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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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“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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Items 101–200 of 264
Divisive Gain Modulation with Dynamic Stimuli in Integrate-and-Fire Neurons
Ly, C., & Doiron, B. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000365 · Cited by 28
“has also been recorded in pyramidal cells and fast-spiking interneurons”
BackgroundFeature selection in simple neurons: how coding depends on spiking dynamicsPreprint
Famulare, M., & Fairhall, A. L. (2012) · Cited by 28
Scale-Free Navigational Planning by Neuronal Traveling Waves
Alijani, A. K., Urbanczik, R., & Senn, W. (2015) · PLoS ONE · doi:10.1371/journal.pone.0127269 · Cited by 25
Self-citationAmsalem, O., Geit, W. A. H. V., Muller, E. B., Markram, H., & Segev, I. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw166 · Cited by 30
“…steady-state (DC) current, I1 themodulated input (as a function of f, frequency), and Inoise the noise component which was generated as a realization of an Ornstein–Uhlenbeck stochastic processwith zero-mean, variance s2, and time correlation τnoise = 5 ms (Rauch et al. 2003; Köndgen et al. 2008).”
BackgroundThalamic neuron models encode stimulus information by burst-size modulation
Elijah, D. H., Samengo, I., & Montemurro, M. A. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00113 · Cited by 26
“We stimulated the models with an Ornstein-Uhlenbeck (OU) colored noise signal (Uhlenbeck andOrnstein, 1930; Smith, 1992; Gillespie, 1996; Rauch et al., 2003; Bibbona et al., 2008).”
MethodParameters of the Diffusion Leaky Integrate-and-Fire Neuronal Model for a Slowly Fluctuating Signal
Picchini, U., Ditlevsen, S., Gaetano, A. D., & Lánský, P. (2008) · Neural Computation · doi:10.1162/neco.2008.11-07-653 · Cited by 26
“Some references in this line using frequently sampled membrane potential measurements between spikes are Rauch et al. (2003); Jolivet et al. (2004, 2006); La Camera et al. (2004); Paninski et al. (2005); Huys et al. (2006); Clopath et al. (2007) and references using first-passage time data are…”
MethodMultiplexing working memory and time in the trajectories of neural networks
Zhou, S., Seay, M. J., Taxidis, J., Golshani, P., & Buonomano, D. V. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01592-y · Cited by 26
“However, there are counterarguments based on noise-induced ‘smoothing’ that continuous functions may be more naturalistic 64,65 .”
BackgroundSpike generation estimated from stationary spike trains in a variety of neurons in vivo
Spanne, A., Geborek, P., Bengtsson, F., & Jörntell, H. (2014) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2014.00199 · Cited by 28
“(C; middle, right) For comparison with previous results, the CV in relation to the average length of the ISIs (middle) (Softky and Koch, 1993) and the CV in relation to the instantaneous firing frequency (right) (Rauch et al., 2003) is also included in the figure.”
BackgroundMethodResultInfluentialNon-Additive Coupling Enables Propagation of Synchronous Spiking Activity in Purely Random Networks
Memmesheimer, R.-M., & Timme, M. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002384 · Cited by 23
“…[44,45] for recent reviews) because they capture essential features of cortical neurons and at the same time allow to investigate the mechanisms underlying the dynamics of networks without obscuring them by a many-parameter, many-variable single neuron description (see, e.g., [44,46–48]).”
Impact of slow K+ currents on spike generation can be described by an adaptive threshold model
Kobayashi, R., & Kitano, K. (2016) · Journal of Computational Neuroscience · doi:10.1007/s10827-016-0601-0 · Cited by 25
“The adaptive threshold models can reproduce the interspike interval statistics (Chacron et al. 2000), f-I curve (Rauch et al. 2003; Kobayashi 2009), and spike timings (Jolivet et al. 2006, 2008) of a neuron recorded in experiments.”
