Pyramidal Neurons Switch From Integrators In Vitro to Resonators Under In Vivo-Like Conditions

Prescott, Steven A.; Ratté, Stéphanie; Koninck, Yves De; Sejnowski, Terrence J.

doi:10.1152/jn.90634.2008

Cited by 159 publications

(174 citation statements)

References 56 publications

(58 reference statements)

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“…7A) because the quenched variability (van Vreeswijk and Sompolinsky, 1998) introduced by each neuron having a different set of presynaptic neurons suffices. Missed and skipped cycles (Glass and Winfree, 1984;Kaplan et al, 1996) are characteristic of the subcritical Hopf bifurcation (Ermentrout, 1996;Rinzel and Ermentrout, 1998;Prescott et al, 2008) underlying type 2 excitability (Hodgkin, 1948;Izhikevich, 2007). Fitzhugh (1976) described a "limiting trajectory" in a model with type 2 excitability, a concept that is very similar to the constant rebound interval after a strong inhibition observed in our study.…”

Section: Discussionmentioning

confidence: 99%

“…We are interested here only in PIR mechanisms fast enough that can be engaged by inhibitory postsynaptic potentials (IPSPs) generated at GABA A synapses; for example, removal of sodium channel inactivation. In a type 2 oscillator, the net steady-state current at membrane potentials traversed during the ISI is outward, so pacing can only be maintained when the ISI is traversed quickly enough that the inward current evoked by the after-hyperpolarization can persist (Prescott et al, 2008); this rate sets the lower bound on spiking frequency. Otherwise, the oscillation will stop at the point the inward and outward currents come into balance.…”

Section: Resonant Interneuronsmentioning

confidence: 99%

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Resonant Interneurons Can Increase Robustness of Gamma Oscillations

et al. 2015

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Gamma oscillations are believed to play a critical role in in information processing, encoding, and retrieval. Inhibitory interneuronal network gamma (ING) oscillations may arise from a coupled oscillator mechanism in which individual neurons oscillate or from a population oscillator in which individual neurons fire sparsely and stochastically. All ING mechanisms, including the one proposed herein, rely on alternating waves of inhibition and windows of opportunity for spiking. The coupled oscillator model implemented with Wang-Buzsáki model neurons is not sufficiently robust to heterogeneity in excitatory drive, and therefore intrinsic frequency, to account for in vitro models of ING. Similarly, in a tightly synchronized regime, the stochastic population oscillator model is often characterized by sparse firing, whereas interneurons both in vivo and in vitro do not fire sparsely during gamma, but rather on average every other cycle. We substituted so-called resonator neural models, which exhibit class 2 excitability and postinhibitory rebound (PIR), for the integrators that are typically used. This results in much greater robustness to heterogeneity that actually increases as the average participation in spikes per cycle approximates physiological levels. Moreover, dynamic clampexperimentsthatshowautapse-inducedfiringinentorhinalcorticalinterneuronssupporttheideathatPIRcanserveasanetworkgamma mechanism. Furthermore, parvalbumin-positive (PV ϩ ) cells were much more likely to display both PIR and autapse-induced firing than GAD2 ϩ cells, supporting the view that PV ϩ fast-firing basket cells are more likely to exhibit class 2 excitability than other types of inhibitory interneurons.

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Section: Discussionmentioning

confidence: 99%

Section: Resonant Interneuronsmentioning

confidence: 99%

Resonant Interneurons Can Increase Robustness of Gamma Oscillations

et al. 2015

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“…(3) Type 2 neurons respond best to fluctuating inputs around a characteristic frequency, whereas type 1 neurons summate inputs across a broad range of frequencies. Types 1 and 2 neurons have therefore been labeled integrators and resonators, respectively [16,29]. (4) The phase response curve (PRC) differs between types 1 and 2 neurons [35]; near the onset of oscillations, type 1 neurons display a monophasic PRC, whereas type 2 neurons can display biphasic PRCs.…”

Section: Linear Spikementioning

confidence: 99%

Bistability in a Leaky Integrate-and-Fire Neuron with a Passive Dendrite

Schwemmer¹,

Lewis²

2012

SIAM J. Appl. Dyn. Syst.

