Nature Rev. Neurosci., 12, 509–523, 2011. [4] Ruderman, D. L. The statistics of natural images. Netw. Comput. Neural Syst., 5, 517–548, 1994. [5] Dong, D. W., Atick, J. J. Statistics of natural time-varying images. Netw. Comput. Neural Syst., 6, 345–358, 1995. [6] Simoncelli, E. P., Olshausen, B. A. Natural image statistics and neural representation. Annu. Rev. Neurosci., 24, 1193–1216, 2001. [7] Koenderink, J. J. Optic flow. Vision Res., 26(1), 161–180, 1986. [8] Kandel, E. R., Schwartz, J. H., Jessell, T. M. Principles of Neural Science. McGraw-Hill, 2000. [9] Wark, B., Lundstrom, B., Fairhall, A. Sensory adaptation. Curr. Op. Neurobiol., 17, 423–429, 2007. [10] Feldman, D. E. Synaptic mechanisms for plasticity in neocortex. Annu. Rev. Neurosci., 32, 33–55, 2009. [11] Borst, A., Flanagin, V. L., Sompolinsky, H. Adaptation without parameter change: Dynamic gain control in motion detection. Proc. Natl. Acad. Sci. USA, 102(17), 6172–6176, 2005. [12] Benda, J. Single Neuron Dynamics - Models Linking Theory and Experiment. PhD Thesis, Humboldt-Universit¨at zu Berlin, 2002. [13] Madison, D. V., Nicoll, R. A. Control of the repetitive discharge of rat ca1 pyramidal neurones In Vitro. J. Physiol., 354, 319–331, 1984. [14] Sah, P. Ca2+-activated k+ currents in neurones: Types, physiological roles and modulation. Trends Neurosci., 19(4), 150–154, 1996. [15] Brown, D. A., Adams, P. R. Muscarinic supression of a novel voltage-sensitive k+ current in a vertebrate neuron. Nature, 183, 673–676, 1980. [16] Adams, P. R., Brown, D. A., Constanti, A. M-currents and other potassium currents in bullfrog sympathetic neurones. J. Physiol., 330, 537–572, 1982. [17] Benda, J., Herz, A. V. M. A universal model for spike-frequency adaptation. Neural Comput., 15, 2523–2564, 2003. [18] Prescott, S. A., Sejnowski, T. J. Spike-rate coding and spike-time coding are affected oppositely by different adaptation mechanisms. J. Neurosci., 28(50), 13649–13661, 2008. [19] Higgs, M. H., Slee, S. J., Spain, W. J. Diversity of gain modulation by noise in neocortical neurons: Regulation by the slow afterhyperpolarization conductance. J. Neurosci., 26(34), 8787–8799, 2006. [20] Wang, X. J. Calcium coding and adaptive temporal computation in cortical pyramidal neurons. J. Neurophysiol., 79, 1549–1566, 1998. [21] Liu, Y. H., Wang, X. J. Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron. J. Comput. Neurosci., 10, 25–45, 2001. [22] Chacron, M. J., Lindner, B., Longtin, A. Noise shaping by interval correlations increases information transfer. Phys. Rev. Lett., 92(8), 080601/1–4, 2004. [23] 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, 021911/1–21, 2005. [24] ´Avila °Akerberg, O., Chacron, M. J. Noise shaping in neural populations. Phys. Rev. E, 79, 011914/1–8, 2009. [25] Peron, S., Gabbiani, F. Spike frequency adaptation mediates looming stimulus selectivity in a collision-detecting neuron. Nature Neurosci., 12, 318–326, 2009. [26] Peron, S. P., Gabbiani, F. Role of spike-frequency adaptation in shaping neuronal response to dynamic stimuli. Biol. Cybern., 100, 505–520, 2009. [27] White, J. A., Rubinstein, J. T., Kay, A. R. Channel noise in neurons. Trends Neurosci., 23(3), 131–137, 2000. [28] Allen, C., Stevens, C. F. An evaluation of causes for unreliability of synaptic transmission. Proc. Natl. Acad. Sci. USA, 91, 10380–10383, 1994. [29] Rieke, F., Baylor, D. A. Single-photon detection by rod cells of the retina. Rev. Mod. Phys., 70(3), 1027–1036, 