An efficient method for studying short-term plasticity with random impulse train stimuli

Gholmieh, Ghassan; Courellis, Spiros H.; Marmarelis, Vasilis Z.; Berger, Theodore W.

doi:10.1016/s0165-0270(02)00164-4

Cited by 24 publications

(19 citation statements)

References 32 publications

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“…A third-order Poisson-Volterra model was used to analyze the RIT data [29], [30], [39], [50], [51]. Three Laguerre basis functions were found to be appropriate as a tradeoff between model complexity and prediction accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…Although second-order Poisson-Volterra models have been used previously to model the dentate gyrus [25], [39] and the CA1 area of the hippocampus [30], a third-order model was found to be necessary in this study in order to fully characterize the nonlinear dynamics of the LPP and the MPP neuronal transformations. Specifically, the NMSE value dropped to 4.88 ±0.47% for the third-order model relative to the NMSE value of 6.71±0.75% when the secondorder model was used, a drop that was found to be statistically significant (p < 0.01).…”

Section: Discussionmentioning

confidence: 99%

“…The brain slices were acquired using the following standard electrophysiological procedures [30]. Hippocampi from both hemispheres were dissected under cold (2 °C) artificial cerebro-spinal fluid (aCSF) (NaCl, 128 mM; KCl, 5 mM; NaH 2 PO 4 , 1.25 mM; NaHCO 3 , 26 mM; Glucose, 10 mM; MgSO 4 , 2 mM; ascorbic acid, 2 mM; CaCl 2 , 2 mM) and aerated with a mixture of 95% O 2 and 5% CO 2 .…”

Section: A Preparation Of Slice and Multielectrode Stimulationmentioning

confidence: 99%

“…The obtained model represents a compact quantitative representation of the dentate gyrus based on experimentally available datasets [30], [31], and it is mathematically rigorous and scalable with predictive capabilities [32], [33]. The predictive accuracy of this model is quantitatively evaluated using the normalized mean-square prediction error.…”

Section: Introductionmentioning

confidence: 99%

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Modeling the Nonlinear Properties of thein vitroHippocampal Perforant Path-Dentate System Using Multielectrode Array Technology

Dimoka

Courellis

Gholmieh

et al. 2008

IEEE Trans. Biomed. Eng.

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A modeling approach to characterize the nonlinear dynamic transformations of the dentate gyrus of the hippocampus is presented and experimentally validated. The dentate gyrus is the first region of the hippocampus which receives and integrates sensory information via the perforant path. The perforant path is composed of two distinct pathways: 1) the lateral path and 2) the medial perforant path. The proposed approach examines and captures the short-term dynamic characteristics of these two pathways using a nonparametric, third-order Poisson-Volterra model. The nonlinear characteristics of the two pathways are represented by Poisson-Volterra kernels, which are quantitative descriptors of the nonlinear dynamic transformations. The kernels were computed with experimental data from in vitro hippocampal slices. The electrophysiological activity was measured with custom-made multielectrode arrays, which allowed selective stimulation with random impulse trains and simultaneous recordings of extracellular field potential activity. The results demonstrate that this mathematically rigorous approach is suitable for the multipathway complexity of the hippocampus and yields interpretable models that have excellent predictive capabilities. The resulting models not only accurately predict previously reported electrophysiological descriptors, such as paired pulses, but more important, can be used to predict the electrophysiological activity of dentate granule cells to arbitrary stimulation patterns at the perforant path.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: A Preparation Of Slice and Multielectrode Stimulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling the Nonlinear Properties of thein vitroHippocampal Perforant Path-Dentate System Using Multielectrode Array Technology

Dimoka

Courellis

Gholmieh

et al. 2008

IEEE Trans. Biomed. Eng.

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“…We applied the RIT stimuli to the Schaffer collaterals of CA1 in vitro preparations and recorded the corresponding population spike responses. We used the reduced VolterraPoisson modeling approach 9,15,16 to compute the STP descriptors in the form of the reduced Volterra-Poisson kernels. We employed a third order model to capture and represent the nonlinear dynamic behavior of the underlying STP mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamic Model of CA1 Short-Term Plasticity using Random Impulse Train Stimulation

et al. 2007

Self Cite

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A comprehensive, quantitative description of the nonlinear dynamic characteristics of the short-term plasticity (STP) in the CA1 hippocampal region is presented. It is based on the Volterra-Poisson modeling approach using random impulse train (RIT) stimuli. In vitro hippocampal slice preparations were used from adult rats. RIT stimuli were applied at the Schaffer collaterals and population spike responses were recorded at the CA1 cell body layer. The computed STP descriptors that capture the nonlinear dynamics of the underlying STP mechanisms were the Volterra-Poisson kernels. The kernels quantified the presence of facilitatory and inhibitory STP behavior in magnitude and duration. A third order Volterra-Poisson STP model was introduced that accurately predicted in-sample and out-of-sample system responses. The proposed model could also accurately predict impulse pair and short impulse train system responses.

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Nonlinear Dynamic Modeling of Physiological Systems

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An efficient method for studying short-term plasticity with random impulse train stimuli

Cited by 24 publications

References 32 publications

Modeling the Nonlinear Properties of thein vitroHippocampal Perforant Path-Dentate System Using Multielectrode Array Technology

Modeling the Nonlinear Properties of thein vitroHippocampal Perforant Path-Dentate System Using Multielectrode Array Technology

Nonlinear Dynamic Model of CA1 Short-Term Plasticity using Random Impulse Train Stimulation

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