BackgroundA leaky integrate-and-fire model with adaptation for the generation of a spike train
Buonocore, A., Caputo, L., Pirozzi, E., Carfora, M. F., & 2. Istituto per le Appplicazioni del Calcolo "Mauro Picone", C. N. D. R. (2016) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2016002 · Cited by 25
Spectrum of Lyapunov exponents of non-smooth dynamical systems of integrate-and-fire type
Zhou, D., Sun, Y., Rangan, A. V., & Cai, D. (2009) · Journal of Computational Neuroscience · doi:10.1007/s10827-009-0201-3 · Cited by 25
Feature Selection in Simple Neurons: How Coding Depends on Spiking Dynamics
Famulare, M., & Fairhall, A. (2009) · Neural Computation · doi:10.1162/neco.2009.02-09-956 · Cited by 25
“Both models have been successfully fit to neuronal data from a variety of neuron types (Izhikevich, 2004 ; Rauch, Camera, Luscher, Senn, & Fusi, 2003 ; Badel, Lefort, Berger, Petersen, Gerstner, & Richardson, 2008 ).”
MethodCellular-resolution optogenetics reveals attenuation-by-suppression in visual cortical neurons
LaFosse, P. K., Zhou, Z., O’Rawe, J. F., Friedman, N. G., Scott, V., Deng, Y., & Histed, M. H. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2318837121 · Cited by 33
Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware
Knight, J. C., Tully, P. J., Kaplan, B. A., Lansner, A., & Furber, S. B. (2016) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2016.00037 · Cited by 25
“We model excitatory and inhibitory cells as IAF neurons with exponentially decaying PSCs (Liu and Wang, 2001; Rauch et al., 2003).”
Population temporal structure supplements the rate code during sensorimotor transformations
Jagadisan, U. K., & Gandhi, N. J. (2022) · Current Biology · doi:10.1016/j.cub.2022.01.015 · Cited by 25
Inferring Network Dynamics and Neuron Properties from Population Recordings
Linaro, D., Storace, M., & Mattia, M. (2011) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2011.00043 · Cited by 24
“This“outof-stimulation” identification evidences the bona fide network dynamics, by avoiding any artifacts due to artificial inputs, like sinusoidal or randomly fluctuating intra- or extracellular electric field changes (Chance et al., 2002; Rauch et al., 2003; Giugliano et al., 2004; Higgs et al., 2006; Arsiero et al…”
BackgroundUnsupervised learning of event-based image recordings using spike-timing-dependent plasticity
Iyer, L., & Basu, A. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966074 · Cited by 25
“Neurons in the network are integrate and fire (IF) neurons [15], [16], [17], [18], [19], [20], [21], etc.”
BackgroundRomero-Sosa, J. L., Motanis, H., & Buonomano, D. V. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.2830-20.2021 · Cited by 24
A neuronal least-action principle for real-time learning in cortical circuits
Senn, W., Dold, D., Kungl, Á. F., Ellenberger, B., Jordan, J., Bengio, Y., Sacramento, J., & Petrovici, M. A. (2024) · eLife · doi:10.1101/2023.03.25.534198 · Cited by 25
Self-citationJolivet, R., & Wulfram Gerstner, B. (2004) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2005.09.010 · Cited by 22
“Nevertheless, these highly simplified models have been shown to yield good predictions when compared to biological data [16,28].”
BackgroundMethodUbiquity of collective irregular dynamics in balanced networks of spiking neurons
Ullner, E., Politi, A., & Torcini, A. (2018) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.5049902 · Cited by 21
Envelope gating and noise shaping in populations of noisy neurons
Middleton, J. W., Harvey‐Girard, E., Maler, L., & Longtin, A. (2007) · Physical Review E · doi:10.1103/physreve.75.021918 · Cited by 21
Frequency-dependent response properties of adapting spiking neurons
Gigante, G., Giudice, P. D., & Mattia, M. (2006) · Mathematical Biosciences · doi:10.1016/j.mbs.2006.11.010 · Cited by 21
Variability of the mesolimbic neuronal activity in a rat model of depression
Friedman, A., Dremencov, E., Crown, H., Levy, D. J., Mintz, M., Overstreet, D. H., & Yadid, G. (2005) · Neuroreport · doi:10.1097/00001756-200504040-00019 · Cited by 21
“The variance of ISIs has been previously studied in theoretical models of the neuronal networks [7], in rat cortical pyramidal cells [8,17,18], cerebellar Purkinje cells [19] and in the striatum [20].”