1998. [30] Wehr, M., Zador, A. M. Balanced inhibition underlies tuning and sharpens spike timing in autitory cortex. Nature, 426, 442–446, 2003. [31] Engel, A. K., Fries, P., Singer, W. Dynamics predictions: Oscillations and synchrony in top-down processing. Nat. Rev. Neurosci., 2, 704–716, 2001. [32] Tsodyks, M., Kenet, T., Grinvald, A., Arieli, A. Linking spontaneous activity of single cortical neurons and the underlying functional architecture. Science, 286, 1943–1946, 1999. [33] Schneidman, E., Freedman, B., Segev, I. Ion channel stochasticity may be critical in determining the reliability and precision of spike timing. Neural Comput., 10, 1679–1703, 1998. [34] Goychuk, I., H¨anggi, P. Ion channel gating: A first-passage time analysis of the kramers type. Proc. Natl. Acad. Sci. USA, 99(6), 3552–3556, 2002. [35] Dobrunz, L. E., Stevens, C. F. Heterogeneity of release probability, facilitation, and depletion at central synapses. Neuron, 18, 995–1008, 1997. [36] David, S. V., Vinje, W. E., Gallant, J. L. Natural stimulus statistics alter the receptive field structure of v1 neurons. J. Neurosci., 24(31), 6991–7006, 2004. [37] Stein, R. B., Gossen, E. R., Jones, K. E. Neuronal variability: Noise or part of the signal? Nat. Rev. Neurosci., 6, 389–397, 2005. [38] Faisal, A. A., Selen, L. P. J., Wolpert, D. M. Noise in the nervous system. Nat. Rev. Neurosci., 9, 292–303, 2008. [39] Adrian, E. D. The impulses produced by sensory nerve endings. part 1. J. Physiol., 61(1), 49–72, 1926. [40] Adrian, E. D., Zotterman, Y. The impulses produced by sensory nerve endings. part 2. the response of a single end-organ. J. Physiol., 61(2), 151–171, 1926. [41] Adrian, E. D., Zotterman, Y. The impulses produced by sensory nerve endings. part 3. impulses set up by touch and pressure. J. Physiol., 61(4), 465–483, 1926. [42] Adrian, E. D. The impulses produced by sensory nerve endings. part 4. impulses from pain receptors. J. Physiol., 62(1), 33–51, 1926. [43] Hubel, D. H., Wiesel, T. Receptive fields of single neurons in the cat’s striate cortex. J. Physiol., 148, 574–591, 1959. [44] Hubel, D. H., Wiesel, T. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol., 160, 106–154, 1962. [45] Hubel, D. H., Wiesel, T. Receptive fields and functional architecture of monkey striate cortex. J. Physiol., 195, 215–243, 1968. [46] Theunissen, F. E., Sen, K., Doupe, A. J. Spectral-temporal receptive fields of nonlinear auditory neurons obtained using natural sounds. J. Neurosci., 20(6), 2315–2331, 2000. [47] Nemenman, I., Lewen, G. D., Bialek, W., de Ruyter van Steveninck, R. Neural coding of natural stimuli: Information at sub-millisecond resolution. PLoS Comput. Biol., 4(3), e1000025/1–12, 2008. [48] Montemurro, M. A., Rasch, M. J., Murayama, Y., Logothetis, N. K., Panzeri, S. Phase-of-firing coding of natural visual stimuli in primary visual cortex. Curr. Biol., 18, 375–380, 2008. [49] Faisal, A. A., Laughlin, S. B. Stochastic simulations on the reliability of action potential propagation in thin axons. PLoS Comput. Biol., 3(5), e79/0783–0795, 2007. [50] Albrecht, D. G., Hamilton, D. B. Striate cortex of monkey and cat: Contrast response function. J. Neurophysiol., 48(1), 217–237, 1982. [51] Albright, T. D. Direction and orientation selectivity of neurons in visual area mt of the macaque. J. Neurophysiol., 52(6), 1106–1130, 1984. [52] Miller, J. P., Jacobs, G. A., Theunissen, F. E. Representation of sensory information in the cricket cercal sensory system. 