Zhuo Wang

Differential Effect of TEA on Long-Term Synaptic Modification in Hippocampal CA1 and Dentate Gyrus in vitro

The effectiveness of tetraethylammonium (TEA) and high-frequency stimulation (HFS) in inducing long-term synaptic modification is compared in CA1 and dentate gyrus (DG) in vitro. High-frequency stimulation induces long-term potentiation (LTP) at synapses of both perforant path-DG granule cell and Schaffer collateral-CA1 pyramidal cell pathways. By contrast, TEA (25 mM) induces long-term depression in DG while inducing LTP in CA1. The mechanisms underlying the differential effect of TEA in CA1 and DG were investigated. It was observed that T-type voltage-dependent calcium channel (VDCC) blocker, Ni2+ (50 microM), partially blocked TEA-induced LTP in CA1. A complete blockade of the TEA-induced LTP occurred when Ni2+ was applied together with the NMDA receptor antagonist, D-APV. The L-type VDCC blocker, nifidipine (20 microM), had no effect on CA1 TEA-induced LTP. In DG of the same slice, TEA actually induced long-term depression (LTD) instead of LTP, an effect that was blocked by D-APV. Neither T-type nor L-type VDCC blockade could prevent this LTD. When the calcium concentration in the perfusion medium was increased, TEA induced a weak LTP in DG that was blocked by Ni2+. During exposure to TEA, the magnitude of field EPSPs was increased in both CA1 and DG, but the increase was substantially greater in CA1. Tetraethylammonium application also was associated with a large, late EPSP component in CA1 that persisted even after severing the connections between CA3 and CA1. All of the TEA effects in CA1, however, were dramatically reduced by Ni2+. The results of this study indicate that TEA indirectly acts via both T-type VDCCs and NMDA receptors in CA1 and, as a consequence, induces LTP. By contrast, TEA indirectly acts via only NMDA receptors in DG and results in LTD. The results raise the possibility of a major synaptic difference in the density and/or distribution of T-type VDCCs and NMDA receptors in CA1 and DG of the rat hippocampus.

Parametric and non-parametric modeling of short-term synaptic plasticity. Part II: Experimental study

This paper presents a synergistic parametric and non-parametric modeling study of short-term plasticity (STP) in the Schaffer collateral to hippocampal CA1 pyramidal neuron (SC) synapse. Parametric models in the form of sets of differential and algebraic equations have been proposed on the basis of the current understanding of biological mechanisms active within the system. Non-parametric Poisson–Volterra models are obtained herein from broadband experimental input–output data. The non-parametric model is shown to provide better prediction of the experimental output than a parametric model with a single set of facilitation/depression (FD) process. The parametric model is then validated in terms of its input–output transformational properties using the non-parametric model since the latter constitutes a canonical and more complete representation of the synaptic nonlinear dynamics. Furthermore, discrepancies between the experimentally-derived non-parametric model and the equivalent non-parametric model of the parametric model suggest the presence of multiple FD processes in the SC synapses. Inclusion of an additional set of FD process in the parametric model makes it replicate better the characteristics of the experimentally-derived non-parametric model. This improved parametric model in turn provides the requisite biological interpretability that the non-parametric model lacks.

A modeling paradigm incorporating parametric and non-parametric methods

A novel parametric/non-parametric modeling paradigm was defined and used in characterization of synaptic transmission. In this paradigm, parametric and nonparametric techniques were incorporated in a complementary manner. Non-parametric method was used to generalize experimental data and extract system input/output properties. It provided a quantitative and intuitive way to validate a parametric model with respect to general, complete input patterns. Biological processes or mechanisms missed by the conventional parametric modeling approach were revealed and subsequently included into the modified parametric model.

Nonparametric interpretation and validation of the parametric short-term plasticity models

Biological systems can be modeled with two different approaches: parametric and nonparametric. The parametric approach (internal model) describes underlying biological mechanisms of the system while the nonparametric approach (external model) directly maps the systems input/output properties. In this study, two nonparametric models of short-term plasticity (STP) were estimated from both a parametric STP model and experimental STP data. These two models allowed us to study the formal mathematical relations between the parametric and nonparametric models of STP. Our results showed that 1) the nonparametric model estimated from the parametric model could efficiently extract the input/output transformational properties defined by the parametric model; 2) the nonparametric model estimated from experimental data could be used to validate the parametric model.