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Abstract. We examine the influence of dendritic load on the firing dynamics of a spatially extended leaky integrate-and-fire (LIF) neuron that explicitly includes spiking dynamics. We obtain an exact analytical solution for this model and use it to derive a return map that completely captures the dynamics of the system. Using the map, we find that dendritic properties can significantly change the firing dynamics of the system. Under certain conditions, the addition of the dendrite can change the LIF model from type 1 excitability to type 2 excitability and induce bistability between periodic firing and the quiescent state. We identify the mechanism that causes the periodic behavior in the bistable regime as somatodendritic ping-pong. Furthermore, we use the return map to fully explore the model parameter space in order to find regions where this bistable behavior occurs. We then give physical interpretations of the dependence of the bistable behavior on model parameters. Finally, we demonstrate that the simpler two-compartment model displays qualitatively similar dynamics to the more complicated ball-and-stick model. 1. Introduction. Neurons are spatially extensive and heterogeneous. They typically consist of a dendritic tree, a soma (cell body), and an axon. The type of model one uses to represent a neuron depends upon a balance between mathematical tractability and biological realism and on the issue being addressed. A common technique in neuronal modeling is to represent the neuron as a single-compartment object that ignores the spatial anatomy of the cell, e.g., [11,25,26]. Although this simplification allows for greater mathematical tractability and computational efficiency, many neurons are not electrotonically compact. Therefore, singlecompartment models cannot be expected to capture the full spectrum of electrical behavior of neurons. Dendrites can have substantial effects on the dynamics of individual neurons. For example, the architecture and ionic channel density of a dendritic tree can alter the firing pattern and encoding properties of a neuronal oscillator [19,21,24], while the additional electrical load due to the dendrite can alter firing frequency [36,38]. For a full understanding of this behavior, more detailed models are required.

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“…In particular, it has been shown that the mechanisms of spike generation can be very precise (Mainen & Sejnowski, 1995) under physiological conditions (Boucsein et al, 2011;Konishi et al, 1988;Lestienne, 2001;Prescott et al, 2008). Spike synchronization, with or without oscillations, has been shown to be involved in the so-called binding problem (Engel & Singer, 2001;Singer & Gray, 1995;Singer, 1999;Super et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Multiple Tests Based on a Gaussian Approximation of the Unitary Events Method with Delayed Coincidence Count

et al. 2014

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The unitary events (UE) method is one of the most popular and efficient methods used over the past decade to detect patterns of coincident joint spike activity among simultaneously recorded neurons. The detection of coincidences is usually based on binned coincidence count (Grün, 1996 ), which is known to be subject to loss in synchrony detection (Grün, Diesmann, Grammont, Riehle, & Aertsen, 1999 ). This defect has been corrected by the multiple shift coincidence count (Grün et al., 1999 ). The statistical properties of this count have not been further investigated until this work, the formula being more difficult to deal with than the original binned count. First, we propose a new notion of coincidence count, the delayed coincidence count, which is equal to the multiple shift coincidence count when discretized point processes are involved as models for the spike trains. Moreover, it generalizes this notion to nondiscretized point processes, allowing us to propose a new gaussian approximation of the count. Since unknown parameters are involved in the approximation, we perform a plug-in step, where unknown parameters are replaced by estimated ones, leading to a modification of the approximating distribution. Finally the method takes the multiplicity of the tests into account via a Benjamini and Hochberg approach (Benjamini & Hochberg, 1995 ), to guarantee a prescribed control of the false discovery rate. We compare our new method, MTGAUE (multiple tests based on a gaussian approximation of the unitary events) and the UE method proposed in Grün et al. ( 1999 ) over various simulations, showing that MTGAUE extends the validity of the previous method. In particular, MTGAUE is able to detect both profusion and lack of coincidences with respect to the independence case and is robust to changes in the underlying model. Furthermore MTGAUE is applied on real data.

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Pyramidal Neurons Switch From Integrators In Vitro to Resonators Under In Vivo-Like Conditions

Cited by 159 publications

References 56 publications

Resonant Interneurons Can Increase Robustness of Gamma Oscillations

Resonant Interneurons Can Increase Robustness of Gamma Oscillations

Bistability in a Leaky Integrate-and-Fire Neuron with a Passive Dendrite

Multiple Tests Based on a Gaussian Approximation of the Unitary Events Method with Delayed Coincidence Count

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