Neocortical Pyramidal Cells Respond as Integrate-and-Fire Neurons to In Vivo–Like Input Currents
Abstract
In the intact brain neurons are constantly exposed to intense synaptic activity. This heavy barrage of excitatory and inhibitory inputs was recreated in vitro by injecting a noisy current, generated as an Ornstein–Uhlenbeck process, into the soma of rat neocortical pyramidal cells. The response to such in vivo–like currents was studied systematically by analyzing the time development of the instantaneous spike frequency, and when possible, the stationary mean spike frequency as a function of both the mean and the variance of the input current. All cells responded with an in vivo–like action potential activity with stationary statistics that could be sustained throughout long stimulation intervals (tens of seconds), provided the frequencies were not too high. The temporal evolution of the response revealed the presence of mechanisms of fast and slow spike frequency adaptation, and a medium duration mechanism of facilitation. For strong input currents, the slow adaptation mechanism made the spike frequency response nonstationary. The minimal frequencies that caused strong slow adaptation (a decrease in the spike rate by more than 1 Hz/s), were in the range 30–80 Hz and depended on the pipette solution used. The stationary response function has been fitted by two simple models of integrate-and-fire neurons endowed with a frequency-dependent modification of the input current. This accounts for all the fast and slow mechanisms of adaptation and facilitation that determine the stationary response, and proved necessary to fit the model to the experimental data. The coefficient of variability of the interspike interval was also in part captured by the model neurons, by tuning the parameters of the model to match the mean spike frequencies only. We conclude that the integrate-and-fire model with spike-frequency–dependent adaptation/facilitation is an adequate model reduction of cortical cells when the mean spike-frequency response to in vivo–like currents with stationary statistics is considered.
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The sensitivity of individual neurons to synaptic fluctuations can vary with neural subtype or cortical area and with biophysical cellular properties, such as membrane capacitance and conductance.
“Furthermore, the sensitivity of individual neurons to synaptic fluctuations can vary with neural subtype or cortical area and with biophysical cellular properties, such as membrane capacitance and conductance89–93.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [93]
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ResultInferring learning rules from distributions of firing rates in cortical neurons
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BackgroundDopamine Increases the Gain of the Input-Output Response of Rat Prefrontal Pyramidal Neurons
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Self-citationSynaptic Shot Noise and Conductance Fluctuations Affect the Membrane Voltage with Equal Significance
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BackgroundUltrafast Population Encoding by Cortical Neurons
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“These results confirm the previously reported observation that large variance changes are necessary to elicit a population firing rate response (Rauch et al., 2003, their Fig.”
BackgroundResultMinimal Models of Adapted Neuronal Response to In Vivo–Like Input Currents
Camera, G. L., Rauch, A., Lüscher, H., Senn, W., & Fusi, S. (2004) · Neural Computation · doi:10.1162/0899766041732468 · Cited by 99 · see citing works
“We have shown in a previous report that the LIF neuron with AHP adaptation can be fitted to the experimental response functions of rat pyramidal neurons under noisy current injection (Rauch et al., 2003).”
BackgroundMethodResultInfluentialSelf-citationMixed selectivity: Cellular computations for complexity
Tye, K. M., Miller, E. K., Taschbach, F., Benna, M. K., Rigotti, M., & Fusi, S. (2024) · Neuron · doi:10.1016/j.neuron.2024.04.017 · Cited by 99 · see citing works
Self-citationModulation of Synaptic Plasticity by Glutamatergic Gliotransmission: A Modeling Study
Pittà, M. D., & Brunel, N. (2016) · Neural Plasticity · doi:10.1155/2016/7607924 · Cited by 96 · see citing works
“In line with experimental evidence [93], the input-output transfer function in the absence of gliotransmission has a typical sigmoidal shape (black dots) which reflects the following: (i) gradual emergence of firing for low (>10Hz) fluctuating synaptic inputs; (ii) the progressive, quasi-linear increase of the firing…”
BackgroundKumar, P. A., & Ohana, O. (2008) · Journal of Neurophysiology · doi:10.1152/jn.90684.2008 · Cited by 90 · see citing works
Single-Neuron Discharge Properties and Network Activity in Dissociated Cultures of Neocortex
Giugliano, M., Darbon, P., Arsiero, M., Lüscher, H.-R., & Streit, J. (2004) · Journal of Neurophysiology · doi:10.1152/jn.00067.2004 · Cited by 92 · see citing works
“As opposed to the previous report by Rauch et al. (2003), the passive properties and the corresponding best-fit effective parameters of the IF models showed a significant cross-correlation , both for the best-fit free search and the penalty search.”
