Journal of Neuroscience Methods
Volume 169, Issue 2, 30 April 2008, Pages 374-390
Methods for Computational Neuroscience
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Measurement of variability dynamics in cortical spike trains
Martin P. Nawrota, b, 1, Clemens Boucseina, b, Victor Rodriguez Molinab, 2, Alexa Riehlec, Ad Aertsena, b and Stefan Rottera, d, ,
aBernstein Center for Computational Neuroscience Freiburg, Germany
bNeurobiology & Biophysics, Faculty of Biology, University of Freiburg, Germany
cInstitut de Neurosciences Cognitives de la Méditerranée – CNRS & Université Aix-Marseille 2, Marseille, France
dTheory & Data Analysis, Institute for Frontier Areas of Psychology and Mental Health, Freiburg, Germany
Received 7 May 2007; revised 5 October 2007; accepted 13 October 2007. Available online 30 October 2007.
We propose a method for the time-resolved joint analysis of two related aspects of single neuron variability, the spiking irregularity measured by the squared coefficient of variation (CV2) of the ISIs and the trial-by-trial variability of the spike count measured by the Fano factor (FF). We provide a calibration of both estimators using the theory of renewal processes, and verify it for spike trains recorded in vitro. Both estimators exhibit a considerable bias for short observations that count less than about 5–10 spikes on average. The practical difficulty of measuring the CV2 in rate modulated data can be overcome by a simple procedure of spike train demodulation which was tested in numerical simulations and in real spike trains. We propose to test neuronal spike trains for deviations from the null-hypothesis FF=CV2. We show that cortical pyramidal neurons, recorded under controlled stationary input conditions in vitro, comply with this assumption. Performing a time-resolved joint analysis of CV2 and FF of a single unit recording from the motor cortex of a behaving monkey we demonstrate how the dynamic change of their quantitative relation can be interpreted with respect to neuron intrinsic and extrinsic factors that influence cortical variability in vivo. Finally, we discuss the effect of several additional factors such as serial interval correlation and refractory period on the empiric relation of FF and CV2.
Keywords: Coefficient of variation; Cortical variability; Gamma process; Fano factor; Monkey motor cortex; Noise current injection; Renewal process, Spiking irregularity
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Fig. 1. Transformation of time. A renewal process of unit rate is simulated in operational time (vertical panel). A spike event at time t′ is translated into a spike event in real time t by the time transformation (center panel, Eq. (2)) given by the integral of the rate function (top panel). Conversely, a rate modulated realization of a point process may be demodulated by mapping an event in real time t onto the corresponding event in operational time t′.
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Fig. 2. Noise current injection. (a) Excitatory (top) and inhibitory (bottom) presynaptic events were generated as independent Poisson processes. Each event contributed a single EPSC or IPSC to the total current (black curve), carrying a net charge of 0.09 and −0.18 pQ, respectively. (b) Membrane potential of a layer 5 pyramidal cell (top) measured during the injection of a fluctuating current (bottom) that replaced excitatory and inhibitory synaptic input.
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Fig. 3. Inter-spike interval distribution for finite observation time. Analytical and simulated distributions for a gamma-process (left) and empirical distributions from a layer 5 pyramidal neuron recorded in vitro (right). The light gray histogram in the left panel displays the probability density f(x) constructed from 105 intervals randomly drawn from a gamma distribution (α=2.8). The dark gray histogram shows the modified distribution for a finite observation interval of length 1.5 in operational time. Solid lines show the corresponding analytical results (Eqs. (3) and (6)). The gamma order of 2.8 was estimated as α=1/CV2 from the full distribution in vitro (light gray histogram, right), constructed from 4181 intervals recorded during 1150 s. Short observations of length 415 ms (1.5 times the mean interval length) yielded the dark gray in vitro histogram.
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Fig. 4. Dependence of interval statistics on observation length. All times are specified in operational time. For gamma processes (left), all three statistical measures of mean interval μ (top), variance of intervals σ2 (middle), and squared coefficient of variation CV2 (bottom) show a monotonic dependence on the width T′ of the analysis window. Shown are the analytic (gray lines) and simulation (thin black lines) results for gamma processes of order α=0.25, 0.5, 1, 2, 4. In the right panels we show the empiric results from five pyramidal neurons that were stimulated with mixed excitatory/inhibitory shotnoise currents. The more irregular the process is, the more slowly the CV2 approaches the asymptotic limit of 1/α for T′→∞. The Poisson process (dashed gray line) saturates at about T′=10. For stationary input the cortical neurons in vitro typically exhibited a more regular spiking than a Poisson process. The curves saturate for about 5–7 units of operational time.
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Fig. 5. Dependence of count statistics on observation length. (Left) The gray lines indicate the analytic solutions for the gamma process (α=0.25, 0.5, 1, 2, 4, top to bottom), the thin black lines show the corresponding simulation results. The variance of the Poisson process (gray dashed line) is equal to the expected mean count corresponding to the operational time on the abscissa Eq. (2). The Fano factor of the Poisson process is equal to 1, irrespective of the observation length T′. For short observation intervals (T′→0), the estimation tends to unity, irrespective of α. For large T′, the Fano factor monotonically approaches the asymptotic value of 1/α, saturating for all gamma orders at about T′=10. (Right) Calibration for the same five recordings as presented in Fig. 4. The count variance of the neuronal process exhibits a behavior that is very similar to the more regular point processes with gamma orders α≥2.
