Fluctuation-driven rhythmogenesis in an excitatory neuronal network with slow adaptation

Nesse, William H.; Borisyuk, Alla; Bressloff, Paul C.

doi:10.1007/s10827-008-0081-y

Cited by 38 publications

(40 citation statements)

References 42 publications

(47 reference statements)

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“…It is possible to derive mean-field systems for network with noise (Nicola et al 2014;Nesse et al 2008). The overall results are similar, with a region of bursting existing in theḡ, I parameter space.…”

Section: Theoretical Aspects and Relation To Our Other Studiessupporting

confidence: 56%

Examining the limits of cellular adaptation bursting mechanisms in biologically-based excitatory networks of the hippocampus

et al. 2015

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Determining the biological details and mechanisms that are essential for the generation of population rhythms in the mammalian brain is a challenging problem. This problem cannot be addressed either by experimental or computational studies in isolation. Here we show that computational models that are carefully linked with experiment provide insight into this problem. Using the experimental context of a whole hippocampus preparation in vitro that spontaneously expresses theta frequency (3-12 Hz) population bursts in the CA1 region, we create excitatory network models to examine whether cellular adaptation bursting mechanisms could critically contribute to the generation of this rhythm. We use biologically-based cellular models of CA1 pyramidal cells and network sizes and connectivities that correspond to the experimental context. By expanding our mean field analyses to networks with heterogeneity and non all-to-all coupling, we allow closer correspondence with experiment, and use these analyses to greatly extend the range of parameter values that are explored. We find that our model excitatory networks can produce theta frequency population bursts in a robust fashion.Thus, even though our networks are limited by not including inhibition at present, our results indicate that cellular adaptation in pyramidal cells could be an important aspect for the occurrence of theta frequency population bursting in the hippocampus. These models serve as a starting framework for the inclusion of inhibitory cells and for the consideration of additional experimental features not captured in our present network models.

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Section: Theoretical Aspects and Relation To Our Other Studiessupporting

confidence: 56%

Examining the limits of cellular adaptation bursting mechanisms in biologically-based excitatory networks of the hippocampus

et al. 2015

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“…The pre-BötC is the kernel for the mammalian inspiratory breathing rhythm [4], with periods from 2-3 min to 2 sec, suggesting that a wide range of adaptation time scales may be involved. These oscillations have recently been modeled as a noise-driven recurrent excitatory neural network whose dynamics can be captured by a low-dimensional mean-field model similar to that studied below [5].Consider a simple cubic FitzHugh-Nagumo-like model for the activity z of an oscillatory system:(1)where ðtÞ is an uncorrelated Gaussian process: hðtÞi ¼ 0, hðtÞðt 0 Þi ¼ t;t 0 , I is the input current, and is the noise strength. The variable H ¼ P M j¼1 h j is the sum of AHP currents h j , each governed by the equation…”

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confidence: 99%

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confidence: 99%

Oscillation Regularity in Noise-Driven Excitable Systems with Multi-Time-Scale Adaptation

2008

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We investigate oscillation regularity of a noise-driven system modeled with a slow after-hyperpolarizing adaptation current (AHP) composed of multiple-exponential relaxation time scales. Sufficiently separated slow and fast AHP time scales (biphasic decay) cause a peak in oscillation irregularity for intermediate input currents I, with relatively regular oscillations for small and large currents. An analytic formulation of the system as a stochastic escape problem establishes that the phenomena is distinct from standard forms of coherence resonance. Our results explain data on the oscillation regularity of the pre-Bötzinger complex, a neural oscillator responsible for inspiratory breathing rhythm generation in mammals.

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“…The contribution of both cellular and network bursting are of interest, and context and specifics need to be taken into consideration when building CA3 network models (Traub et al 1989(Traub et al , 1992. Finally, it will be essential to continue taking advantage of other modeling and theoretical studies (e.g., Ho 2011;Latham et al 2000;Nesse et al 2008;Tabak et al 2010;Vladimirski et al 2008), to be able to obtain an understanding of underlying model mechanisms.…”

Section: Discussionmentioning

confidence: 99%

Network bursting using experimentally constrained single compartment CA3 hippocampal neuron models with adaptation

et al. 2011

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The hippocampus is a brain structure critical for memory functioning. Its network dynamics include several patterns such as sharp waves that are generated in the CA3 region. To understand how population outputs are generated, models need to consider aspects of network size, cellular and synaptic characteristics and context, which are necessarily 'balanced' in appropriate ways to produce particular outputs. Thick slice hippocampal preparations spontaneously produce sharp waves that are initiated in CA3 regions and depend on the right balance of glutamatergic activities. As a step toward developing network models that can explain important balances in the generation of hippocampal output, we develop models of CA3 pyramidal cells. Our models are single compartment in nature, use an Izhikevich-type structure and involve parameter values that are specifically designed to encompass CA3 intrinsic properties. Importantly, they incorporate spike frequency adaptation characteristics that are directly comparable to those measured experimentally. Excitatory networks using these model cells are able to produce bursting suggesting that the amount of spike frequency adaptation expressed in the biological cells is an essential contributor to network bursting, and as such, may be important for sharp wave generation. The network bursting mechanism is numerically dissected showing the critical balance between adaptation and excitatory drive. The compact nature of our models allows large network simulations to be efficiently computed. This, together with the linkage of our models to cellular characteristics, will allow us to develop an understanding of population output of CA3 hippocampus with direct biological comparisons.

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Fluctuation-driven rhythmogenesis in an excitatory neuronal network with slow adaptation

Cited by 38 publications

References 42 publications

Examining the limits of cellular adaptation bursting mechanisms in biologically-based excitatory networks of the hippocampus

Examining the limits of cellular adaptation bursting mechanisms in biologically-based excitatory networks of the hippocampus

Oscillation Regularity in Noise-Driven Excitable Systems with Multi-Time-Scale Adaptation

Network bursting using experimentally constrained single compartment CA3 hippocampal neuron models with adaptation

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