1. response properties of the primary interneurons. J. Neurophysiol., 66(5), 1680–1689, 1991. [53] Cao, P., Gu, Y., Wang, S. R. Visual neurons in the pigeon brain encode the acceleration of stimulus motion. J. Neurosci., 24(35), 7690–7698, 2004. [54] Salinas, E. How behavioral constraints may determine optimal sensory representations. PLoS Biology, 4(12), e387/2383–2392, 2006. [55] Brenner, N., Bialek, W., de Ruyter van Steveninck, R. Adaptive rescaling maximizes information transmission. Neuron, 26, 695–702, 2000. [56] Brenner, N., Agam, O., Bialek, W., de Ruyter van Steveninck, R. Statistical properties of spike trains: Universal and stimulus-dependent aspects. Phys. Rev. E, 66, 031907/1–14, 2002. [57] Schaette, R., Gollisch, T., Herz, A. V. M. Spike-train variability of auditory neurons in vivo: Dynamics responses follow predictions from constant stimuli. J. Neurophysiol., 93, 3270–3281, 2005. [58] Bryant, H. L., Segundo, J. P. Spike initiation by transmembrane current: A white-noise analysis. J. Physiol., 260, 279–314, 1976. [59] Nagel, K. I., Doupe, A. J. Organizing principles of spectro-temporal encoding in the avian primary auditory area field l. Neuron, 58, 938–955, 2008. [60] Gabbiani, F., Koch, C. Principles of spike train analysis. En: C. Koch, I. Segev (eds.) Methods in Neuronal Modeling: From Ions to Networks, p´ags. 313–360. The MIT Press, 1999. [61] Chichilnisky, E. J. A simple white noise analysis of neuronal light responses. Network: Comput. Neural Syst., 12, 199–213, 2001. [62] Schwartz, O., Pillow, J. W., Rust, N. C., Simoncelli, E. P. Spike-triggered neural characterization. J. Vision, 6, 484–507, 2006. [63] Adelson, E. H., Bergen, J. R. Spatiotemporal energy models for the perception of motion. J. Opt. Soc. Am. A, 2(2), 284–299, 1985. [64] Segev, R., Puchalla, J., Berry II, M. J. Functional organization of ganglion cells in the salamander retina. J. Neurophysiol., 95, 2277–2292, 2006. [65] Nagel, K. I., Doupe, A. J. Temporal processing and adaptation in the songbird auditory forebrain. Neuron, 51, 845–859, 2006. [66] Bair, W., Movshon, J. A. Adaptive temporal integration of motion in directionselective neurons in macaque visual cortex. J. Neurosci., 24(33), 7305–7323, 2004. [67] Meister, M., Berry II, M. J. The neural code of the retina. Neuron, 22, 435–450, 1999. [68] Carandini, M., Demb, J. B., Mante, V., Tolhurst, D. J., Dan, Y., Olshausen, B. A., et al. Do we know what the early visual system does? J. Neurosci., 25(46), 10577–10597, 2005. [69] Rust, N. C., Schwartz, O., Movshon, J. A., Simoncelli, E. P. Spatiotemporal elements of macaque v1 receptive fields. Neuron, 46, 945–956, 2005. [70] Gollisch, T., Meister, M. Rapid neural coding in the retina with relative spike latencies. Science, 319, 1108–1111, 2008. [71] Gollisch, T., Meister, M. Modeling convergent on and off pathways in the early visual system. Biol. Cybern., 99, 263–278, 2008. [72] Geffen, M., Broome, B. M., Laurent, G., Meister, M. Neural encoding of rapidly fluctuating odors. Neuron, 61, 570–586, 2009. [73] Fontanini, A., Bower, J. M. Slow-waves in the olfactory system: An olfactory perpective on cortical rhythms. Trends Neurosci., 29(8), 429–437, 2006. [74] Saper, C. B. Brain stem modulation of sensation, movement, and consciousness. En: E. R. Kandel, J. H. Schwartz, T. M. Jessell (eds.) Principles of Neural Science, p´ags. 889–909. McGraw-Hill, 2000. [75] Doya, K. Metalearning and neuromodulation. Neural Netw., 15, 495–506, 