메뉴 건너뛰기




Volumn 23, Issue 12, 2011, Pages 3070-3093

Estimation of time-dependent input from neuronal membrane potential

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ALGORITHM; ANIMAL; BIOLOGICAL MODEL; BRAIN CORTEX; CELL MEMBRANE POTENTIAL; CYTOLOGY; HUMAN; LETTER; NERVE CELL; PHYSIOLOGY; STATISTICS; TIME;

EID: 84856380698     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00205     Document Type: Letter
Times cited : (26)

References (49)
  • 1
    • 0000395622 scopus 로고    scopus 로고
    • Field theories for learning probability distributions
    • Bialek,W., Callan, C. G., & Strong, S. P. (1996). Field theories for learning probability distributions. Phys. Rev. Lett., 77, 4693-4697.
    • (1996) Phys. Rev. Lett. , vol.77 , pp. 4693-4697
    • Bialek, W.1    Callan, C.G.2    Strong, S.P.3
  • 2
    • 0032574998 scopus 로고    scopus 로고
    • Visual input evokes transient and strong shunting inhibition in visual cortical neurons
    • Borg-Graham, L. J., Monier, C., & Frégnac, Y. (1998). Visual input evokes transient and strong shunting inhibition in visual cortical neurons. Nature, 393, 369-373.
    • (1998) Nature , vol.393 , pp. 369-373
    • Borg-Graham, L.J.1    Monier, C.2    Frégnac, Y.3
  • 3
    • 48749103873 scopus 로고    scopus 로고
    • High-resolution intracellular recordings using a real-time computational model of the electrode
    • Brette, R., Piwkowska, Z., Monier,C., Rudolph-Lilith, M., Fournier, J., Levy,M., et al. (2008). High-resolution intracellular recordings using a real-time computational model of the electrode. Neuron, 59, 379-391.
    • (2008) Neuron , vol.59 , pp. 379-391
    • Brette, R.1    Piwkowska, Z.2    Monier, C.3    Rudolph-Lilith, M.4    Fournier, J.5    Levy, M.6
  • 4
    • 33745712258 scopus 로고    scopus 로고
    • A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input
    • Burkitt, A. N. (2006). A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input. Biol. Cybern., 95, 1-19.
    • (2006) Biol. Cybern. , vol.95 , pp. 1-19
    • Burkitt, A.N.1
  • 5
    • 0002629270 scopus 로고
    • Maximum likelihood from incomplete data via the EM algorithm
    • Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. B, 39, 1-38.
    • (1977) J. Roy. Stat. Soc. B , vol.39 , pp. 1-38
    • Dempster, A.P.1    Laird, N.M.2    Rubin, D.B.3
  • 6
    • 0003043451 scopus 로고    scopus 로고
    • Kinetic models of synaptic transmission
    • C. Koch & I. Segev (Eds.), Cambridge, MA: MIT Press
    • Destexhe, A., Mainen, Z., & Sejnowski, T. J. (1998). Kinetic models of synaptic transmission. In C. Koch & I. Segev (Eds. ), Methods in neuronal modeling (pp. 1-26). Cambridge, MA: MIT Press.
    • (1998) Methods in neuronal modeling , pp. 1-26
    • Destexhe, A.1    Mainen, Z.2    Sejnowski, T.J.3
  • 7
    • 0141499222 scopus 로고    scopus 로고
    • The high-conductance state of neocortical neurons in vivo
    • Destexhe, A., Rudolph, M., & Pare, D. (2003). The high-conductance state of neocortical neurons in vivo. Nat. Rev. Neurosci., 4, 739-751.
    • (2003) Nat. Rev. Neurosci. , vol.4 , pp. 739-751
    • Destexhe, A.1    Rudolph, M.2    Pare, D.3
  • 8
    • 33751309853 scopus 로고    scopus 로고
    • Non-gaussian membrane potential dynamics imply sparse, synchronous activity in auditory cortex
    • DeWeese, M. R.,&Zador, A. M. (2006).Non-gaussian membrane potential dynamics imply sparse, synchronous activity in auditory cortex J. Neurosci., 26, 12206-12218.