BackgroundBerthet, P., Lindahl, M., Tully, P. J., Kotaleski, J. H., & Lansner, A. (2016) · Frontiers in Neural Circuits · doi:10.3389/fncir.2016.00053 · Cited by 19
“The neuron model implemented is of the leaky integrate-andfire type with alpha function-shaped postsynaptic conductance (Meffin et al., 2004), which has been shown to be a useful model reduction of cortical neurons (Rauch et al., 2003).”
BackgroundMethodNaud, R., & Gerstner, W. (2012) · Springer eBooks · doi:10.1007/978-94-007-3858-4_6 · Cited by 19
“We also need a process to account for adaptation (Rauch et al, 2003; Jolivet et al, 2006b), and this is the topic of the next section.”
BackgroundComputational models as statistical tools
Durstewitz, D., Koppe, G., & Toutounji, H. (2016) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2016.07.004 · Cited by 19
Aggarwal, S., & Ray, S. (2023) · Cerebral Cortex Communications · doi:10.1093/texcom/tgad011 · Cited by 23
Recognition by Variance: Learning Rules for Spatiotemporal Patterns
Barak, O., & Tsodyks, M. V. (2006) · Neural Computation · doi:10.1162/neco.2006.18.10.2343 · Cited by 19
Bauermeister, C., Schwalger, T., Russell, D. F., NEIMAN, A. B., & Lindner, B. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003170 · Cited by 19
“[26,27]).”
The tuning of tuning: How adaptation influences single cell information transfer
Zeldenrust, F., Calcini, N., Xuan, Y., Bijlsma, A., & Celikel, T. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012043 · Cited by 19
“…shown that such adaptation is an essential part of the functioning of cortical neurons, and any model of cortical firing should include such adaptation (Brette and Gerstner, 2005; Fontaine et al., 2014; Gerstner and Naud, 2009; Jolivet et al., 2004; Rauch et al., 2003; Rossant et al., 2011).”
BackgroundFunctional Identification of Spike-Processing Neural Circuits
Lazar, A. A., & Slutskiy, Y. B. (2013) · Neural Computation · doi:10.1162/neco_a_00543 · Cited by 18
“Although it is an idealization of a biophysical spike generator, IAF-based models have been found to reliably predict spike trains of many real neurons, including neocortical pyramidal cells, retinal ganglion cells and lateral geniculate nucleus neurons in the visual pathway (Keat et al., 2001; Rauch et al., 2003; Jol…”
MethodThe neuron as a direct data-driven controller
Moore, J. J., Genkin, A., Tournoy, M., Pughe-Sanford, J. L., van Steveninck, R. R. D. R., & Chklovskii, D. B. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2311893121 · Cited by 19
Emergence of band-pass filtering through adaptive spiking in the owl's cochlear nucleus
Fontaine, B., MacLeod, K. M., Lubejko, S. T., Steinberg, L. J., Köppl, C., & Peña, J. L. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00132.2014 · Cited by 17
Anisotropic connectivity implements motion-based prediction in a spiking neural network
Kaplan, B. A., Lansner, A., Masson, G. S., & Perrinet, L. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00112 · Cited by 18
“The cell and synapse parameters have been chosen to be in a similar range as seen in experimental studies [see Table 3 in the study by Rauch et al. (2003)] to allow for comparison between future modeling and experimental studies.”