2002. [76] Ermentrout, B. Linearization of f-i curves by adaptation. Neural Comput., 10, 1721–1729, 1998. [77] Georgopoulos, A. P., Kalaska, J. F., Caminiti, R., Massey, J. T. On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. J. Neurosci., 2(11), 1527–1537, 1982. [78] Aviel, Y., Gerstner, W. From spiking neurons to rate models: A cascade model as an approximation to spiking neuron models with refractoriness. Phys. Rev. E, 73, 051908/1–10, 2006. [79] Atick, J. J. Could information theory provide an ecological theory of sensory processing? Network, 3, 213–251, 1992. [80] Schwartz, O., Simoncelli, E. P. Natural signal statistics and sensory gain control. Nature Neurosci., 4(8), 819–825, 2001. [81] Mante, V., Frazor, R. A., Bonin, V., Geisler, W. S., Carandini, M. Independence of luminance and contrast in natural scenes and in the early visual system. Nature Neurosci., 8, 1690–1697, 2005. [82] Ulanovsky, N., Las, L., Nelken, I. Processing of low-probability sounds by cortical neurons. Nature Neurosci., 6(4), 391–398, 2003. [83] Petersen, C. C. H. Short-term dynamics of synaptic transmission within the excitatory neuronal network of rat layer 4 barrel cortex. J. Neurophysiol., 87, 2904–2914, 2002. [84] Dahmen, J. C., Hartley, D. E. H., King, A. J. Stimulus-timing-dependent plasticity of cortical frequency representation. J. Neurosci., 28(50), 13629–13639, 2008. [85] Carandini, M., Ferster, D. A tonic hyperpolarization underlying contrast adaptation in cat visual cortex. Science, 276, 949–952, 1997. [86] Higley, M. J., Contreras, D. Balanced excitation and inhibition determine spike timing during frequency adaptation. J. Neurosci., 26(2), 448–457, 2006. [87] Sanchez-Vives, M. V., Nowak, L. G., McCormick, D. A. Cellular mechanisms of long-lasting adaptation in visual cortical neurons in vitro. J. Neurosci., 20(11), 4286–4299, 2000. [88] Gabbiani, F., Krapp, H. G. Spike-frequency adaptation and intrinsic properties of an identified, looming-sensitive neuron. J. Neurophysiol., 96, 2951–2962, 2006. [89] Berman, N. J., Maler, L. Neural architecture of the electrosensory lateral line lobe: Adaptations for coincidence detection, a sensory searchlight and frequencydependent adaptive filtering. J. Exp. Biol., 202, 1243–1253, 1999. [90] 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, 539–543, 2003. [91] Lefebvre, J., Longtin, A., LeBlanc, V. G. Dynamics of driven recurrent networks of on and off cells. Phys. Rev. E, 80, 041912/1–9, 2009. [92] Field, G. D., Chichilnisky, E. J. Information processing in the primate retina: Circuitry and coding. Annu. Rev. Neurosci., 30, 1–30, 2007. [93] Field, G. D., Sher, A., Gauthier, J. L., Greschner, M., Shlens, J., Litke, A. M., et al. Spatial properties and functional organization of small bistratified ganglion cells in primate retina. J. Neurosci., 27(48), 13261–13272, 2007. [94] Gauthier, J. L., Field, G. D., Sher, A., Shlens, J., Greschner, M., Litke, A. M., et al. Uniform signal redundancy of parasol and midget ganglion cells in primate retina. J. Neurosci., 29(14), 4675–4680, 2009. [95] Gauthier, J. L., Field, G. D., Sher, A., Greschner, M., Shlens, J., Litke, A. M., et al. Receptive fields in primate retina are coordinated to sample visual space more uniformly. PLoS Biology, 7(4), e1000063/0001–0009, 2009. [96] Pillow, J. W., Shlens, J., Paninski, L., Sher, A., Litke, A. M., Chichilnisky, E. J., et al. Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature, 454, 995–999, 2008. [97] Greschner, M., Shlens, J., Bakolitsa, C., Field, G. D., Gauthier, J. L., Jepson, L. H., et al. Correlated firing among major ganglion cell types in primate retina. J. Physiol., 589(1), 75–86, 2011. [98] Enroth-Cugell, C., Shapley, R. M. Adaptation and dynamics of cat retinal ganglion cells. J. Physiol., 233, 271–309, 1973. [99] Carandini, M., Heeger, D. J. Summation and division by neurons in primate visual cortex. Science, 264, 1333–1336, 1994. [100] Victor, J. D. The dynamics of the cat retinal x cell centre. J. Physiol., 386, 219–246, 1987. [101] van Hateren, J. H., R¨uttiger, L., Sun, H., Lee, B. B. Processing of natural temporal stimuli by macaque retinal ganglion cells. J. Neurosci., 22(22), 9945– 9960, 2002. [102] Press, W. H., Teukolsky, S. A., Vetterling, W. T., Flannery, B. P. Numerical Recipes: The Art of Scientific Computing. Cambridge University Press, 2007. [103] Hodgkin, A. L., Huxley, A. F., Katz, B. Measurement of current-voltage relations in the membrane of the giant axon of Loligo. J. Physiol., 116, 424–448, 1952. [104] Hodgkin, A. L., Huxley, A. F. Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo. J. Physiol., 116, 449–472, 1952. [105] Hodgkin, A. L., Huxley, A. F. The components of membrane conductance in the giant axon of Loligo. J. Physiol., 116, 473–496, 1952. [106] Hodgkin, A. L., Huxley, A. F. The dual effect of membrane potential on sodium conductance in the giant axon of Loligo. J. Physiol., 116, 497–506, 1952. [107] Hodgkin, A. L., Huxley, A. F. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol., 117, 500– 544, 1952. [108] Izhikevich, E. M. Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. The MIT Press, 2007. [109] Hille, B. Ion Channels of Excitable Membranes. Sinauer Associates, 1992. [110] Gerstner, W., Kistler, W. Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, 2002. [111] Kistler, W., Gerstner, W., van Hemmen, J. L. Reduction of the hodgkin-huxley equations to a single-variable threshold model. Neural Comput., 9, 1015–1045, 1997. [112] Koch, C. Biophysics of Computation: Information Processing in Single Neurons. Oxford University Press, 1999. [113] Knight, B. W. Dynamics of encoding in a population of neurons. J. Gen. Physiol., 59, 734–766, 1972. [114] Fourcaud-Trocm´e, N., Hansel, D., van Vreeswijk, C., Brunel, N. How spike generation mechanisms determine the neuronal response to fluctuating inputs. J. Neurosci., 23(37), 11628–11640, 2003. [115] Badel, L., Lefort, S., Brette, R., Petersen, C. C. H., Gerstner, W., Richardson, M. J. E. Dynamic i-v curves are reliable predictors of naturalistic pyramidal-neuron voltage traces. J. Neurophysiol., 99, 656–666, 2008. [116] Goldwyn, J. H., Imennov, N. S., Famulare, M., Shea-Brown, E. Stochastic differential equation models for ion channel noise in hodgkin-huxley neurons. Phys. Rev. E, 83, 041908/1–16, 2011. [117] Stein, R. B. A theoretical analysis of neuronal variability. Biophys. J., 5, 173–194, 1965. [118] L´ansk´y, P., Sacerdote, L. The ornstein-uhlenbeck neuronal model with signaldependent noise. Phys. Lett. A, 285, 132–140, 2001. [119] Risken, H. The Fokker-Planck Equation: Methods of Solutions and Applications. Springer-Verlag, 1989. [120] Tuckwell, H. C. Introduction to Theoretical Neurobiology, Volume 2: Nonlinear and Stochastic Theories. Cambridge University Press, 1988. [121] Lindner, B. Moments of the first passage time under external driving. J. Stat. Phys., 117(314), 703–737, 2004. [122] Abramowitz, M., Stegun, I. A. E. Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Dover Publications, 1972. [123] Plesser, H. E., Gerstner, W. Escape rate models for noisy integrate-and-fire neurons. Neurocomputing, 32-33, 219–224, 2000. [124] Plesser, H. E., Gerstner, W. Noise in integrate-and-fire neurons: From stochastic input to escape rates. Neural Comput., 12, 367–384, 2000. [125] Lindner, B. Coherence and Stochastic Resonance in Nonlinear Dynamical Systems. PhD Thesis, Humboldt-Universit¨at zu Berlin, 2002. [126] Gerstein, G. L., Mandelbrot, B. Random walk models for the spike activity of a single neuron. Biophys. J., 4, 41–68, 1964. [127] Rodieck, R. W., Kiang, N. Y. S., Gerstein, G. L. Some quantitative methods for the study of spontaneous activity of single neurons. Biophys. J., 2, 351–368, 1962. [128] Izhikevich, E. M. Simple model of spiking neurons. IEEE Trans. Neural Netw., 14(6), 1569–1572, 2003. [129] Izhikevich, E. M. Which model to use for cortical spiking neurons? IEEE Trans. Neural Netw., 15(5), 1063–1070, 2004. [130] Rauch, A., La Camera, G., L¨uscher, H. R., Senn, W., Fusi, S. Neocortical pyramidal cells respond as integrate-and-fire neurons to In vivo-like input currents. J. Neurophysiol., 90, 1598–1612, 2003. [131] La Camera, G., Rauch, A., L¨uscher, H. R., Senn, W., Fusi, S. Minimal models of adapted neuronal response to In vivo-like currents. Neural Comput., 16(10), 2101–2124, 2004. [132] Gollisch, T., Sch¨utze, H., Benda, J., Herz, A. V. M. Energy integration describes sound-intensity coding in an insect auditory system. J. Neurosci., 22(23), 10434– 10448, 2002. [133] Gollisch, T., Herz, A. V. M. Input-driven components of spike-frequency adaptation can be unmasked In Vivo. J. Neurosci., 24(34), 7435–7444, 2004. [134] Ditlevsen, S., Lansky, P. Estimation of the input parameters in the ornsteinuhlenbeck neuronal model. Phys. Rev. E, 71, 011907/1–9, 2005. [135] Katz, P. S. Intrinsic and extrinsic neuromodulation of motor circuits. Curr. Op. Neurobiol., 5(6), 799–808, 1995. [136] Nusbaum, M. P., Beenhakker, M. P. A small-systems approach to motor pattern generation. Nature, 417, 343–350, 2002. [137] Berens, P., Keliris, G. A., Ecker, A. S., Logothetis, N. K., Tolias, A. S. Comparing the feature selectivity of the gamma-band of the local field potential and the underlying spiking activity in primate visual cortex. Front. Syst. Neurosci., 2, 1–11, 2008. [138] Marder, E. Modulating membrane properties of neurons: Role in information processing. En: T. A. Poggio, D. A. Glaser (eds.) Exploring Brain Functions: Models in Neuroscience, p´ags. 27–42. John Wiley & Sons, 1993. [139] Picchini, U., Ditlevsen, S., De Gaetano, A., Lansky, P. Parameters of the diffusion leaky integrate-and-fire neuronal model for a slowly fluctuating signal. Neural Comput., 20, 2696–2714, 2008. [140] T´oth, K., Freund, T. F., Miles, R. Disinhibition of rat hippocampal pyramidal cells by gabaergic afferents from the septum. J. Physiol., 500(2), 463–474, 1997. [141] Lundstrom, B., Fairhall, A. L. Decoding stimulus variance from a distributional neural code of interspike intervals. J. Neurosci., 26(35), 9030–9037, 2006. [142] Helmchen, F., Imoto, K., Sakmann, B. Ca2+ buffering and action potentialevoked ca2+ signaling in dendrites of pyramidal neurons. Biophys. J., 70, 1069– 1081, 1996. [143] van Kampen, N. G. Stochastic Processes in Physics and Chemistry. North- Holland, 2007. [144] H¨anggi, P., Marchesoni, F. Introduction: 100 years of brownian motion. Chaos, 15, 026101/1–5, 2005. [145] Gardiner, C. W. Handbook of Stochastic Methods: For Physics, Chemistry and the Natural Sciences. 