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Fig. 6. Variance of estimation. The calibration is based on simulated ensembles of gamma processes. The standard deviation of estimating CV2 (top) and FF (bottom) is approximately inversely proportional to the gamma order α. Increasing the observation length T′ increases the average number of ISIs and, therefore, decreases the standard deviation of the CV2 estimator (top left), but does not influence the reliability of the FF estimator (bottom left). With an increasing number of trials N (right panels), the standard deviation decreases as for both estimators. The estimation error is generally smaller for the CV2 (top) than for the FF (bottom). In the left panels we used a fixed N=100, in the right panels we used a fixed window width T′=100.
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Fig. 7. Spike time irregularity quantified in operational time. (a) Raster diagram of 20 realizations of a gamma process (α=4) simulated in operational time (left). The ISI distribution (right) is displayed in units of the mean interval μ. (b) Spike density in real time follows the deterministic rate function (gray curve). The ISI distribution (right) is broadened by the non-stationarity of rate, leading to an over-estimation of the ‘stationary’ value of CV2. (c) Spike trains after de-modulation according to the empirical rate estimate (black curve in (b)). The demodulated ISIs (right) match well the gamma distribution in (a). (d) In the left panel, the estimates of CV2 for true (open circles) and empirical (filled circles) operational time match well, as shown for 10 repeated simulations. By contrast, the modulated spike trains (open squares) consistently lead to a considerable over-estimation. This result is independent of the number of trials (middle panel). The positive estimation bias introduced by rate modulation is stronger for more regular processes (right).
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Fig. 8. De-modulation of spike trains. (a) Action potentials of one pyramidal neuron measured during 20 experimental control trials (stationary input condition) displayed in operational time. (b) The estimated rate function (black curve) from 20 spike trains recorded during test trials reflect the modulation of excitatory shotnoise input according to a Gaussian profile with 200 ms standard width. (c) Same spike train ensemble as in (b) after de-modulation of time using the transformation tt′ (Eq. (2), Fig. 1). (d) Estimated cumulative distribution functions (cdf) of ISIs relative to the mean interval μ for control (blue), test (black) and de-modulated (red). (e) Q-Q plots of cumulative ISI distributions for test vs. control (black) and de-modulated vs. control (red). Colored values represent the 5%–95% inter-quantile range, gray values are outside this range. Repetition of the same experiment for 10 additional neurons for a modulation of standard width (f) 200 ms, and (g) 300 ms confirmed the good agreement of estimated CV2 in the control (open circles) and de-modulated test condition (filled circles). Open squares represent the rate modulated test condition.
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Fig. 9. Interval variability vs. count variability. Estimated FF and CV2 from ensembles of 15 trials and for a mean spike count of 10 (see text). Recordings are from the same five neurons (different symbols) as shown in Fig. 4 and Fig. 5, for two different current stimuli with either balanced input (ri=0.5, open symbols) or purely excitatory input (solid symbols). Dark and light gray shading represent 95% and 99% confidence regions from numerical simulations of gamma renewal processes; 108 of total 111 data points fall within the 95% region. Inset shows average values of FF (gray) and CV2 (white) for both input conditions. Symbols indicate averages for individual neurons.
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Fig. 10. Time resolved analysis of irregularity and variability. (a) Top panel shows 20 realizations of a rate modulated gamma process (α=2) in real time. Empirical estimates of rate function, CV2 and FF are based on 100 trials, respectively. The horizontal bar indicates the analysis window of width T=385 ms. The gray horizontal line shows the asymptotic value. (b) De-modulated spike trains and time resolved measurement in operational time with a fixed window of length T′=4. (c) Spike output of a cortical neuron during 20 repeated trials of modulated input current and time resolved measurement of CV2 and FF (T=661 ms). The gray line represents the empiric values as estimated during 20 control trials (see Section 2). (d) Demodulated spike trains as in b with T′=5.
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Fig. 11. Task-related changes of variability in monkey motor cortex. (a) Spiking activity of a motor cortical neuron during repeated trials which were aligned to the preparatory signal (PS) at time t=0. Blue circles indicate the time of movement onset. (b) Estimated firing rate shows clear task-related modulation with a strong response shortly before movement onset. (c–f) Blue curves show variability measured in original time (window width T=590 ms), red curves show variability as measured in operational time (T′=5) after back-transformation to the experimental time axis. (c) The CV2 exhibits modulations in relation to rate changes when measured on the original time axis (blue). These modulations are largely diminished when measured in operational time (red) with mean CV2=0.69 smaller than unity. (d) Measuring the CV2 in each trial separately (see text) leads to a significantly reduced trial-averaged which now appears to be almost constant throughout the task with an average of 0.50 in operational time (red). (e) Task-related Fano factor is highest at the beginning of the task but strongly decreases during the period of movement preparation. At the rate response peak shortly before onset of the center-out movement the Fano factor reaches a minimum at FF≈0.5–0.6. (f) The ratio of and FF, both measured in operational time, is close to unity during the task-related rate response.
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Journal of Neuroscience Methods
Volume 169, Issue 2, 30 April 2008, Pages 374-390
Methods for Computational Neuroscience
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