MethodStatistics of a leaky integrate-and-fire model of neurons driven by dichotomous noise
Mankin, R., & Lumi, N. (2016) · Physical review. E · doi:10.1103/physreve.93.052143 · Cited by 17
Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal Dynamics
Barranca, V. J., & Zhou, D. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01101 · Cited by 14
“, 2018), and experimental measurements of neuronal firing-activity also generally exhibit a similar linear dependence on input strength (Rauch et al., 2003; La Camera et al., 2006).”
BackgroundExact firing time statistics of neurons driven by discrete inhibitory noise
Olmi, S., Angulo‐García, D., Imparato, A., & Torcini, A. (2017) · Scientific Reports · doi:10.1038/s41598-017-01658-8 · Cited by 15
Zhou, D., Zhang, Y., Xiao, Y., & Cai, D. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00075 · Cited by 15
“Experimentally, it has been shown that models of I&F neurons are able to capture rather well the subthreshold dynamics of a real neuron in terms of linear response properties as well as statistical firing properties of a real neuron (Carandini et al., 1996; Rauch et al., 2003; Burkitt, 2006).”
MethodLeváková, M., Kostal, L., Monsempès, C., Lucas, P., & Kobayashi, R. (2019) · Journal of The Royal Society Interface · doi:10.1098/rsif.2019.0246 · Cited by 15
“Reduced neuronal models, such as the leaky integrateand-fire (LIF) neuron [19–21], can be good approximations of real neurons [22,23] and therefore useful tools for simulating and investigating prominent features of network dynamics [24,25].”
Characterizing the Firing Properties of an Adaptive Analog VLSI Neuron
Rubin, D. B. D., Chicca, E., & Indiveri, G. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27835-1_15 · Cited by 15
“The CVs are in accordance with theoretical [19] and experimental studies on neurons of layer 4 and 5 of the rat [14].”
BackgroundResultNetwork-induced chaos in integrate-and-fire neuronal ensembles
Zhou, D., Rangan, A. V., Sun, Y., & Cai, D. (2009) · Physical Review E · doi:10.1103/physreve.80.031918 · Cited by 15
Fast and deep: energy-efficient neuromorphic learning with first-spike timesPreprint
Göltz, J., Kriener, L., Baumbach, A., Billaudelle, S., Breitwieser, O., Cramer, B., Dold, D., Kungl, Á. F., Senn, W., Schemmel, J., et al. (2019) · arXiv (Cornell University) · Cited by 19
Self-citationCortical Representation of Touch in Silico
Huang, C., Zeldenrust, F., & Celikel, T. (2022) · Neuroinformatics · doi:10.1007/s12021-022-09576-5 · Cited by 15
“All these models are shown to have a high accuracy in reproducing cortical spike trains, as long as the slow adaptation parameter is included (Botella-Soler et al., 2018; Brette & Gerstner, 2005; Fontaine et al., 2014; Gerstner & Naud, 2009; Jolivet et al., 2004; Jones et al., 2015; Kobayashi et al., 2009; Naud & Gers…”
BackgroundLondon, M., Larkum, M. E., & Häusser, M. (2008) · Biological Cybernetics · doi:10.1007/s00422-008-0268-3 · Cited by 14
“Many successful sophisticated methods for constructing sim-plified models of spiking neurons are described in this issue, and in the literature (Brunel and Latham 2003; Fourcaud-Trocme et al. 2003; Rauch et al. 2003; Keren 2005; Jolivet et al. 2006; Badel et al. 2008).”
Seal Formation in Silicon Planar Patch-Clamp Microstructures
Curtis, J. C., Baldwin, K., Dworak, B. J., Stevenson, J., Delivopoulos, E., MacLeod, N. K., & Murray, A. F. (2008) · Journal of Microelectromechanical Systems · doi:10.1109/jmems.2008.924270 · Cited by 14
“activity-modeled noise [2].”