BackgroundMethodResultInfluentialSelf-citationDeciphering neuron-glia compartmentalization in cortical energy metabolism
Jolivet, R. (2009) · Frontiers in Neuroenergetics · doi:10.3389/neuro.14.004.2009 · Cited by 87 · see citing works
“However, the frequency versus current (f-I) curve of cortical neurons is close to linear in the presence of noise (Rauch et al., 2003).”
Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data
Pereira, U., & Brunel, N. (2018) · Neuron · doi:10.1016/j.neuron.2018.05.038 · Cited by 89 · see citing works
Lánský, P., & Ditlevsen, S. (2008) · Biological Cybernetics · doi:10.1007/s00422-008-0237-x · Cited by 86 · see citing works
“Some references of estimation of input parameters from ISI data are Brillinger (1988), Inoue et al. (1995), Shinomoto et al. (1999), Rauch et al. (2003), Paninski et al. (2004), Ditlevsen and Lansky (2005, 2006, 2007, 2008) and Ditlevsen and Ditlevsen (2008).”
MethodDynamics and Plasticity of Stimulus-selective Persistent Activity in Cortical Network Models
Brunel, N. (2003) · Cerebral Cortex · doi:10.1093/cercor/bhg096 · Cited by 83 · see citing works
“For purely illustrative purposes, we use in all figures as plausible values for cortical pyramidal cells I c = 0.2 nA, ν c = 10 Hz (Rauch et al., 2003).”
MethodInfluentialSingle neuron dynamics and computation
Brunel, N., Hakim, V., & Richardson, M. J. E. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.01.005 · Cited by 86 · see citing works
Finite-size dynamics of inhibitory and excitatory interacting spiking neurons
Mattia, M., & Giudice, P. D. (2004) · Physical Review E · doi:10.1103/physreve.70.052903 · Cited by 82 · see citing works
An Event-Based Neural Network Architecture With an Asynchronous Programmable Synaptic Memory
Moradi, S., & Indiveri, G. (2013) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2013.2255873 · Cited by 79 · see citing works
“Also in this case the response is linear, but the curve saturates for high values of input currents (as observed experimentally also in real neurons [40] ), thanks to the effect of refractory period circuit.”
BackgroundVilela, R. D., & Lindner, B. (2009) · Physical Review E · doi:10.1103/physreve.80.031909 · Cited by 78 · see citing works
Action Potential Onset Dynamics and the Response Speed of Neuronal Populations
Naundorf, B., Geisel, T., & Wolf, F. (2005) · Journal of Computational Neuroscience · doi:10.1007/s10827-005-0329-8 · Cited by 78 · see citing works
“Benda and Herz, 2003; Rauch et al., 2003; Izhikevich, 2004), studies on the dynamic response properties are rare.”
BackgroundArsiero, M., Lüscher, H.-R., Lundstrom, B. N., & Giugliano, M. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.4937-06.2007 · Cited by 75 · see citing works
“For example, Rauch et al. (2003) mention that they observed a divergence in the linear part of the f–”
BackgroundMethodResultInfluentialSelf-citationOverview of facts and issues about neural coding by spikes
Cessac, B., Paugam‐Moisy, H., & Viéville, T. (2009) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2009.11.002 · Cited by 76 · see citing works
“See, e.g. Rauch et al. (2003) for further elements in the context of experimental frameworks and Camera et al. (2008a,b) for a review.”
Rudolph, M., & Alain Destexhe, A. (2006) · Neural Computation · doi:10.1162/neco.2006.18.9.2146 · Cited by 76 · see citing works
“conductance dynamics (exponential synapse; α-synapse, see Rall, 1967; synapse with two-state kinetics, see Destexhe, Mainen, & Sejnowski, 1994, 1998; computational models are described in appendix A). In all models, the parameters were adjusted to yield the same total conductance applied to the membrane for individual…”
BackgroundSpike-Driven Synaptic Dynamics Generating Working Memory States
Amit, D. J., & Mongillo, G. (2003) · Neural Computation · doi:10.1162/089976603321192086 · Cited by 74 · see citing works
“This model does not include adaptation effects, essential for fitting neural response dynamics of real cells (Rauch et al., 2002), limiting the increase in neural emission rates during sustained stimulation.”