2nd ed., Springer-Verlag, 1985. [146] Redner, S. A Guide to First-Passage Processes. Cambridge University Press, 2001. [147] Ricciardi, L. M. Diffusion Processes and Related Topics in Biology. Springer- Verlag, 1977. [148] Paninski, L., Haith, A., Szirtes, G. Integral equation methods for computing likelihoods and their derivatives in the stochastic integrate-and-fire model. J. Comput. Neurosci., 24, 69–79, 2008. [149] Siegert, A. On the first passage time probability problem. Phys. Rev., 81, 617– 623, 1951. [150] Tuckwell, H. C. On the first-exit time problem for temporally homogeneous markov processes. J. Appl. Prob., 13, 39–48, 1976. [151] Lindner, B., Longtin, A. Effect of an exponentially decaying threshold on the firing statistics of a stochastic integrate-and-fire neuron. J. Theor. Biol., 232, 505–521, 2005. [152] Tuckwell, H. C.,Wan, F. Y. M. First-passage time of markov processes to moving barriers. J. Appl. Prob., 21, 695–709, 1984. [153] Molini, A., Talkner, P., Katul, G. G., Porporato, A. First passage time statistics of brownian motion with purely time dependent drift and diffusion. Physica A, 390, 1841–1852, 2011. [154] Benda, J., Maler, L., Longtin, A. Nonlinear signal transmission in neuron models with adaptation currents or dynamic thresholds. J. Neurophysiol., 104(5), 2806– 2820, 2010. [155] Gammaitoni, L., H¨anggi, P., Jung, P., Marchesoni, F. Stochastic resonance. Rev. Mod. Phys., 70(1), 223–287, 1998. [156] Bulsara, A. R., Lowen, S. B., Rees, C. D. Cooperative behavior in the periodically modulated wiener process: Noise-induced complexity in a model neutron. Phys. Rev. E, 49(6), 4989–5000, 1994. [157] Gitterman, M., Weiss, G. H. Coherent stochastic resonance in the presence of a field. Phys. Rev. E, 52(5), 5708–5711, 1995. [158] Bulsara, A. R., Lowen, S. B., Rees, C. D. Reply to “coherent stochastic resonance in the presence of a field”. Phys. Rev. E, 52(5), 5712–5713, 1995. [159] Bulsara, A. R., Elston, T. C., Doering, C. R., Lowen, S. B., Lindenberg, K. Cooperative behavior in periodically driven noisy integrate-and-fire models of neuronal dynamics. Phys. Rev. E, 53(4), 3958–3969, 1996. [160] Schindler, M., Talkner, P., H¨anggi, P. Firing time statistics for driven neuron models: Analytic expressions versus numerics. Phys. Rev. Lett., 93(4), 048102/1– 4, 2004. [161] Burkitt, A. A review of the integrate-and-fire neuron model: Ii. inhomogeneous synaptic input and network properties. Biol. Cybern., 95, 97–112, 2006. [162] Choi, M. H., Fox, R. F. Evolution of escape processes with a time-varying load. Phys. Rev. E, 66, 031103/1–6, 2002. [163] Cox, D. R., Miller, H. D. The Theory of Stochastic Processes. Methuen and Co. Ltd., 1965. [164] Stein, R. B., French, A. S., Holden, A. V. The frequency response, coherence, and information capacity of two neuronal models. Biophys. J., 12, 295–322, 1972. [165] Oberhettinger, F., Badii, L. Tables of Laplace Transforms. Springer-Verlag, 1973. [166] Chung, S., Li, X., Nelson, S. B. Short-term depression at thalamocortical synapses contributes to rapid adaptation of cortical sensory responses in vivo. Neuron, 34, 437–446, 2002. [167] Benda, J., Longtin, A., Maler, L. Spike-frequency adaptation separates transient communication signals from background oscillations. J. Neurosci., 25(9), 2312– 2321, 2005. [168] Avila-Akerberg, O., Chacron, M. J. Nonrenewal spike train