Equilibrium and response properties of the integrate-and-fire neuron in discrete time
Helias, M. (2009) · Frontiers in Computational Neuroscience · doi:10.3389/neuro.10.029.2009 · Cited by 17
“This model is effi cient to simulate and in certain regimes it is a good approximation of both more complicated neuron models (Izhikevich, 2004) and of real neurons (Rauch et al., 2003; La Camera et al., 2004; Jovilet et al., 2008).”
The impact of spike-frequency adaptation on balanced network dynamics
Barranca, V. J., Huang, H. P., & Li, S. (2018) · Cognitive Neurodynamics · doi:10.1007/s11571-018-9504-2 · Cited by 14
Neural Functional Connectivity Reconstruction with Second‐Order Memristor Network
Wu, Y., Moon, J., Zhu, X., & Lu, W. D. (2021) · Advanced Intelligent Systems · doi:10.1002/aisy.202000276 · Cited by 13
Comparison between an exact and a heuristic neural mass model with second-order synapses
Clusella, P., Ersöz, E. K., García‐Ojalvo, J., & Ruffini, G. (2022) · Biological Cybernetics · doi:10.1007/s00422-022-00952-7 · Cited by 17
“This reflects the continued increase of firing activity with increase input, which has been reported in experimental studies (Rauch et al. 2003).”
ResultFunctional Implications of Dale's Law in Balanced Neuronal Network Dynamics and Decision Making
Barranca, V. J., Bhuiyan, A., Sundgren, M., & Xing, F. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.801847 · Cited by 17
“Experimental recordings of neuronal activity often exhibit a linear increase in firing rate with external input strength (Rauch et al., 2003; La Camera et al., 2006), but here we see that adherence to Dale’s law is not in fact necessary to produce such a linear response.”
BackgroundDiagrammatic expansion of pulse-coupled network dynamics in terms of subnetworks
Rangan, A. V. (2009) · Physical Review E · doi:10.1103/physreve.80.036101 · Cited by 12
Kreeger, L. J., Arshed, A., & MacLeod, K. M. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00092.2012 · Cited by 12
Network structure and input integration in competing firing rate models for decision-making
Barranca, V. J., Huang, H. P., & Kawakita, G. (2019) · Journal of Computational Neuroscience · doi:10.1007/s10827-018-0708-6 · Cited by 12
The neuronal transfer function: contributions from voltage- and time-dependent mechanisms
Cook, E. P., Wilhelm, A. C., Guest, J. A., Liang, Y., Masse, N. Y., & Colbert, C. M. (2007) · Progress in brain research · doi:10.1016/s0079-6123(06)65001-2 · Cited by 13
Bifurcation Enhances Temporal Information Encoding in the Olfactory Periphery
Choi, K., Rosenbluth, W., Graf, I. R., Kadakia, N., & Emonet, T. (2024) · PRX Life · doi:10.1103/prxlife.2.043011 · Cited by 11
Finite Post Synaptic Potentials Cause a Fast Neuronal Response
Helias, M., Deger, M., Rotter, S., & Diesmann, M. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00019 · Cited by 11
“Despite its simplicity the model successfully reproduces prominent features of neuronal dynamics, for example it qualitatively captures the relation between injected current and firing rate (Rauch et al., 2003; La Camera et al., 2004).”
BackgroundA Cerebellum-Inspired Spiking Neural Model With Adapting Rate Neurons
Liu, Y., Liu, R., Wang, J., Chen, W., Wang, Y., & Sun, C. (2023) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2023.3237776 · Cited by 11
Errors in estimation of the input signal for integrate-and-fire neuronal models
Bibbona, E., Lánský, P., Sacerdote, L., & Sirovich, R. (2008) · Physical Review E · doi:10.1103/physreve.78.011918 · Cited by 11
A neuronal least-action principle for real-time learning in cortical circuitsPreprint
Senn, W., Dold, D., Kungl, Á. F., Ellenberger, B., Jordan, J., Bengio, Y., Sacramento, J., & Petrovici, M. A. (2023) · eLife · doi:10.7554/elife.89674 · Cited by 17
Self-citationSingle-cell optogenetics reveals attenuation-by-suppression in visual cortical neuronsPreprint
LaFosse, P. K., Zhou, Z., O’Rawe, J. F., Friedman, N. G., Scott, V. M., Deng, Y., & Histed, M. H. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.13.557650 · Cited by 11
“…affected by activity in the local recurrent network (Chance et al., 2002; Hansel and van Vreeswijk, 2002; Miller and Troyer, 2002; Priebe and Ferster, 2002; Agüera y Arcas and Fairhall, 2003; Rauch et al., 2003; Carandini, 2004; Ostojic and Brunel, 2011; Atallah et al., 2012; Haider et al., 2013).”