BackgroundGiudice, P. D., Fusi, S., & Mattia, M. (2003) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2004.01.021 · Cited by 74 · see citing works
“Such a function has been measured for cortical cells in vitro and turned out to be well described by the theoretical transfer function of both IF models [44].”
Self-citationThe parameters of the stochastic leaky integrate-and-fire neuronal model
Lánský, P., Šanda, P., & He, J. (2006) · Journal of Computational Neuroscience · doi:10.1007/s10827-006-8527-6 · Cited by 73 · see citing works
“Estimation methods from in vitro voltage recordings for known input were presented by Stevens and Zador (1998), Rauch et al. (2003) , Le Camera et al. (2004), Jolivet et al. (2006), Paninski et al. (2004).”
MethodNonlinear local electrovascular coupling. I: A theoretical model
Riera, J. J., Wan, X., Jiménez, J. C., & Kawashima, R. (2006) · Human Brain Mapping · doi:10.1002/hbm.20230 · Cited by 71 · see citing works
Claudia Clopath, A., Jolivet, R., Rauch, A., Lüscher, H., & Gerstner, W. (2006) · Neurocomputing · doi:10.1016/j.neucom.2006.10.047 · Cited by 71 · see citing works
“The variable w describes the level of adaptation of the neuron and a represents the relevance of subthreshold adaptation.”
BackgroundSelf-citationAugustin, M., Ladenbauer, J., Baumann, F., & Obermayer, K. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005545 · Cited by 68 · see citing works
“Specifically, to account for a wide range of oscillation frequencies for presynaptic spike rates, ext is described by an Ornstein-Uhlenbeck (OU) process _ ext = ext ˝ ou + r 2 ˝ ou # ˘(t); (10) where ˝ ou denotes the correlation time, and # are the mean and standard deviation of the stationary normal distribution, i.e…”
MethodSpike-train spectra and network response functions for non-linear integrate-and-fire neurons
Richardson, M. J. E. (2008) · Biological Cybernetics · doi:10.1007/s00422-008-0244-y · Cited by 67 · see citing works
“It can be noted that a more depolarized value for such a zero-flux condition has been suggested (Lansky and Lanska 1987; Rauch et al. 2003) as an approximation to the effects of the inhibitory synaptic-reversal potential.”
BackgroundMethodSpike Timing or Rate? Neurons Learn to Make Decisions for Both Through Threshold-Driven Plasticity
Yu, Q., Li, H., & Tan, K. C. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2821692 · Cited by 67 · see citing works
“Theoretical and experimental results show that the firing rate of an integrate-and-fire neuron is a monotonic function of the mean input currents [48], [49].”
BackgroundA Mixed-Signal Structured AdEx Neuron for Accelerated Neuromorphic Cores
Aamir, S. A., Muller, P., Kiene, G., Kriener, L., Stradmann, Y., Grubl, A., Schemmel, J., & Meier, K. (2018) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2018.2848203 · Cited by 63 · see citing works
Spike-Frequency Adaptation and Intrinsic Properties of an Identified, Looming-Sensitive Neuron
Gabbiani, F., & Krapp, H. G. (2006) · Journal of Neurophysiology · doi:10.1152/jn.00075.2006 · Cited by 65 · see citing works
“J Neurophysiol • VOL 96 • DECEMBER 2006 • www.jn.org by 10.220.33.1 on O ctober 9, 2016 http://jn.physiology.org/ D ow nloaded from”
BackgroundInfluentialDynamics of the Instantaneous Firing Rate in Response to Changes in Input Statistics
Fourcaud‐Trocmé, N., & Brunel, N. (2005) · Journal of Computational Neuroscience · doi:10.1007/s10827-005-0337-8 · Cited by 62 · see citing works
“Rauch et al. (2003) studied f-I curves of cortical pyramidal cells in the presence of noise.”