    • (2006) J. Neurosci. , vol.26 , pp. 12206-12218
    • DeWeese, M.R.1    Zador, A.M.2
  • 9
    • 0033518170 scopus 로고    scopus 로고
    • Stable propagation of synchronous spiking in cortical neural networks
    • Diesmann, M., Gewaltig, M. O., & Aertsen, A. (1999). Stable propagation of synchronous spiking in cortical neural networks. Nature, 402, 529-533.
    • (1999) Nature , vol.402 , pp. 529-533
    • Diesmann, M.1    Gewaltig, M.O.2    Aertsen, A.3
  • 10
    • 1842733199 scopus 로고    scopus 로고
    • Dynamic analyses of neural encoding by point process adaptive filtering
    • Eden, U. T., Frank, L. M., Barbieri, R., Solo, V., & Brown, E. N. (2004). Dynamic analyses of neural encoding by point process adaptive filtering. Neural Comput., 16, 971-998.
    • (2004) Neural Comput. , vol.16 , pp. 971-998
    • Eden, U.T.1    Frank, L.M.2    Barbieri, R.3    Solo, V.4    Brown, E.N.5
  • 12
    • 67049095670 scopus 로고    scopus 로고
    • Smoothing of, and parameter estimation from, noisy biophysical recordings
    • Huys, Q.J.M., & Paninski, L. (2009). Smoothing of, and parameter estimation from, noisy biophysical recordings. PLoS Comput. Biol., 5, e1000379.
    • (2009) PLoS Comput. Biol. , vol.5
    • Huys, Q.J.M.1    Paninski, L.2
  • 14
    • 3142666109 scopus 로고    scopus 로고
    • Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailedmodel to a high degree of accuracy
    • Jolivet, R., Lewis, T. J., & Gerstner,W. (2004). Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailedmodel to a high degree of accuracy. J. Neurophysiol., 92, 959-976.
    • (2004) J. Neurophysiol. , vol.92 , pp. 959-976
    • Jolivet, R.1    Lewis, T.J.2    Gerstner, W.3
  • 15
    • 0000706722 scopus 로고    scopus 로고
    • Reduction of the Hodgkin-Huxley equations to a single-variable threshold model
    • Kistler, W., Gerstner, W., & van Hemmen, J. L. (1997). Reduction of the Hodgkin-Huxley equations to a single-variable threshold model. Neural Comput., 9, 1015-1045.
    • (1997) Neural Comput. , vol.9 , pp. 1015-1045
    • Kistler, W.1    Gerstner, W.2    van Hemmen, J.L.3
  • 16
    • 0032347276 scopus 로고    scopus 로고
    • A self-organizing state-space model
    • Kitagawa, G. (1998). A self-organizing state-space model. J. Am. Stat. Assoc., 93, 1203-1215.
    • (1998) J. Am. Stat. Assoc. , vol.93 , pp. 1203-1215
    • Kitagawa, G.1
  • 17
    • 33846650165 scopus 로고    scopus 로고
    • State space method for predicting the spike times of a neuron
    • Kobayashi, R., & Shinomoto, S. (2007). State space method for predicting the spike times of a neuron. Phys. Rev. E, 75, 011925.
    • (2007) Phys. Rev. E , vol.75 , pp. 011925
    • Kobayashi, R.1    Shinomoto, S.2
  • 18
    • 77953482919 scopus 로고    scopus 로고
    • Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold
    • Kobayashi, R., Tsubo, Y., & Shinomoto, S. (2009). Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold. Front. Comput. Neurosci., 3, 9.
