Dynamics of the Instantaneous Firing Rate in Response to Changes in Input Statistics

Fourcaud-Trocmé, Nicolas; Brunel, Nadège

doi:10.1007/s10827-005-0337-8

Cited by 60 publications

(65 citation statements)

References 32 publications

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“…5A) as well as in variance. This agrees with suggestions from previous experiments and with predictions from modeling studies (Silberberg et al, 2004;Fourcaud-Trocme and Brunel, 2005).…”

Section: Association Between Adaptation and Gain Rescalingsupporting

confidence: 93%

Intrinsic Mechanisms for Adaptive Gain Rescaling in Barrel Cortex

Díaz-Quesada¹,

Maravall²

2008

J. Neurosci.

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Barrel cortex neuronal responses adapt to changes in the statistics of complex whisker stimuli. This form of adaptation involves an adjustment in the input-output tuning functions of the neurons, such that their gain rescales depending on the range of the current stimulus distribution. Similar phenomena have been observed in other sensory systems, suggesting that adaptive adjustment of responses to ongoing stimulus statistics is an important principle of sensory function. In other systems, adaptation and gain rescaling can depend on intrinsic properties; however, in barrel cortex, whether intrinsic mechanisms can contribute to adaptation to stimulus statistics is unknown. To examine this, we performed whole-cell patch-clamp recordings of pyramidal cells in acute slices while injecting stochastic current stimuli. We induced changes in statistical context by switching across stimulus distributions. The firing rates of neurons adapted in response to changes in stimulus statistics. Adaptation depended on the form of the changes in stimulus distribution: in vivo-like adaptation occurred only for rectified stimuli that maintained neurons in a persistent state of net depolarization. Under these conditions, neurons rescaled the gain of their input-output functions according to the scale of the stimulus distribution, as observed in vivo. This stimulus-specific adaptation was caused by intrinsic properties and correlated strongly with the amplitude of calciumdependent slow afterhyperpolarizations. Our results suggest that widely expressed intrinsic mechanisms participate in barrel cortex adaptation but that their recruitment is highly stimulus specific.

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“…5A) as well as in variance. This agrees with suggestions from previous experiments and with predictions from modeling studies (Silberberg et al, 2004;Fourcaud-Trocme and Brunel, 2005).…”

Section: Association Between Adaptation and Gain Rescalingsupporting

confidence: 93%

Intrinsic Mechanisms for Adaptive Gain Rescaling in Barrel Cortex

Díaz-Quesada¹,

Maravall²

2008

J. Neurosci.

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“…6c, d). This behavior is consistent with a nonlinear IF model with a very sharp spike, and a nonlinearity which is intermediate between exponential and quadratic (Fourcaud-Trocmé and Brunel 2005). …”

Section: Response To Sinusoidal Inputs In the Presence Of Fluctuationssupporting

confidence: 85%

The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs

et al. 2008

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The response of a population of neurons to time-varying synaptic inputs can show a rich phenomenology, hardly predictable from the dynamical properties of the membrane's inherent time constants. For example, a network of neurons in a state of spontaneous activity can respond significantly more rapidly than each single neuron taken individually. Under the assumption that the statistics of the synaptic input is the same for a population of similarly behaving neurons (mean field approximation), it is possible to greatly simplify the study of neural circuits, both in the case in which the statistics of the input are stationary (reviewed in La Camera et al. in Biol Cybern, 2008) and in the case in which they are time varying and unevenly distributed over the dendritic tree. Here, we review theoretical and experimental results on the single-neuron properties that are relevant for the dynamical collective behavior of a population of neurons. We focus on the response of integrate-and-fire neurons and real cortical neurons to long-lasting, noisy, in vivo-like stationary inputs and show how the theory can predict the observed rhythmic activity of cultures of neurons. We then show how cortical neurons adapt on multiple time scales in response to input with stationary statistics in vitro. Next, we review how it is possible to study the general response properties of a neural circuit to time-varying inputs by estimating the response of single neurons to noisy sinusoidal currents. Finally, we address the dendrite-soma interactions in cortical neurons leading to gain modulation and spike bursts, and show how these effects can be captured by a two-compartment integrate-and-fire neuron. Most of the experimental results reviewed in this article have been successfully reproduced by simple integrate-and-fire model neurons.

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“…An alternative proposal for the nonlinearity is the EIF model (Fourcaud-Trocmé et al 2003) that includes the activation of the spike-generating sodium current. It has been shown (Fourcaud-Trocmé and Brunel 2005) that the functional form used to model the spike-initiation is a crucial determinant of the neuronal response to rapid synaptic signaling.…”

Section: One-variable Neuron Modelsmentioning

confidence: 99%

DynamicI-VCurves Are Reliable Predictors of Naturalistic Pyramidal-Neuron Voltage Traces

Badel¹,

Lefort²,

Brette³

et al. 2008

Journal of Neurophysiology

197

293

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Badel L, Lefort S, Brette R, Petersen CC, Gerstner W, Richardson MJ. Dynamic I-V curves are reliable predictors of naturalistic pyramidal-neuron voltage traces. J Neurophysiol 99: 656 -666, 2008. First published December 5, 2007 doi:10.1152/jn.01107.2007. Neuronal response properties are typically probed by intracellular measurements of current-voltage (I-V) relationships during application of current or voltage steps. Here we demonstrate the measurement of a novel I-V curve measured while the neuron exhibits a fluctuating voltage and emits spikes. This dynamic I-V curve requires only a few tens of seconds of experimental time and so lends itself readily to the rapid classification of cell type, quantification of heterogeneities in cell populations, and generation of reduced analytical models. We apply this technique to layer-5 pyramidal cells and show that their dynamic I-V curve comprises linear and exponential components, providing experimental evidence for a recently proposed theoretical model. The approach also allows us to determine the change of neuronal response properties after a spike, millisecond by millisecond, so that postspike refractoriness of pyramidal cells can be quantified. Observations of I-V curves during and in absence of refractoriness are cast into a model that is used to predict both the subthreshold response and spiking activity of the neuron to novel stimuli. The predictions of the resulting model are in excellent agreement with experimental data and close to the intrinsic neuronal reproducibility to repeated stimuli. I N T R O D U C T I O NAccurate models of electrically active cells and their interactions are central requirements for the understanding of the computational processes taking place in nervous tissue. The construction of network models, even at the level of cortical columns, requires the identification of cell classes and the quantification of both their typical behavior and the heterogeneities within a population. The volume of data that is required for this tissue-level modeling demands a high-throughput approach in which response properties can be routinely measured.Electrophysiology provides an array of techniques for the extraction of neuronal response properties. Standard methods involve probing the response to step-change stimuli leading to current-voltage (I-V) curves for the steady-state or instantaneous response. Used systematically with pharmacology, they can yield a full conductance-based description Koch 1999) although the time required is prohibitive for routine neuron-by-neuron classification. More recently, an elegant optimization method (Huys et al. 2006) has been proposed that promises to significantly facilitate the construction of biophysically detailed models, given some prior knowledge of the kinetics of the channels present.Detailed models, comprising hundreds of compartments, are important for understanding the biophysical properties probed during electrophysiological and pharmacological manipulations. Such models can be used for network simulatio...

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Dynamics of the Instantaneous Firing Rate in Response to Changes in Input Statistics

Cited by 60 publications

References 32 publications

Intrinsic Mechanisms for Adaptive Gain Rescaling in Barrel Cortex

Intrinsic Mechanisms for Adaptive Gain Rescaling in Barrel Cortex

The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs

DynamicI-VCurves Are Reliable Predictors of Naturalistic Pyramidal-Neuron Voltage Traces

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