BackgroundBand-to-Band Tunneling Based Ultra-Energy-Efficient Silicon Neuron
Chavan, T., Dutta, S., Mohapatra, N. R., & Ganguly, U. (2020) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2020.2985167 · Cited by 59 · see citing works
“Further, this leakiness potentially enables the neurons to process noisy signals in a noisy environment [30].”
BackgroundGABAB receptor‐dependent modulation of network activity in the rat prefrontal cortex in vitro
Wang, Y., Neubauer, F. B., Lüscher, H., & Thurley, K. (2010) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2010.07191.x · Cited by 59 · see citing works
“We fixed the correlation time to 3 ms in order to resemble fast excitatory and inhibitory synaptic inputs, and chose a standard deviation of 100 pA, which is in a similar range to what was used in other studies (Rauch et al., 2003; Arsiero et al., 2007; Thurley et al., 2008).”
BackgroundMethodResultInfluentialSelf-citationEvent-Driven Simulation of Spiking Neurons with Stochastic Dynamics
Reutimann, J., Giugliano, M., & Fusi, S. (2003) · Neural Computation · doi:10.1162/08997660360581912 · Cited by 57 · see citing works
Self-citationBabiec, W. E., Jami, S. A., Guglietta, R., Chen, P. B., & O’Dell, T. J. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.3196-16.2017 · Cited by 56 · see citing works
“As integrate-and-fire neurons, pyramidal cells are not only sensitive to the rising slope and peak of the EPSP but also the duration and magnitude of the EPSP envelope (Shadlen and Newsome, 1994; Sakai et al., 1999; Rauch et al., 2003).”
BackgroundStimulus Dependence of Local Field Potential Spectra: Experiment versus Theory
Barbieri, F., Mazzoni, A., Logothetis, N. K., Panzeri, S., & Brunel, N. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.5365-13.2014 · Cited by 53 · see citing works
Lundstrom, B. N., Famulare, M., Sorensen, L. B., Spain, W. J., & Fairhall, A. L. (2009) · Journal of Computational Neuroscience · doi:10.1007/s10827-009-0142-x · Cited by 53 · see citing works
“This can be seen as a type of gain control (Chance et al. 2002; Fellous et al. 2003; Rauch et al. 2003; Higgs et al. 2006; Arsiero et al. 2007; Lundstrom et al. 2008), whereby the strength of background fluctuations modulates the response to the mean.”
InfluentialMotoneuron membrane potentials follow a time inhomogeneous jump diffusion process
Jahn, P., Berg, R. W., Hounsgaard, J., & Ditlevsen, S. (2011) · Journal of Computational Neuroscience · doi:10.1007/s10827-011-0326-z · Cited by 53 · see citing works
“…the neuronal input known; references using intracellular recordings are Clopath et al. (2007), Huys et al. (2006), Jolivet et al. (2004), Jolivet et al. (2006), La Camera et al. (2004), Paninski et al. (2005), Rauch et al. (2003) and references using spike times are Paninski et al. (2004, 2005).”
MethodAnalysis of Cultured Neuronal Networks Using Intraburst Firing Characteristics
Stegenga, J., le Feber, J., Marani, E., & Rutten, W. L. C. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.913987 · Cited by 53 · see citing works
Sequential Memory: A Putative Neural and Synaptic Dynamical Mechanism
Deco, G., & Rolls, E. T. (2005) · Journal of Cognitive Neuroscience · doi:10.1162/0898929053124875 · Cited by 53 · see citing works
“A full statistical analysis of a model of sodium inactivation in the framework of integrate-and-fire models was introduced by Giugliano, La Camera, Rauch, Luescher and Fusi (2002) as a realistic candidate for long-lasting nonmonotonic effects in current-to-rate response functions observed in vitro ( Rauch, La Camera,…”
MethodVilela, R. D., & Lindner, B. (2008) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2008.11.004 · Cited by 52 · see citing works
“In another approach, model parameters are estimated using solely the ISI statistics (Tuckwell and Richter, 1978; Inoue et al., 1995; Rauch et al., 2003; Camera et al., 2004; Shinomoto et al., 1999; Ditlevsen and Lansky, 2005, 2007; Mullowney and Iyengar, 2008).”
