Coefficient of variation vs. mean interspike interval curves: What do they tell us about the brain?

Christodoulou, Chris; Bugmann, Guido

doi:10.1016/s0925-2312(01)00480-5

Cited by 42 publications

(30 citation statements)

References 22 publications

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“…All effects were reversible. *p Ͻ 0.01; **p Ͻ 0.0001. the more variable the bursting (Christodoulou and Bugmann, 2001). The average CV of the interburst intervals was 0.43 Ϯ 0.03 (n ϭ 72 cells; range, 0.08 -0.93).…”

Section: Frequency and Regularity Of Burstingmentioning

confidence: 96%

Olfactory Bulb Glomeruli: External Tufted Cells Intrinsically Burst at Theta Frequency and Are Entrained by Patterned Olfactory Input

Hayar¹,

Karnup²,

Shipley³

et al. 2004

J. Neurosci.

194

310

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Glomeruli, the initial sites of synaptic processing in the olfactory system, contain at least three types of neurons collectively referred to as juxtaglomerular (JG) neurons. The role of JG neurons in odor processing is poorly understood. We investigated the morphology, spontaneous, and sensory-evoked activity of one class of JG neurons, external tufted (ET) cells, using whole-cell patch-clamp and extracellular recordings in rat olfactory bulb slices. ET cells have extensive dendrites that ramify within a single glomerulus or, rarely, in two adjacent glomeruli. All ET neurons exhibit spontaneous rhythmic bursts of action potentials (ϳ1-8 bursts/sec). Bursting is intrinsically generated; bursting persisted and became more regular in the presence of ionotropic glutamate and GABA receptor antagonists. Burst frequency is voltage dependent; frequency increased at membrane potentials depolarized relative to rest and decreased during membrane potential hyperpolarization. Spontaneous bursting persisted in blockers of calcium channels that eliminated low-threshold calcium spikes (LTS) in ET cells. ET cells have a persistent sodium current available at membrane potentials that generate spontaneous bursting. Internal perfusion with a fast sodium channel blocker eliminated spontaneous bursting but did not block the LTS. These results suggest that persistent sodium channels are essential for spontaneous burst generation in ET cells. ET cell bursts were entrained to ON stimuli delivered over the range of theta frequencies. Thus, ET cells appear to be tuned to the frequency of sniffing.

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Section: Frequency and Regularity Of Burstingmentioning

confidence: 96%

Olfactory Bulb Glomeruli: External Tufted Cells Intrinsically Burst at Theta Frequency and Are Entrained by Patterned Olfactory Input

Hayar¹,

Karnup²,

Shipley³

et al. 2004

J. Neurosci.

194

310

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show abstract

“…However, comparing the results obtained for the gIF3 model with the behavior observed for the gIF1 and gIF2 model (see Figure 5, gIF1 and gIF2), both the incorporation of the state-dependent PSP amplitude and the effect of the reversal potential on the PSP amplitude are necessary conditions to reproduce the discharge statistics of the biophysical model. In order to fully reproduce the spontaneous discharge statistics seen in experiments (e.g., Smith & Smith, 1965;Noda & Adey, 1970;Burns & Webb, 1976;Softky & Koch, 1993;Stevens & Zador, 1998b), the high irregularity should also stem from a Poisson process; that is, the spike trains must be both exponentially distributed according to a gamma distribution and independent (Christodoulou & Bugmann, 2001;. Notes: For all models, the synaptic input rates for excitation and inhibition (ν e and ν i , respectively) were chosen to yield an output rate ν out of about 13 Hz, a high discharge variability C V around 1 (except for cLIF and vLIF), and in the conductance-based models (BM, PME, gIF1, gIF2, gIF3) a total input conductance about five times larger than the leak conductance and comparable to the leak conductance of the vLIF model.…”

Section: Response Dynamics Of Gif Modelsmentioning

confidence: 99%

Analytical Integrate-and-Fire Neuron Models with Conductance-Based Dynamics for Event-Driven Simulation Strategies