    • (2009) Front. Comput. Neurosci. , vol.3 , pp. 9
    • Kobayashi, R.1    Tsubo, Y.2    Shinomoto, S.3
  • 19
    • 77956912830 scopus 로고    scopus 로고
    • Comparison of brain-computer interface decoding algorithms in open-loop and closed-loop control
    • Koyama, S., Chase, S. M., Whitford, A. S., Velliste, M., Schwartz, A. B., & Kass, R. E. (2010). Comparison of brain-computer interface decoding algorithms in open-loop and closed-loop control. J. Comput. Neurosci., 29, 73-87.
    • (2010) J. Comput. Neurosci. , vol.29 , pp. 73-87
    • Koyama, S.1    Chase, S.M.2    Whitford, A.S.3    Velliste, M.4    Schwartz, A.B.5    Kass, R.E.6
  • 21
    • 22544467020 scopus 로고    scopus 로고
    • Empirical Bayes interpretations of random point events
    • Koyama, S.,&Shinomoto, S. (2005). Empirical Bayes interpretations of random point events. J. Physics A, 38, L531-L537.
    • (2005) J. Physics A , vol.38
    • Koyama, S.1    Shinomoto, S.2
  • 22
    • 0021076773 scopus 로고
    • Inference for the diffusion models of neuronal activity
    • Lansky, P. (1983). Inference for the diffusion models of neuronal activity. Math. Biosci., 67, 247-260.
    • (1983) Math. Biosci. , vol.67 , pp. 247-260
    • Lansky, P.1
  • 23
    • 0021279321 scopus 로고
    • On approximations of Stein's neuronal model
    • Lansky, P. (1984). On approximations of Stein's neuronal model. J. Theor. Biol., 107, 631-647.
    • (1984) J. Theor. Biol. , vol.107 , pp. 631-647
    • Lansky, P.1
  • 24
    • 56549129789 scopus 로고    scopus 로고
    • A review of the methods for signal estimation in stochastic diffusion leaky integrate-and-fire neuronal models
    • Lansky, P., & Ditlevsen, S. (2008). A review of the methods for signal estimation in stochastic diffusion leaky integrate-and-fire neuronal models. Biol. Cybern., 99, 253-262.
    • (2008) Biol. Cybern. , vol.99 , pp. 253-262
    • Lansky, P.1    Ditlevsen, S.2
  • 25
    • 0035796609 scopus 로고    scopus 로고
    • The Ornstein-Uhlenbeck neuronal model with signal-dependent noise
    • Lansky, P., & Sacerdote, L. (2001). The Ornstein-Uhlenbeck neuronal model with signal-dependent noise. Phys. Lett. A, 285, 132-140.
    • (2001) Phys. Lett. A , vol.285 , pp. 132-140
    • Lansky, P.1    Sacerdote, L.2
  • 26
    • 33747272940 scopus 로고    scopus 로고
    • The parameters of the stochastic leaky integrate-and-fire neuronal model
    • Lansky, P., Sanda, P.,&He, J. (2006). The parameters of the stochastic leaky integrate-and-fire neuronal model. J. Comput. Neurosci., 21, 211-223.
    • (2006) J. Comput. Neurosci. , vol.21 , pp. 211-223
    • Lansky, P.1    Sanda, P.2    He, J.3
  • 27
    • 77952548905 scopus 로고    scopus 로고
    • Effect of stimulation on the input parameters of stochastic leaky integrate-and-fire neuronal model
    • Lansky, P., Sanda, P., & He, J. (2010). Effect of stimulation on the input parameters of stochastic leaky integrate-and-fire neuronal model. J. Physiol. (Paris), 104, 160-166.
    • (2010) J. Physiol. (Paris) , vol.104 , pp. 160-166
    • Lansky, P.1    Sanda, P.2    He, J.3
  • 28
    • 33745837827 scopus 로고    scopus 로고
    • Comment on "Characterization of subthreshold voltage fluctuations in neuronal membranes," by M. Rudolph and A. Destexhe
    • Lindner, B., & Longtin, A. (2006). Comment on "Characterization of subthreshold voltage fluctuations in neuronal membranes," by M. Rudolph and A. Destexhe. Neural Comp., 18, 1896-1931.