Echoes in correlated neural systems
Helias, M., Tetzlaff, T., & Diesmann, M. (2013) · New Journal of Physics · doi:10.1088/1367-2630/15/2/023002 · Cited by 52 · see citing works
“A theory for networks of leaky integrate-and-fire (LIF) model neurons [27] is required, because this model has been shown to well approximate the properties of mammalian pyramidal neurons [28] and novel experimental techniques allow to reliably assess the temporal structure of correlations in the cortex [29].”
BackgroundEstimation of the input parameters in the Feller neuronal model
Ditlevsen, S., & Lánský, P. (2006) · Physical Review E · doi:10.1103/physreve.73.061910 · Cited by 52 · see citing works
“Considering the stochastic diffusion models, the literature often makes reference to cortical neurons in vivo which are characterized by irregular spike sequences and many synaptic inputs with relatively small individual contributions to the membrane depolarization [22, 25, 28, 31–34].”
BackgroundThe response of cortical neurons to in vivo-like input current: theory and experiment
Camera, G. L., Giugliano, M., Senn, W., & Fusi, S. (2008) · Biological Cybernetics · doi:10.1007/s00422-008-0272-7 · Cited by 50 · see citing works
“The maximal output firing rates in figure are the maximal firing rates sustainable by the neurons Camera et al. 2006), pyramidal (PYR) neurons from SSC (Rauch et al. 2003) and mPFC (Arsiero et al. 2007), and dissociated cultures of rat neocortex (Giugliano et al. 2004).”
InfluentialSelf-citationSynaptic and nonsynaptic plasticity approximating probabilistic inference
Tully, P. J., Hennig, M. H., & Lansner, A. (2014) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2014.00008 · Cited by 47 · see citing works
“…of background activity caused an expansive non-linearity in the IAF input-output curve within a physiologically relevant 1 up to 20 Hz range of firing, which has been reported previously for conductance-based IAF neurons (Fourcaud-Trocmé et al., 2003) and cortical neurons (Rauch et al., 2003).”
BackgroundMaximum-Entropy Closures for Kinetic Theories of Neuronal Network Dynamics
Rangan, A. V., & Cai, D. (2006) · Physical Review Letters · doi:10.1103/physrevlett.96.178101 · Cited by 49 · see citing works
Intrinsic Mechanisms for Adaptive Gain Rescaling in Barrel Cortex
Díaz-Quesada, M., & Maravall, M. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.4931-07.2008 · Cited by 48 · see citing works
“…to white noise currents that share properties of in vivo activity, and Gaussian white noise provides a basic model for the distribution of total current inputs at the soma under conditions of intense synaptic bombardment (Mainen and Sejnowski, 1995; Destexhe et al., 2001; Rauch et al., 2003).”
BackgroundResultInfluentialCoding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram
Naud, R., & Wulfram Gerstner, B. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002711 · Cited by 50 · see citing works
“To include adaptation into previously non-adaptive models, a common approach is to modify the effective input by rescaling the external input with a function that depends on the mean neuronal firing rate in the past [15,43,44].”
BackgroundMethodApfaltrer, F., Ly, C., & Tranchina, D. (2006) · Network Computation in Neural Systems · doi:10.1080/09548980601069787 · Cited by 48 · see citing works
“Furthermore, Rauch et al. (2003) showed that “neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like currents.””
BackgroundLearning in realistic networks of spiking neurons and spike‐driven plastic synapses
Mongillo, G., Curti, E., Romani, S., & Amit, D. J. (2005) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2005.04087.x · Cited by 45 · see citing works
“The neural elements are exponential integrate-and-fire neurons, which capture experimentally observed neural response characteristics (Rauch et al ., 2003).”
BackgroundIigaya, K. (2016) · eLife · doi:10.7554/elife.18073 · Cited by 43 · see citing works
“…or collection of multiple exponents, rather than a single exponent ( Thorson and Biederman-Thorson, 1974 ; Ulanovsky et al., 2004 ; Corrado et al., 2005 ; Fusi et al., 2007 ; Kording et al., 2007 ; Wark et al., 2009 ; Lundstrom et al., 2010 ; Rauch et al., 2003 ; Pozzorini et al., 2013 ).”