Rudolph¹,

Destexhe

2006

Neural Computation

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Event-driven simulation strategies were proposed recently to simulate integrate-and-fire (IF) type neuronal models. These strategies can lead to computationally efficient algorithms for simulating large-scale networks of neurons; most important, such approaches are more precise than traditional clock-driven numerical integration approaches because the timing of spikes is treated exactly. The drawback of such event-driven methods is that in order to be efficient, the membrane equations must be solvable analytically, or at least provide simple analytic approximations for the state variables describing the system. This requirement prevents, in general, the use of conductance-based synaptic interactions within the framework of event-driven simulations and, thus, the investigation of network paradigms where synaptic conductances are important. We propose here a number of extensions of the classical leaky IF neuron model involving approximations of the membrane equation with conductancebased synaptic current, which lead to simple analytic expressions for the membrane state, and therefore can be used in the event-driven framework. These conductance-based IF (gIF) models are compared to commonly used models, such as the leaky IF model or biophysical models in which conductances are explicitly integrated. All models are compared with respect to various spiking response properties in the presence of synaptic activity, such as the spontaneous discharge statistics, the temporal precision in resolving synaptic inputs, and gain modulation under in vivo-like synaptic bombardment. Being based on the passive membrane equation with fixed-threshold spike generation, the proposed gIF models are situated in between leaky IF and biophysical models but are much closer to the latter with respect to their dynamic behavior and response characteristics, while still being nearly as computationally efficient as simple IF neuron models. gIF models should therefore provide a useful tool for efficient and precise simulation of large-scale neuronal networks with realistic, conductance-based synaptic interactions.

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“…

AbstractIn this paper, we demonstrate that the high firing irregularity produced by the leaky integrate-and-fire neuron with the partial somatic reset mechanism, which has been shown to be the most likely candidate to reflect the mechanism used in the brain for reproducing the highly irregular cortical neuron firing at high rates (Bugmann, Christodoulou & Taylor, 1997;Christodoulou & Bugmann, 2001), enhances learning. More specifically, it enhances reward-modulated Spike Timing-Dependent Plasticity with eligibility trace when used in spiking neural networks, as shown by the results when tested in the simple benchmark problem of XOR as well as in a complex multiagent setting task.

…”

mentioning

confidence: 91%

“…They also showed that the Leaky Integrate-and-Fire (LIF) neuron model, which temporally integrates excitatory postsynaptic potentials generated by independent stochastic input spike trains, failed in reproducing this observed high firing irregularity. While many methods were proposed to reproduce Softky and Koch's findings (for a brief review, see Christodoulou & Bugmann, 2000, 2001, we have shown that a LIF neuron model with partial somatic reset is a very good candidate for reproducing the observed highly irregular firing at high rates by cortical neurons (Bugmann, Christodoulou & Taylor, 1997;Christodoulou & Bugmann, 2001). In this paper, we are investigating whether the high firing irregularity produced by LIF neurons with the partial somatic reset mechanism, when used in spiking neural networks in the benchmark problem of XOR and in a general-sum game, enhances reward-modulated Spike Timing-Dependent Plasticity (STDP) with eligibility trace (Florian, 2007).…”

Section: Introductionmentioning

confidence: 99%

Does High Firing Irregularity Enhance Learning?

Christodoulou

Cleanthous

2011

Neural Computation

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In this paper, we demonstrate that the high firing irregularity produced by the leaky integrate-and-fire neuron with the partial somatic reset mechanism, which has been shown to be the most likely candidate to reflect the mechanism used in the brain for reproducing the highly irregular cortical neuron firing at high rates (Bugmann, Christodoulou & Taylor, 1997;Christodoulou & Bugmann, 2001), enhances learning. More specifically, it enhances reward-modulated Spike Timing-Dependent Plasticity with eligibility trace when used in spiking neural networks, as shown by the results when tested in the simple benchmark problem of XOR as well as in a complex multiagent setting task.

show abstract

Coefficient of variation vs. mean interspike interval curves: What do they tell us about the brain?

Cited by 42 publications

References 22 publications

Olfactory Bulb Glomeruli: External Tufted Cells Intrinsically Burst at Theta Frequency and Are Entrained by Patterned Olfactory Input

Olfactory Bulb Glomeruli: External Tufted Cells Intrinsically Burst at Theta Frequency and Are Entrained by Patterned Olfactory Input

Analytical Integrate-and-Fire Neuron Models with Conductance-Based Dynamics for Event-Driven Simulation Strategies

Does High Firing Irregularity Enhance Learning?

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