BackgroundNeural adaptation and fractional dynamics as a window to underlying neural excitability
Lundstrom, B. N., & Richner, T. J. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010527 · Cited by 10
Liu, T., Chua, Y., Ning, Y., Liu, P., Zhang, Y., Li, T., Wan, Z., Wan, Z., Chen, W., & Zhang, S. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106745 · Cited by 10
Cogno, S. G., & Mato, G. (2015) · Frontiers in Neural Circuits · doi:10.3389/fncir.2015.00042 · Cited by 10
“Moreover, in Rauch et al. (2003) it was FIGURE 9 | When the balanced state is unstable for the first Fourier mode, increasing functional connectivity in the inhibitory interactions leads to reduction of selectivity.”
Hamaguchi, K., Riehle, A., & Brunel, N. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00858.2009 · Cited by 10
Capone, C., & Mattia, M. (2017) · Scientific Reports · doi:10.1038/srep39611 · Cited by 10
“Indeed, the input-output gain function measured in biological neurons 21–23 , and accurately characterized by the sigmoidal function f LIF ( u ) (Fig.”
BackgroundRefractoriness Accounts for Variable Spike Burst Responses in Somatosensory Cortex
Teleńczuk, B., Kempter, R., Curio, G., & Alain Destexhe, A. (2017) · eNeuro · doi:10.1523/eneuro.0173-17.2017 · Cited by 10
“…two statistics, the model error was evaluated as the normalised differences between the simulated Xmodel and validations spike trains Xvalidate (cf. Rauch et al., 2003): where is either the PSTH or the spike pattern distribution of model (Xmodel) or validation (Xvalidate) set; N is the size of the…”
MethodTransient rhythmic network activity in the somatosensory cortex evoked by distributed input in vitro
Berger, T. K., Lüscher, H. R., & Giugliano, M. (2006) · Neuroscience · doi:10.1016/j.neuroscience.2006.03.003 · Cited by 10
Self-citationA Mathematical Approach to Self-Organized Criticality in Neural Networks
Levina, A. (2008) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-2496 · Cited by 9
Shomali, S. R., Rasuli, S. N., Ahmadabadi, M. N., & Shimazaki, H. (2023) · Communications Biology · doi:10.1038/s42003-023-04511-z · Cited by 8
“To make it more biologically plausible, we added an adaptation term to the LIF neuron model 69 – 71 and simulated its effect on the pairwise and triple-wise interactions (see Supplementary Table 2 and Supplementary Note 7).”
Modulation of Firing Rate by Background Synaptic Noise Statistics in Rat Visual Cortical Neurons
Sceniak, M. P., & Sabo, S. L. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00023.2010 · Cited by 9
Efficient digital neurons for large scale cortical architectures
Smith, J. E. (2014) · ACM SIGARCH Computer Architecture News · doi:10.1145/2678373.2665707 · Cited by 9
“Some modelers prefer to construct systems that include background “noise” which is added to a neuron model’s behavior [6][37].”
BackgroundPerfect Detection of Spikes in the Linear Sub-threshold Dynamics of Point Neurons
Krishnan, J., Mana, P. P., Helias, M., Diesmann, M., & Napoli, E. D. (2018) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00075 · Cited by 10
“Leaky integrate-and-fire models, despite their simplicity, approximate the behavior of real neurons with high accuracy (Rauch et al., 2003).”