    • (2006) Neural Comp. , vol.18 , pp. 1896-1931
    • Lindner, B.1    Longtin, A.2
  • 29
    • 41049116498 scopus 로고    scopus 로고
    • In vitro and in vivomeasures of evoked excitatory and inhibitory conductance dynamics in sensory cortices
    • Monier, C., Fournier, J., & Frégnac, Y. (2008). In vitro and in vivomeasures of evoked excitatory and inhibitory conductance dynamics in sensory cortices. J. Neurosci. Methods, 169, 323-365.
    • (2008) J. Neurosci. Methods , vol.169 , pp. 323-365
    • Monier, C.1    Fournier, J.2    Frégnac, Y.3
  • 31
    • 0031941210 scopus 로고    scopus 로고
    • Impact of spontaneous synaptic activity on the resting properties of cat neocortical neurons in vivo
    • Pare, D., Shink, E., Gaudreau, H., Destexhe, A., & Lang, E. J. (1998). Impact of spontaneous synaptic activity on the resting properties of cat neocortical neurons in vivo. J. Neurophysiol., 79, 1450-1460.
    • (1998) J. Neurophysiol. , vol.79 , pp. 1450-1460
    • Pare, D.1    Shink, E.2    Gaudreau, H.3    Destexhe, A.4    Lang, E.J.5
  • 32
    • 0032159698 scopus 로고    scopus 로고
    • Noise-induced coherent oscillations in randomly connected neural networks
    • Pham, J., Pakdaman, K., & Vibert, J.-F. (1998). Noise-induced coherent oscillations in randomly connected neural networks. Phys. Rev. E, 58, 3610-3622.
    • (1998) Phys. Rev. E , vol.58 , pp. 3610-3622
    • Pham, J.1    Pakdaman, K.2    Vibert, J.-F.3
  • 33
    • 58149469532 scopus 로고    scopus 로고
    • Extracting synaptic conductances from single membrane potential traces
    • Pospischil, M., Piwkowska, Z., Bal, T., & Destexhe, A. (2009). Extracting synaptic conductances from single membrane potential traces. Neuroscience, 158, 545-552.
    • (2009) Neuroscience , vol.158 , pp. 545-552
    • Pospischil, M.1    Piwkowska, Z.2    Bal, T.3    Destexhe, A.4
  • 34
    • 0017088370 scopus 로고
    • Diffusion approximation for a multi-input model neuron
    • Ricciardi, L. M. (1976). Diffusion approximation for a multi-input model neuron. Biol. Cybern., 24, 237-240.
    • (1976) Biol. Cybern. , vol.24 , pp. 237-240
    • Ricciardi, L.M.1
  • 36
    • 0141860842 scopus 로고    scopus 로고
    • Characterization of subthreshold voltage fluctuations in neuronal membranes
    • Rudolph, M., & Destexhe, A. (2003). Characterization of subthreshold voltage fluctuations in neuronal membranes. Neural Comput., 15, 2577-2618.
    • (2003) Neural Comput. , vol.15 , pp. 2577-2618
    • Rudolph, M.1    Destexhe, A.2
  • 37
    • 25144447290 scopus 로고    scopus 로고
    • An extended analytical expression for the membrane potential distribution of conductance-based synaptic noise
    • Rudolph, M., & Destexhe, A. (2005). An extended analytical expression for the membrane potential distribution of conductance-based synaptic noise. Neural Comput., 17, 2301-2315.
    • (2005) Neural Comput. , vol.17 , pp. 2301-2315
    • Rudolph, M.1    Destexhe, A.2
  • 38
    • 2442615811 scopus 로고    scopus 로고
    • A method to estimate synaptic conductances from membrane potential fluctuations
    • Rudolph, M., Piwkowska, Z., Badoual, M., Bal, T., & Destexhe, A. (2004). A method to estimate synaptic conductances from membrane potential fluctuations. J. Neurophysiol., 91, 2884-2896.