BackgroundFast numerical methods for simulating large-scale integrate-and-fire neuronal networks
Rangan, A. V., & Cai, D. (2006) · Journal of Computational Neuroscience · doi:10.1007/s10827-006-8526-7 · Cited by 43 · see citing works
“…neuron is very idealized, therefore, a simulation of I&F neuronal networks can (usually) only offer a statistical understanding of a real physiological network, rather than a detailed neuron-by-neuron account of that network (Rauch et al., 2003; Fourcaud-Trocme et al., 2003; Geisler et al., 2005).”
BackgroundMechanisms That Modulate the Transfer of Spiking Correlations
Rosenbaum, R., & Josić́, K. (2011) · Neural Computation · doi:10.1162/neco_a_00116 · Cited by 39 · see citing works
“properties of neurons (Salinas and Sejnowski, 2000; Rauch et al., 2003) and captures the fundamental mechanisms of a physiological cell.”
BackgroundHertäg, L., Haß, J., Golovko, T., & Durstewitz, D. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00062 · Cited by 40 · see citing works
“I curves can change considerably with the input (Chance et al., 2002; Rauch et al., 2003).”
BackgroundMethodInfluentialZerlaut, Y., Telenczuk, B., Deleuze, C., Bal, T., Ouanounou, G., & Destexhe, A. (2016) · Journal of Physiology · doi:10.1113/jp272317 · Cited by 46 · see citing works
“To obtain a meaningful estimation of the firing rate response we need long and stable recordings (Rauch et al. 2003; Köndgen et al. 2008).”
BackgroundLundstrom, B. N., Hong, S., Higgs, M. H., & Fairhall, A. L. (2008) · Neural Computation · doi:10.1162/neco.2007.05-07-536 · Cited by 39 · see citing works
Mean-field analysis of selective persistent activity in presence of short-term synaptic depression
Romani, S., Amit, D. J., & Mongillo, G. (2006) · Journal of Computational Neuroscience · doi:10.1007/s10827-006-6308-x · Cited by 39 · see citing works
“The IF description of the spike emission process captures rather well the in vitro discharge dynamics of real, biological neurons ( Rauch et al., 2003 ).”
BackgroundEnhanced Responsiveness and Low-Level Awareness in Stochastic Network States
Zerlaut, Y., & Alain Destexhe, A. (2017) · Neuron · doi:10.1016/j.neuron.2017.04.001 · Cited by 37 · see citing works
Granger Causality Network Reconstruction of Conductance-Based Integrate-and-Fire Neuronal Systems
Zhou, D., Xiao, Y., Zhang, Y., Xu, Z. J., & Cai, D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0087636 · Cited by 35 · see citing works
“As mentioned previously, with in-vivo-like current injection, the I & F neuronal model can capture well both the firing rate and sub-threshold dynamics of cortical neurons [30,31].”
BackgroundCook, E. P., Guest, J. A., Liang, Y., Masse, N. Y., & Colbert, C. M. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00414.2007 · Cited by 34 · see citing works
Mensi, S., Hagens, O., Gerstner, W., & Pozzorini, C. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004761 · Cited by 34 · see citing works
“In vivo-like fluctuating currents were generated with a filtered Gaussian process and injected at the soma of L5 Pyr neurons of mouse SSC (see Materials and Methods).”
ResultInfluentialFundamental Activity Constraints Lead to Specific Interpretations of the Connectome
Schuecker, J., Schmidt, M., van Albada, S. J., Diesmann, M., & Helias, M. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005179 · Cited by 34 · see citing works
“Moreover, the current-based leaky integrate-and-fire neuron can reproduce in-vivo like activity (Rauch et al., 2003; Jolivet et al., 2006) and is analytically tractable, which enables the identification of mechanisms underlying specific network effects.”
BackgroundInstantaneous Non-Linear Processing by Pulse-Coupled Threshold Units
Helias, M., Deger, M., Rotter, S., & Diesmann, M. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000929 · Cited by 33 · see citing works
“The integrate-and-fire neuron model [1] can efficiently be simulated [2,3] and well approximates the properties of mammalian neurons [4–6] and more detailed models [7].”
