Sources of errors and uncertainties in the assessment of forest soil carbon stocks at different scales-review and recommendations.

Spatially explicit knowledge of recent and past soil organic carbon (SOC) stocks in forests will improve our understanding of the effect of human- and non-human-induced changes on forest C fluxes. For SOC accounting, a minimum detectable difference must be defined in order to adequately determine temporal changes and spatial differences in SOC. This requires sufficiently detailed data to predict SOC stocks at appropriate scales within the required accuracy so that only significant changes are accounted for. When designing sampling campaigns, taking into account factors influencing SOC spatial and temporal distribution (such as soil type, topography, climate and vegetation) are needed to optimise sampling depths and numbers of samples, thereby ensuring that samples accurately reflect the distribution of SOC at a site. Furthermore, the appropriate scales related to the research question need to be defined: profile, plot, forests, catchment, national or wider. Scaling up SOC stocks from point sample to landscape unit is challenging, and thus requires reliable baseline data. Knowledge of the associated uncertainties related to SOC measures at each particular scale and how to reduce them is crucial for assessing SOC stocks with the highest possible accuracy at each scale. This review identifies where potential sources of errors and uncertainties related to forest SOC stock estimation occur at five different scales-sample, profile, plot, landscape/regional and European. Recommendations are also provided on how to reduce forest SOC uncertainties and increase efficiency of SOC assessment at each scale.

On parsing the neural code in the prefrontal cortex of primates using principal dynamic modes.

Nonlinear modeling of multi-input multi-output (MIMO) neuronal systems using Principal Dynamic Modes (PDMs) provides a novel method for analyzing the functional connectivity between neuronal groups. This paper presents the PDM-based modeling methodology and initial results from actual multi-unit recordings in the prefrontal cortex of non-human primates. We used the PDMs to analyze the dynamic transformations of spike train activity from Layer 2 (input) to Layer 5 (output) of the prefrontal cortex in primates performing a Delayed-Match-to-Sample task. The PDM-based models reduce the complexity of representing large-scale neural MIMO systems that involve large numbers of neurons, and also offer the prospect of improved biological/physiological interpretation of the obtained models. PDM analysis of neuronal connectivity in this system revealed "input-output channels of communication" corresponding to specific bands of neural rhythms that quantify the relative importance of these frequency-specific PDMs across a variety of different tasks. We found that behavioral performance during the Delayed-Match-to-Sample task (correct vs. incorrect outcome) was associated with differential activation of frequency-specific PDMs in the prefrontal cortex.

Design of optimal stimulation patterns for neuronal ensembles based on Volterra-type hierarchical modeling.

This paper presents a general methodology for the optimal design of stimulation patterns applied to neuronal ensembles in order to elicit a desired effect. The methodology follows a variant of the hierarchical Volterra modeling approach that utilizes input-output data to construct predictive models that describe the effects of interactions among multiple input events in an ascending order of interaction complexity. The illustrative example presented in this paper concerns the multi-unit activity of CA1 neurons in the hippocampus of a rodent performing a learned delayed-nonmatch-to-sample (DNMS) task. The multi-unit activity of the hippocampal CA1 neurons is recorded via chronically implanted multi-electrode arrays during this task. The obtained model quantifies the likelihood of having correct performance of the specific task for a given multi-unit (spatiotemporal) activity pattern of a CA1 neuronal ensemble during the 'sample presentation' phase of the DNMS task. The model can be used to determine computationally (off-line) the 'optimal' multi-unit stimulation pattern that maximizes the likelihood of inducing the correct performance of the DNMS task. Our working hypothesis is that application of this optimal stimulation pattern will enhance performance of the DNMS task due to enhancement of memory formation and storage during the 'sample presentation' phase of the task.

Dynamic nonlinear modeling of interactions between neuronal ensembles using principal dynamic modes.

We present a novel methodology for modeling the interactions between neuronal ensembles that utilizes the concept of Principal Dynamic Modes (PDM) and their associated nonlinear functions (ANF). This new approach seeks to reduce the complexity of the multi-input/multi-output (MIMO) model of the interactions between neuronal ensembles--an issue of critical practical importance in scaling up the MIMO models to incorporate hundreds (or even thousands) of input-output neurons. Global PDMs were extracted from the data using estimated first-order and second-order kernels and singular value decomposition (SVD). These global PDMs represent an efficient "coordinate system" for the representation of the MIMO model. The ANFs of the PDMs are estimated from the histograms of the combinations of PDM output values that lead to output spikes. For initial testing and validation of this approach, we applied it to a set of data collected at the pre-frontal cortex of a non-human primate during a behavioral task (Delayed Match-to-Sample). Recorded spike trains from Layer-2 neurons were viewed as the "inputs" and from Layer-5 neurons as the outputs. Model prediction performance was evaluated by means of computed Receiver Operating Characteristic (ROC) curves. The results indicate that this methodology may greatly reduce the complexity of the MIMO model without significant degradation of performance.

Restorative encoding memory integrative neural device: "REMIND".

Construction and application of a neural prosthesis device that enhances existing and replaces lost memory capacity in humans is the focus of research described here in rodents. A unique approach for the analysis and application of neural population firing has been developed to decipher the pattern in which information is successfully encoded by the hippocampus where mnemonic accuracy is critical. A nonlinear dynamic multi-input multi-output (MIMO) model is utilized to extract memory relevant firing patterns in CA3 and CA1 and to predict online what the consequences of the encoded firing patterns reflect for subsequent information retrieval for successful performance of delayed-nonmatch-to-sample (DNMS) memory task in rodents. The MIMO model has been tested successfully in a number of different contexts, each of which produced improved performance by a) utilizing online predicted codes to regulate task difficulty, b) employing electrical stimulation of CA1 output areas in the same pattern as successful cell firing, c) employing electrical stimulation to recover cell firing compromised by pharmacological agents and d) transferring and improving performance in naïve animals using the same stimulation patterns that are effective in fully trained animals. The results in rodents formed the basis for extension of the MIMO model to nonhuman primates in the same type of memory task that is now being tested in the last step prior to its application in humans.