    • (2004) J. Neurophysiol. , vol.91 , pp. 2884-2896
    • Rudolph, M.1    Piwkowska, Z.2    Badoual, M.3    Bal, T.4    Destexhe, A.5
  • 39
    • 34249044194 scopus 로고    scopus 로고
    • Inhibition determines membrane potential dynamics and controls action potential generation in awake and sleeping cat cortex
    • Rudolph, M., Pospischil, M., Timofeev, I., & Destexhe, A. (2007). Inhibition determines membrane potential dynamics and controls action potential generation in awake and sleeping cat cortex J. Neurosci., 27, 5280-5290.
    • (2007) J. Neurosci. , vol.27 , pp. 5280-5290
    • Rudolph, M.1    Pospischil, M.2    Timofeev, I.3    Destexhe, A.4
  • 41
    • 0032525177 scopus 로고    scopus 로고
    • The variable discharge of cortical neurons: Implications for connectivity, computation, and information coding
    • Shadlen, M. N.,&Newsome,W. T. (1998). The variable discharge of cortical neurons: Implications for connectivity, computation, and information coding. J. Neurosci., 18, 3870-3896.
    • (1998) J. Neurosci. , vol.18 , pp. 3870-3896
    • Shadlen, M.N.1    Newsome, W.T.2
  • 42
    • 68249118540 scopus 로고    scopus 로고
    • Estimating instantaneous irregularity of neuronal firing
    • Shimokawa, T., & Shinomoto, S. (2009). Estimating instantaneous irregularity of neuronal firing. Neural Comput., 21, 1931-1951.
    • (2009) Neural Comput. , vol.21 , pp. 1931-1951
    • Shimokawa, T.1    Shinomoto, S.2
  • 43
    • 0033561846 scopus 로고    scopus 로고
    • The Ornstein-Uhlenbeck process does not reproduce spiking statistics of neurons in prefrontal cortex
    • 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.
    • (1999) Neural Comput. , vol.11 , pp. 935-951
    • Shinomoto, S.1    Sakai, Y.2    Funahashi, S.3
  • 44
    • 0038605051 scopus 로고    scopus 로고
    • Estimating a state-space model from point process observations
    • Smith, A. C., & Brown, E. N. (2003). Estimating a state-space model from point process observations. Neural Comput., 15, 965-991.
    • (2003) Neural Comput. , vol.15 , pp. 965-991
    • Smith, A.C.1    Brown, E.N.2
  • 45
    • 0027498486 scopus 로고
    • The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs
    • Softky,W. R., & Koch, C. (1993). The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J. Neurosci., 13: 334-350.
    • (1993) J. Neurosci. , vol.13 , pp. 334-350
    • Softky, W.R.1    Koch, C.2
  • 46
    • 11844290435 scopus 로고
    • Atheoretical analysis of neuronal variability
    • Stein, R. B. (1965).Atheoretical analysis of neuronal variability. Biophys. J., 5, 173-194.
    • (1965) Biophys. J. , vol.5 , pp. 173-194
    • Stein, R.B.1
  • 48
    • 59649129373 scopus 로고    scopus 로고
    • Are the input parameters of white noise driven integrate and fire neurons uniquely determined by rate and CV?
    • Vilela, R. D., & Lindner, B. (2009). Are the input parameters of white noise driven integrate and fire neurons uniquely determined by rate and CV? J. Theor. Biol., 257, 90-99.
    • (2009) J. Theor. Biol. , vol.257 , pp. 90-99
    • Vilela, R.D.1    Lindner, B.2
  • 49
    • 0344120308 scopus 로고    scopus 로고
    • Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex
    • Wehr, M., & Zador, A. M. (2003). Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex. Nature, 426, 442-446.
    • (2003) Nature , vol.426 , pp. 442-446
    • Wehr, M.1    Zador, A.M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.