MethodThe Human Body as a Super Network: Digital Methods to Analyze the Propagation of Aging
Whitwell, H. J., Bacalini, M. G., Blyuss, O. B., Chen, S., Garagnani, P., Gordleeva, S. Y., Jalan, S., Ivanchenko, M., Игоревич, К. О., Кустикова, В. Д., et al. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00136 · Cited by 32 · see citing works
“Substantial effort has been made to develop simple models of excitatory and inhibitory spiking neurons, aiming to mimic the cortical activity (Gutkin and Ermentrout, 1998; Rauch et al., 2003; Jolivet et al., 2004, 2006; Shlizerman and Holmes, 2012).”
BackgroundCurti, E., Mongillo, G., Camera, G. L., & Amit, D. J. (2004) · Neural Computation · doi:10.1162/0899766042321805 · Cited by 32 · see citing works
Stimulus Competition by Inhibitory Interference
Tiesinga, P. H. E. (2005) · Neural Computation · doi:10.1162/0899766054796905 · Cited by 32 · see citing works
“Previously, three other mechanisms have been proposed for how gain changes can be achieved (Burkitt, 2001; Burkitt et al., 2003; Chance et al., 2002; Destexhe et al., 2001; Doiron et al., 2001; Fellous et al., 2003; Holt and Koch, 1997; Kuhn et al., 2004 ; Larkum et al., 2004; Mitchell and Silver, 2003; Murphy and Mil…”
BackgroundPredicting single spikes and spike patterns with the Hindmarsh–Rose model
de Lange, E., & Hasler, M. (2008) · Biological Cybernetics · doi:10.1007/s00422-008-0260-y · Cited by 32 · see citing works
“The first set consists of four rat neocortical pyramidal cells to which a protocol of in vivo like stimulation signals has been applied (Rauch et al. 2003; Destexhe et al. 2001).”
BackgroundMethodStochasticity from function — Why the Bayesian brain may need no noise
Dold, D., Bytschok, I., Kungl, Á. F., Baumbach, A., Breitwieser, O., Senn, W., Schemmel, J., Meier, K., & Petrovici, M. A. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.08.002 · Cited by 29 · see citing works
“We have chosen the above model because it provides a computationally tractable abstraction of neurosynaptic dynamics [40], but our general conclusions are not restricted to these specific dynamics.”
MethodSelf-citationDynamics of the exponential integrate-and-fire model with slow currents and adaptation
Barranca, V. J., Johnson, D. C., Moyher, J. L., Sauppe, J. P., Shkarayev, M. S., Kovačič, G., & Cai, D. (2014) · Journal of Computational Neuroscience · doi:10.1007/s10827-013-0494-0 · Cited by 30 · see citing works
“…as was discussed in Kistler et al. (1997) and Fourcaud-Trocme et al. (2003), with appropriately chosen parameter values, versions of the IF model provide an excellent approximation of the HH model and even some experimental data (Carandini 1996; Rauch et al. 2003; Badel et al. 2008a, b).”
MethodResultVasilaki, E., & Giugliano, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0084626 · Cited by 30 · see citing works
“This is known to affect the firing rate distributions in recurrent model networks (82; 83; 39), and generally lower the location of steady-states stable equilibria (Figs.”
Background
- Authors
- Rauch, A., La Camera, G., Lüscher, H.-R., Senn, W., & Fusi, S.
- Affiliations
- University of Bern (CH)
- Institute of Physiology, University of Bern, 3012 Bern, Switzerland
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 2003-09
- Volume
- 90
- Issue
- 3
- Pages
- 1598-1612
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- OpenAlex
- W2120259469
- Cited by
- 264
- References
- 59
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 10 · 2023: 15 · 2022: 9 · 2021: 6 · 2020: 8 · 2019: 10 · 2018: 9 · 2017: 14 · 2016: 20 · 2015: 12 · 2014: 13 · 2013: 8 · 2012: 6
- FWCI
- 7.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Neurophysiology (publishedVersion)
- PubMed (publishedVersion)
- BORIS (University Library Bern) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- neocortical pyramidal cells, integrate-and-fire neuron model, spike frequency adaptation, facilitation, Ornstein-Uhlenbeck process, coefficient of variation, synaptic noise, rat neocortex
- MeSH terms
- Action Potentials, Animals, Female, Male, Models, Biological, Neurons, Rats, Wistar, Pyramidal Cells, Neocortex, Rats, In Vitro Techniques
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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