BackgroundCompressive sensing reconstruction of feed-forward connectivity in pulse-coupled nonlinear networks
Barranca, V. J., Zhou, D., & Cai, D. (2016) · Physical review. E · doi:10.1103/physreve.93.060201 · Cited by 9
Analysis and Simulation of Gain Control and Precision in Crayfish Visual Interneurons
Glantz, R. M., & Schroeter, J. P. (2004) · Journal of Neurophysiology · doi:10.1152/jn.00448.2004 · Cited by 9
Noisy Softplus: an activation function that enables SNNs to be trained as ANNsPreprint
Liu, Q., Chen, Y., & Furber, S. B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.03609 · Cited by 16
“The response function [17,11] of the LIF neuron to a noisy current, also known as Siegert formula [18], is driven by the μ and σ:”
A nonlinear meccano for Alzheimer's emergence by amyloid β-mediated glutamatergic hyperactivity
Bonifazi, G., Luchena, C., Gaminde‐Blasco, A., Ortiz-Sanz, C., Capetillo‐Zarate, E., Matute, C., Alberdi, E. M., & Pittà, M. D. (2024) · Neurobiology of Disease · doi:10.1016/j.nbd.2024.106473 · Cited by 9
The Ordinal Serial Encoding Model: Serial Memory in Spiking Neurons
Choo, F.-X. (2010) · UWSpace (University of Waterloo) · Cited by 8
Learning in large-scale spiking neural networks
Bekolay, T. (2011) · UWSpace (University of Waterloo) · Cited by 7
Efficient digital neurons for large scale cortical architectures
Smith, J. E. (2014) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca.2014.6853206 · Cited by 7
Non-monotonic Current-to-Rate Response Function in a Novel Integrate-and-Fire Model Neuron
Giugliano, M., Camera, G. L., Rauch, A., Lüscher, H., & Fusi, S. (2002) · Lecture notes in computer science · doi:10.1007/3-540-46084-5_24 · Cited by 7
“Below the excitability threshold θ, the behavior of the novel IMF neuron schematically resembles a linear integrator of the afferent current I(t), as for the linear integrate-and-fire model (LIF) [6, 1].”
BackgroundInfluentialSelf-citationIntroducing numerical bounds to improve event-based neural network simulationPreprint
Cessac, B., Rochel, O., & Viéville, T. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0810.3992 · Cited by 7
Shomali, S. R., Ahmadabadi, M. N., Shimazaki, H., & Rasuli, S. N. (2017) · Journal of Computational Neuroscience · doi:10.1007/s10827-017-0664-6 · Cited by 5
“…neural properties (Izhike-vich 2004; Ostojic and Brunel 2011), the LIF neuron model can capture the properties of the cortical pyramidal neurons (Rauch et al. 2003; La Camera et al. 2004; Jolivet et al. 2008); it is still the widely studied neuron model because of its simplicity to be driven…”
BackgroundFrom LIF to AdEx neuron models: Accelerated analog 65 nm CMOS implementation
Aamir, S. A., Müller, P. F., Kriener, L., Kiene, G., Schemmel, J., & Meier, K. (2017) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2017.8325167 · Cited by 6
“However, when adaptation and refractoriness are included, LIF based neurons can very well predict the spike times of biological neurons [10], [12], [13].”
BackgroundBarranca, V. J. (2022) · Journal of Computational Neuroscience · doi:10.1007/s10827-022-00831-x · Cited by 6
“The I&F model has been shown to well reproduce both subthreshold membrane potential dynamics as well as firing statistics measured in experiment and has been utilized in a host of theoretical investigations of brain network computations (Mather et al. 2009; Rauch et al. 2003; Barranca et al. 2014a; Rangan and Cai 2006…”
BackgroundMethodDendritic excitability controls overdispersion
Friedenberger, Z., & Naud, R. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00580-6 · Cited by 7
“Yet, in this view of the input–output function there remains an assumption, one based on the predominance of evidence about the integrative properties of the cell body 9,10 .”
