The Impact of Input Fluctuations on the Frequency–Current Relationships of Layer 5 Pyramidal Neurons in the Rat Medial Prefrontal Cortex

Arsiero, Maura; Lüscher, Hans‐Rudolf; Lundstrom, Brian Nils; Giugliano, Michèle

doi:10.1523/jneurosci.4937-06.2007

Cited by 73 publications

(120 citation statements)

References 73 publications

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“…We also hope that recent observations, like gain modulation by distal dendritic inputs or the divergence of the response functions in prefrontal cortex neurons (Arsiero et al 2007), can open the door for new quantitative models and their application to analysis of network behavior. While such an interaction between theory and experiment is a widely consolidated tradition in physics, it is becoming only slowly established in neuroscience.…”

Section: Discussionmentioning

confidence: 93%

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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Section: Discussionmentioning

confidence: 93%

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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“…For example, studies have shown that the "steady-state" (low-frequency) gain of neurons depends on the SD of stimulus noise (Chance et al, 2002;Longtin et al, 2002;Fellous et al, 2003;Shu et al, 2003;Higgs et al, 2006;Arsiero et al, 2007). Here, we investigated how frequencydependent gain depends on the stimulus power spectrum, measuring G( f ) in response to 1 ms exponential-filtered noise and 1/f noise, in addition to the 5 ms exponential-filtered noise described above.…”

Section: Frequency Response For Different Stimulus Waveformsmentioning

confidence: 99%

Conditional Bursting Enhances Resonant Firing in Neocortical Layer 2–3 Pyramidal Neurons

Higgs¹,

Spain²

2009

J. Neurosci.

156

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The frequency response properties of neurons are critical for signal transmission and control of network oscillations. At subthreshold membrane potential, some neurons show resonance caused by voltage-gated channels. During action potential firing, resonance of the spike output may arise from subthreshold mechanisms and/or spike-dependent currents that cause afterhyperpolarizations (AHPs) and afterdepolarizations (ADPs). Layer 2-3 pyramidal neurons (L2-3 PNs) have a fast ADP that can trigger bursts. The present study investigated what stimuli elicit bursting in these cells and whether bursts transmit specific frequency components of the synaptic input, leading to resonance at particular frequencies. We found that two-spike bursts are triggered by step onsets, sine waves in two frequency bands, and noise. Using noise adjusted to elicit firing at ϳ10 Hz, we measured the gain for modulation of the time-varying firing rate as a function of stimulus frequency, finding a primary peak (7-16 Hz) and a high-frequency resonance (250 -450 Hz). Gain was also measured separately for single and burst spikes. For a given spike rate, bursts provided higher gain at the primary peak and lower gain at intermediate frequencies, sharpening the high-frequency resonance. Suppression of bursting using automated current feedback weakened the primary and high-frequency resonances. The primary resonance was also influenced by the SK channel-mediated medium AHP (mAHP), because the SK blocker apamin reduced the sharpness of the primary peak. Our results suggest that resonance in L2-3 PNs depends on burst firing and the mAHP. Bursting enhances resonance in two distinct frequency bands.

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“…Recently, Higgs et al found that intrinsic mechanisms regulate how background noise affects the gain of responses to a parametrically varying mean DC signal (Higgs et al, 2006). Neurons can also modulate their sensitivity to fluctuations (i.e., noise) over a range of changes in stimulus mean (i.e., signal) (Arsiero et al, 2007). Our present findings show that intrinsic mechanisms can modulate neuronal gain under changes in the distribution of sensory inputs, when signal and noise are not identified a priori.…”

Section: Intrinsic Gain Regulationmentioning

confidence: 99%

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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The Impact of Input Fluctuations on the Frequency–Current Relationships of Layer 5 Pyramidal Neurons in the Rat Medial Prefrontal Cortex

Cited by 73 publications

References 73 publications

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

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

Conditional Bursting Enhances Resonant Firing in Neocortical Layer 2–3 Pyramidal Neurons

Intrinsic Mechanisms for Adaptive Gain Rescaling in Barrel Cortex

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