High-Resolution Intracellular Recordings Using a Real-Time Computational Model of the Electrode

Brette, Romain; Piwkowska, Zuzanna; Monier, Cyril; Rudolph-Lilith, Michelle; Fournier, Julien; Levy, Manuel; Frégnac, Yves; Bal, Thierry; Destexhe, Alain

doi:10.1016/j.neuron.2008.06.021

Cited by 64 publications

(88 citation statements)

References 43 publications

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“…As in [51,52], this tail is interpreted as the membrane filter K (i) m . To improve the accuracy, this procedure was repeated 15 times by resampling experimental data from the available subthreshold segments.…”

Section: Methodsmentioning

confidence: 99%

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Temporal whitening by power-law adaptation in neocortical neurons

et al. 2013

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Neural signaling requires a large amount of metabolic energy 1 . Consequently, neurons are thought to communicate using efficient codes in which redundant information is discarded 2 . Theories of efficient coding 3 successfully predict several features of sensory systems. At early stages of visual processing, inputs coming from the external world are decorrelated in both space and time [4][5][6][7] ; through sensory adaptation 8 , codes are dynamically modified so as to maximize information transmission [9][10][11][12] ; and sensory adaptation on multiple timescales 11,13,14 could possibly reflect the statistics of the external world 15 .Sensory adaptation is at least partially a result of intrinsic properties of individual neurons and, in particular, of SFA. SFA is not only observed at the early stages of sensory processing, but is also widespread in cortical neurons embedded in highly recurrent networks. Often modeled by a single process with one specific timescale 16,17 , SFA also occurs on multiple timescales [18][19][20] . In pyramidal neurons of the rat somatosensory cortex, three or more processing steps away from sensory receptors, SFA is scale free 21 , meaning that the effective speed at which individual neurons adapt is not fixed but depends on the input. Scale-free adaptation can be captured by simple threshold models with a power law-decaying spike-triggered process 22 that possibly describes the combined action of Na + -channel inactivation [23][24][25] and ion channels mediating adaptation currents [26][27][28] .Thus, three questions arise. First, can the temporal features of spiketriggered currents and spike-triggered changes in firing threshold, possibly spanning multiple timescales, be directly extracted from experimental data? Second, can SFA be explained by these spiketriggered effects? And finally, do the timescales of SFA match the temporal statistics of the inputs received by individual neurons? If temporal characteristics of inputs and SFA were matched, SFA could lead to a perfect decorrelation of the information contained in one spike with that of the previous one of the same neuron, a phenomenon known as temporal whitening 29 . Temporal whitening in turn implies that, at a high signal-to-noise ratio (SNR), information transmission is enhanced 30 . RESULTSThe question of whether SFA is optimally designed for efficient coding can only be addressed if both the dynamics of SFA and the statistical properties of the inputs generated in biologically relevant situations are known. We used a combined theoretical and experimental approach to extract the dynamics of spike-triggered processes and SFA directly from in vitro recordings of cortical neurons. We then analyzed the synaptically driven membrane potential dynamics recorded in vivo from somatosensory neurons during active whisker sensation (data from ref. 31). Our overall goal was to study whether adaptation optimally removes the temporal correlations in the input to single neurons embedded in the highly recurrent network of the cortex.SFA i...

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Section: Methodsmentioning

confidence: 99%

“…To obtain an accurate estimate of the membrane potential, the recorded voltage was preprocessed with Active Electrode Compensation (AEC) [51] following the procedure described in ref. [52].…”

Section: Supplementary Data Preprocessingmentioning

confidence: 99%

Temporal whitening by power-law adaptation in neocortical neurons

et al. 2013

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“…Based on this and a possible reduction of bursting, we expect that the primary resonance of L2-3 PNs will be smaller in high-conductance states. Experiments to investigate this will require recently developed techniques for dynamic clamp using high-resistance electrodes (Brette et al, 2008), because we find that burst firing in L2-3 PNs is greatly weakened and AHPs are reduced by washout during whole-cell recording.…”

Section: Remaining Questions About Natural Inputmentioning

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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“…Whereas one does not know the optimal τ a priori, one can obtain a semi-optimal solution by setting τ to be ten milliseconds. Furthermore, in many experiments with real biological cells, it is nearly impossible to stimulate with pure white noise, because measurement systems have a low-pass filter characteristic [21]. By using our method, one can cancel out the low-pass filter effect of the measurement systems.…”

Section: Discussionmentioning

confidence: 99%

Optimal Colored Noise for Estimating Phase Response Curves

2015

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The phase response curve (PRC) is an important measure representing the interaction between oscillatory elements. To understand synchrony in biological systems, many research groups have sought to measure PRCs directly from biological cells including neurons. Ermentrout et al. andOta et al. showed that PRCs can be identified through measurement of white-noise spike-triggered averages. The disadvantage of this method is that one has to collect more than ten-thousand spikes to ensure the accuracy of the estimate. In this paper, to achieve a more accurate estimation of PRCs with a limited sample size, we use colored noise, which has recently drawn attention because of its unique effect on dynamical systems. We numerically show that there is an optimal colored noise to estimate PRCs in the most rigorous fashion.

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High-Resolution Intracellular Recordings Using a Real-Time Computational Model of the Electrode

Cited by 64 publications

References 43 publications

Temporal whitening by power-law adaptation in neocortical neurons

Temporal whitening by power-law adaptation in neocortical neurons

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

Optimal Colored Noise for Estimating Phase Response Curves

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