Interaction of Short-Term Neuronal Plasticity and Synaptic Plasticity Revealed by Nonlinear Systems Analysis in Dentate Granule Cells

Dentate granule cells receive inputs from the entorhinal cortex as the perforant path. There are two components of the perforant path: the lateral component (LPP) and the medial component (MPP). LPP and MPP convey different sensory modality information. It remains elusive as to how signals from different inputs interact and integrate at the granule cell level. We attempted to address this issue by using nonlinear systems analytic methods. Granule cell EPSPs and action potentials were recorded intracellularly from in vitro hippocampal slices of the rat. MPP and LPP were activated simultaneously by two independent Poisson random trains. Poisson-Volterra kernel models were estimated using Laguerre expansion of Volterra kernel technique. In the kernel models, self-kernels represent the intrinsic input/output properties of each pathway, while cross-kernels quantify the interactions between the two inputs. Short-term plasticity (STP) was revealed by both 2nd order self and cross kernels. We reason that the underlying mechanisms of the STP are diffusely distributed along input-specific synapses, dendritic tree and soma. The plasticity held by the dendritic tree/soma and synapses can be divided and referred to as neuronal and synaptic plasticity respectively. We argue that the cross kernel properties are determined primarily by neuronal plasticity while the self kernel properties are controlled largely by synaptic plasticity. Our experimental data suggest that linear summation of the membrane potential of the postsynaptic neuron can only partially explain the neuronal plasticity. Both supra- and sublinear summations were observed. Thus, the neuronal plasticity is likely to be the product of passive and active processes of the postsynaptic neuron and plays a pivotal role in multiple inputs integration

Probabilistic transformation of temporal information at individual synapses

The temporal structure of synaptic activation is critical to the induction of long-term synaptic plasticity. Experimentally, action potentials in presynaptic axons are reliably triggered by electric stimulation. The frequency of presynaptic input is the same as the stimulation frequency and is defined here as input frequency. Upon arrival of presynaptic action potentials, transmitter release at individual synapses is probabilistic. We define the average frequency of transmitter release at individual synapses as release frequency. Computer modeling has been widely used to simulate the input frequency-dependent elevation of calcium in dendritic spines, which is believed to trigger the induction of synaptic plasticity. In these models, input frequency has been used indiscriminately as release frequency to simulate synaptic events. Here, using both empirical and theoretical methods, we demonstrated that the release frequency is significantly smaller than the input frequency due to the probabilistic nature of transmitter release. We propose that this temporal transformation be taken into account in future synaptic plasticity models.

Experimental and modeling studies of the contribution of NMDA receptor-channels to the expression of long-term potentiation

We Investigated the contribution of NMDA receptor-channels (NMDARs) to the expression of long-term potentiation (LTP) using electrophysiological and modeling methods. Intracellular recordings were obtained from hippocampal dentate gyrus (DG) granule cells. Perforant path afferents were stimulated with short bursts of pulses at 40 Hz, instead of the traditional protocol of a single stimulus at less than 0.1 Hz. The synaptic summation provided by the burst protocol enabled us to measure the NMDAR-mediated component of synaptic responses in the presence of physiological concentrations of Mg/sup 2+/. We tested the hypothesis that NMDARs undergo LTP by comparing the NMDA/AMPA ratio of synaptic responses of control and LTP slices. An increase in the ratio was observed in the LTP group strongly suggesting potentiation of the NMDARs. In order to infer changes in synaptic conductances based on EPSPs recorded at the soma, we constructed a compartmental model of a granule cell. The effect of changes in AMPAR synaptic conductances, and of changes in the distribution of activated synapses on the NMDA/AMPA ratio was studied with computer simulations. Results confirmed that NMDARs are potentiated following LTP induction, and contribute significantly to the expression of LTP.