Nonlinear Dynamic Model of CA1 Short-Term Plasticity using Random Impulse Train Stimulation
- 92 Downloads
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.
KeywordsCA1 Multielectrode array Nonlinear analysis Paired pulse Random impulse train Short-term plasticity Kernels
This work was supported by Grant No. RR-01861 from the Division of Research Resources of the National Institutes of Health and by Grants 0646 and 0259 from DARPA Controlled Biological Systems Program and the Office of Naval Research.
- 2.Berger T. W., J. L. Eriksson, D. A. Ciarolla, R. J Sclabassi (1988) Nonlinear systems analysis of the hippocampal Perforant Path-Dentate projection. II Effects of random train stimulation. J. Neurophysiol. 60:1077–1094Google Scholar
- 8.Creager R., T. Dunwiddie, G. Lynch (1980) Paired-pulse and frequency facilitation in the CA1 region of the in vitro rat hippocampus. J. Neurophysiol. 299:409–424Google Scholar
- 9.Courellis, S. H., V. Z. Marmarelis, and T. W. Berger. Modeling event-driven nonlinear dynamics in neuronal systems with multiple inputs. Annual Conference Biomedical Engineering Society, Seattle, WA, 2000Google Scholar
- 13.Fuhrmann G., I. Segev, H. Markram, M. Tsodyks (2002) Coding of temporal information by activity-dependent synapses. J. Neurophys. 87:140–148Google Scholar
- 21.Marmarelis V. Z. (2004) Nonlinear Dynamic Modeling of Physiological Systems. Wiley, New York, NYGoogle Scholar
- 28.Rieke, F., D. Waarland, R. R. De Ruyter Van Steveninck, and W. Bialek. Spikes: Exploring the Neural Code. MIT Press, Cambridge, MA, 1997Google Scholar