Balanced Active Core in Heterogeneous Neuronal Networks
Gu, Q.-L. L., Li, S., Dai, W., Zhou, D., & Cai, D. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2018.00109 · Cited by 6
“As an extension to the binary neuron model, we resort to the I&F model in our simulation (Carandini et al., 1996; Rauch et al., 2003; Cai et al., 2005; Rangan et al., 2005; Zhou et al., 2009, 2013).”
MethodMembrane potential and spike train statistics depend distinctly on input statistics
Rosenbaum, R., & Josić, K. (2011) · Physical Review E · doi:10.1103/physreve.84.051902 · Cited by 5
On Firing Rate Estimation for Dependent Interspike Intervals
Benedetto, E., Polito, F., & Sacerdote, L. (2015) · Neural Computation · doi:10.1162/neco_a_00709 · Cited by 5
“This data were recorded and discussed by Rauch et al. (2003) and correspond to single electrode in vitro recordings from rat neocortical pyramidal cells under random in vivo- 1http://lcn.epfl.ch/∼ gerstner/QuantNeuronMod2007/ like current injection.”
BackgroundMethodUrdapilleta, E. (2016) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/115/68002 · Cited by 5
Urdapilleta, E. (2016) · Americanae (AECID Library) · Cited by 5
A constrained linear approach to identify a multi-timescale adaptive threshold neuronal model
Jabalameli, A., & Behal, A. (2015) · IEEE International Conference on Computational Advances in Bio and Medical Sciences · doi:10.1109/iccabs.2015.7344704 · Cited by 4
“The details of the experimental protocol are available in [20] and [21].”
MethodSpike-Triggered Regression for Synaptic Connectivity Reconstruction in Neuronal Networks
Zhang, Y., Xiao, Y., Zhou, D., & Cai, D. (2017) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2017.00101 · Cited by 4
“Experimentally, it has been shown that the I&F models can capture linear subthreshold properties as well as firing statistics of a real neuron (Carandini et al., 1996; Rauch et al., 2003; Burkitt, 2006).”
BackgroundThe extended Granger causality analysis for Hodgkin–Huxley neuronal models
Cheng, H., Cai, D., & Zhou, D. (2020) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0006349 · Cited by 4
Barkdoll, K., Lu, Y., & Barranca, V. J. (2023) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2023.1137015 · Cited by 5
“Though relatively idealized, the I&F model has been shown to well replicate experimentally observed subthreshold voltage dynamics and has been utilized to great effect in numerous large-scale model-based studies (Abbott, 1999; Rauch et al., 2003; Burkitt, 2006; Rangan and Cai, 2006; Mather et al., 2009; Barranca et al…”
MethodFiring Rate Adaptation without Losing Sensitivity to Input Fluctuations
Camera, G. L., Rauch, A., Senn, W., Lüscher, H., & Fusi, S. (2002) · Lecture notes in computer science · doi:10.1007/3-540-46084-5_30 · Cited by 4
“If τ ′ is very short, the correlation length of the current becomes negligible, and the current can be replaced by white noise [7, 5]:”
BackgroundMethodInfluentialSelf-citationPressley, J., & Troyer, T. W. (2011) · Neural Computation · doi:10.1162/neco_a_00114 · Cited by 4
When Less Is More: Non-monotonic Spike Sequence Processing in Neurons
Arnoldt, H., Chang, S., Jahnke, S., Urmersbach, B., Taschenberger, H., & Timme, M. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004002 · Cited by 4
“For instance, spike frequency adaptation, one specific form of resource limitation, may turn a neuron into a high pass filter with a response function that is modified compared to neurons without adaptation but still monotonic [40, 41].”
BackgroundSpatio-Temporal Dynamics of Pattern Formation in the Cerebral Cortex
Huang, M. (2009) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-3248 · Cited by 3
Metadata
- 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
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