The DOI matches its Crossref record; 58 of the 59 references it cites were found in a registry or an index.
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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Artiñano, T. G., Andalibi, V., Atula, I., Maestri, M., & Vanni, S. (2023) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2023.1011814 · Cited by 2
“LIF model has a limited ability to replicate such membrane voltage traces, whereas relatively simple adaptive models fit considerably better (Rauch et al., 2003; Jolivet et al., 2004; Brette and Gerstner, 2005; Gerstner and Naud, 2009).”
BackgroundFiring proprieties of an adaptive analog VLSI neuron
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“More-over, it has been shown to reproduce experimental spike trains of pyramidal cells [39], and many of its statistical properties are available [26, 27, 40, 41].”
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“Evidence of it is the extended use of integrate and fire network computational models centered on the suprathreshold properties of neurons [1], for a review see [2,3].”
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Self-citationAspects of information processing by individual neurons and populations of neurons
Hoch, T. (2007) · DepositOnce · doi:10.14279/depositonce-1597
“If simulated with an adequate model of synaptic activity, the LIF is able to reproduce some aspects of neural activity (e.g. irregularity of neuronal firing) reasonable well (Rauch et al., 2003).”
BackgroundMethodInfluentialSwift Two-sample Test on High-dimensional Neural Spiking DataPreprint
Xu, Z. J., Zhou, D., & Cai, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.12314
“It has been shown in experiment that I&F models can statistically faithfully capture the response of cortical neurons under in-vivo-like currents in terms of both firing dynamics and subthreshold membrane dynamics [31].”
Uncovering Network Architecture Using an Exact Statistical Input-Output Relation of a Neuron ModelPreprint
Shomali, S. R., Rasuli, S. N., Ahmadabadi, M. N., & Shimazaki, H. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/479956
“The LIF neuron model is a good reduced model, that can reproduce in vivo spiking activity of neurons (Camera et al., 2004; Rauch et al., 2003).”
BackgroundStimulus competition by inhibitory interferencePreprint
Tiesinga, P. H. E. (2004) · arXiv (Cornell University) · doi:10.48550/arxiv.q-bio/0410019
“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…”
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A Spiking Neural Network Model for Category LearningPreprint
Iyer, L., & Basu, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.23.916593
“Ψ(C)− {i|μ(i) = 1 in concept C} (3) The feature layer is modeled by integrate and fire (IF) neurons [29, 30, 31, 32, 33, 34, 35], etc.”
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Analysis and extension of exact mean-field theory with dynamic synaptic currentsPreprint
Ruffini, G. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.01.458563
“The system of equations becomes — first the firing rate equations, φ̇1 = ∆1/π + 2φ1v1 [33] v̇1 = v2 1 + Γ12s2 + Γ11s1 + η̄1 + I − π(2)φ(2)1 [34] φ̇2 = ∆2/π + 2φ2v2 [35] v̇2 = v2 2 + Γ21s1 + Γ22s2 + η̄2 − π(2)φ(2)2 [36] and the synapse equations, assuming the type of synapse is fixed for each source neuron, ṡ1 = z1 [37…”
Neural Networks with Nonlinear Couplings
Jahnke, S. (2015) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-5056
Modelling Instantaneous Firing Rate of DBS Target Neuronal Ensembles in Basal Ganglia and ThalamusPreprint
Tian, Y., Murphy, M. J. H., Steiner, L. A., Kalia, S. K., Hodaie, M., Lozano, A. M., Hutchison, W. D., Popović, M. R., Milosevic, L., & Lankarany, M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.28.497834
“The firing rate and I&'$ were ordered according to their percentiles (from 0 to 100), and the percentiles were matched at each point in the curve to qualitatively represent the relationship between the firing rate and input current [46][47].”
MethodBrunel, N., & Hakim, V. (2022) · Springer eBooks · doi:10.1007/978-1-0716-1006-0_74
Chen, M., Feng, D., & Su, H. (2022) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2022.09.001
Aggarwal, S., & Ray, S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.15.528644
The nonlinear meccano of hyperactivity in AlzheimerPreprint
Bonifazi, G., Luchena, C., Gaminde-Blasco, A., Ortiz-Sanz, C., Capetillo-Zárate, E., Matute, C., Alberdi, E., & Pittà, M. D. (2023) · bioRxiv · doi:10.1101/2023.10.09.561541
“The fact that neurons can only fire when depolarized beyond a firing threshold by excitatory (glutamatergic) synapses (Rauch et al., 2003) implies that the feedback of extracellular glutamate on neuronal depolarization, and thus firing, and downstream synaptic release, kicks in only around and…”
Buendía, V., Biggiogera, J., & Sanzeni, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.18.624135
“Single-neuron responses were modeled using a validated approximation of the input–output function of leaky integrate-and-fire neurons driven by Gaussian noise [56–58, Considering both single-neuron and network properties, the model contained eight free parameters.”
Biophysical and circuit properties underlying population dynamics in neocortical networks
Zerlaut, Y. (2016) · theses.fr (ABES) · doi:10.70675/65c9fe21zd042z4128zbe2bz5b4f3d52fb33
“To obtain a meaningful estimation of the firing rate response we need long and stable recordings (Rauch et al., 2003; Köndgen et al., 2008).”
From dynamics to computations in recurrent neural networks
Mastrogiuseppe, F. (2017) · theses.fr (ABES) · doi:10.70675/8534b1a4z7c95z45efz9de8z52f51f68a178
Modeling of neuron-astrocyte interaction : application to signal and image processing
Lorenzo, J. (2022) · theses.fr (ABES) · doi:10.70675/9e7d026ezff73z4f89z8b1azef7f2e29fb41
Hikino, K. N., Nakayama, M., Wu, Y., & Barranca, V. J. (2026) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2026.1793265
“…maintaining computational feasibility, the I&F model is commonly used in large-scale neuronal network models and includes the key nonlinear features of neuronal voltage dynamics (Mather et al., 2009; Rauch et al., 2003; Barranca et al., 2014a; Rangan and Cai, 2006; Burkitt, 2006; Abbott, 1999).”
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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