statistics: Causes and functional consequences on neural coding. Exp. Brain Res., 210, 353–371, 2011. [169] Cox, D. R., Lewis, P. A. W. The Statistical Analysis of Series of Events. Methuen, 1966. [170] Nawrot, M. P., Boucsein, C., Rodriguez-Molina, V., Aertsen, A., Grün, S., Rotter, S. Serial interval statistics of spontaneous activity in cortical neurons in vivo and in vitro. Neurocomputing, 70, 1717–1722, 2007. [171] Nawrot, M. P. Analysis and interpretation of interval and count variability in neural spike trains. En: S. Gr¨un, S. Rotter (eds.) Analysis of Parallel Spike Trains, p´ags. 37–58. Springer-Verlag, 2010. [172] 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, 021905/1–10, 2009. [173] Farkhooi, F., Muller, E., Nawrot, M. P. Adaptation reduces variability of the neuronal population code. Phys. Rev. E, 83, 050905/1–4, 2011. [174] Nesse, W. H., Maler, L., Longtin, A. Biophysical information representation in temporally correlated spike trains. Proc. Natl. Acad. Sci. USA, 107(51), 21973– 21978, 2010. [175] Lowen, S. B., Teich, M. C. Fractal-based Point Processes. John Wiley & Sons, 2005. [176] Lowen, S. B., Teich, M. C. Auditory-nerve action potentials form a nonrenewal point process over short as well as long time scales. J. Acoust. Soc. Am., 92(2), 803–806, 1992. [177] Longtin, A., Racicot, D. M. Spike train patterning and forecastability. Biosystems, 40, 111–118, 1997. [178] Ratnam, R., Nelson, M. E. Nonrenewal statistics of electrosensory afferent spike trains: Implications for the detection of weak sensory signals. J. Neurosci., 20(17), 6672–6683, 2000. [179] Chacron, M. J., Longtin, A., Maler, L. Negative interspike interval correlations increase the neuronal capacity for encoding time-dependent stimuli. J. Neurosci., 21(14), 5328–5343, 2001. [180] 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, 021920/1– 8, 2003. [181] Lindner, B. Interspike interval statistics of neurons driven by colored noise. Phys. Rev. E, 69, 022901/1–4, 2004. [182] Schwalger, T., Schimansky-Geier, L. Interspike interval statistics of a leaky integrate-and-fire neuron driven by gaussian noise with large correlation times. Phys. Rev. E, 77, 031914/1–9, 2008. [183] Schwalger, T., Fisch, K., Benda, J., Lindner, B. How noisy adaptation of neurons shapes interspike interval histograms and correlations. PLoS Comput. Biol., 6(12), e1001026, 2010. [184] Muller, E., Buesing, L., Schemmel, J., Meier, K. Spike-frequency adapting neural ensembles: Beyond mean adaptation and renewal theories. Neural Comput., 19(11), 2958–3010, 2007. [185] Schwalger, T., Lindner, B. Theory for serial correlations of interevent intervals. Eur. Phys. J. Spec. Topics, 187, 211–221, 2010. [186] Lindner, B., Schwalger, T. Correlations in the sequence of residence times. Phys. Rev. Lett., 98, 210603/1–4, 2007. [187] Schwalger, T., Lindner, B. Higher-order statistics of a bistable system driven by dichotomous colored noise. Phys. Rev. E, 78, 021121/1–13, 2008. [188] van Vreeswijk, C. Stochastic models of spike trains. En: S. Gr¨un, S. Rotter (eds.) Analysis of Parallel Spike Trains, p´ags. 3–20. Springer-Verlag, 2010. |
| Materias: | Biología > Biología celular Estadística |
| Divisiones: | Investigación y aplicaciones no nucleares > Física > Física estadística |
| Código ID: | 371 |
| Depositado Por: | Marisa G. Velazco Aldao |
| Depositado En: | 05 Nov 2012 12:09 |
| Última Modificación: | 05 Nov 2012 12:09 |
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