Modeling neuron-glia interactions: from parametric model to neuromorphic hardware.

Recent experimental evidence suggests that glial cells are more than just supporting cells to neurons - they play an active role in signal transmission in the brain. We herein propose to investigate the importance of these mechanisms and model neuron-glia interactions at synapses using three approaches: A parametric model that takes into account the underlying mechanisms of the physiological system, a non-parametric model that extracts its input-output properties, and an ultra-low power, fast processing, neuromorphic hardware model. We use the EONS (Elementary Objects of the Nervous System) platform, a highly elaborate synaptic modeling platform to investigate the influence of astrocytic glutamate transporters on postsynaptic responses in the detailed micro-environment of a tri-partite synapse. The simulation results obtained using EONS are then used to build a non-parametric model that captures the essential features of glutamate dynamics. The structure of the non-parametric model we use is specifically designed for efficient hardware implementation using ultra-low power subthreshold CMOS building blocks. The utilization of the approach described allows us to build large-scale models of neuron/glial interaction and consequently provide useful insights on glial modulation during normal and pathological neural function.

Improving bioassay sensitivity for neurotoxins detection using volterra based third order nonlinear analysis.

Based on a novel analytical method for analyzing short-term plasticity (STP) of the CA1 hippocampal region in vitro, a screening tool for the detection and classification of unknown chemical compounds affecting the nervous system was recently introduced [1], [2]. The recorded signal consisted of evoked population spike in response to Poisson distributed random train impulse stimuli. The developed analytical approach used the first order Volterra kernel and the Laguerre coefficients of the second order Volterra model as classification features [3]. The biosensor showed encouraging results, and was able to classify out of sample compounds correctly [2]. We have taken an exploratory step to investigate the advantage of introducing a third order model [4]. DAP5, an NMDA channel blocker, did not show major changes in the second order kernel and in its corresponding Laguerre coefficients. Data were reanalyzed using a third order model. DAP5 showed discernable changes in the third order kernel as well as in the some of the corresponding Laguerre coefficients. Hence, the third order Volterra based model has the potential to improve the sensitivity and the discriminatory power of the proposed bioassay.

17beta-Estradiol potentiates field excitatory postsynaptic potentials within each subfield of the hippocampus with greatest potentiation of the associational/commissural afferents of CA3.

We sought to determine the impact of 17beta-estradiol throughout the hippocampal trisynaptic pathway and to investigate the afferent fiber systems within CA1 and CA3 in detail. To achieve this objective, we utilized multielectrode arrays to simultaneously record the field excitatory postsynaptic potentials from the CA1, dentate gyrus, and CA3 of rat hippocampal slices in the presence or absence of 100 pM 17beta-estradiol. We confirmed our earlier findings in CA1, where 17beta-estradiol significantly increased field excitatory postsynaptic potentials amplitude (20%+/-3%) and slope (22%+/-7%). 17beta-Estradiol significantly potentiated the field excitatory postsynaptic potentials in dentate gyrus, amplitude (15%+/-4%) and slope (17%+/-5), and in CA3, amplitude (15%+/-4%) and slope (19%+/-5%). Using a high-density multielectrode array, we sought to determine the source of potentiation in CA1 and CA3 by determining the impact of 17beta-estradiol on the apical afferents and the basal afferents within CA1 and on the mossy fibers and the associational/commissural fibers within CA3. In CA1, 17beta-estradiol induced a modest increase in the amplitude (7%+/-2%) and slope (9%+/-3%) following apical stimulation with similar magnitude of increase following basal stimulation amplitude (10%+/-2%) and slope (12%+/-3%). In CA3, 17beta-estradiol augmented the mossy fiber amplitude (15%+/-3%) and slope (18%+/-6%) and the associational/commissural fiber amplitude (31%+/-13%) and slope (40%+/-15%). These results indicate that 17beta-estradiol potentiated synaptic transmission in each subfield of the hippocampal slice, with the greatest magnitude of potentiation at the associational/commissural fibers in CA3. 17beta-Estradiol regulation of CA3 responses provides a novel site of 17beta-estradiol action that corresponds to the density of estrogen receptors within the hippocampus. The implications of 17beta-estradiol potentiation of the field potential in each of the hippocampal subfields and in particular CA3 associational/commissural fibers for memory function and clinical assessment are discussed.

General methodology for nonlinear modeling of neural systems with Poisson point-process inputs.

This paper presents a general methodological framework for the practical modeling of neural systems with point-process inputs (sequences of action potentials or, more broadly, identical events) based on the Volterra and Wiener theories of functional expansions and system identification. The paper clarifies the distinctions between Volterra and Wiener kernels obtained from Poisson point-process inputs. It shows that only the Wiener kernels can be estimated via cross-correlation, but must be defined as zero along the diagonals. The Volterra kernels can be estimated far more accurately (and from shorter data-records) by use of the Laguerre expansion technique adapted to point-process inputs, and they are independent of the mean rate of stimulation (unlike their P-W counterparts that depend on it). The Volterra kernels can also be estimated for broadband point-process inputs that are not Poisson. Useful applications of this modeling approach include cases where we seek to determine (model) the transfer characteristics between one neuronal axon (a point-process 'input') and another axon (a point-process 'output') or some other measure of neuronal activity (a continuous 'output', such as population activity) with which a causal link exists.

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.

Hierarchical model of the population dynamics of hippocampal dentate granule cells.

A hierarchical modeling approach is used as the basis for a mathematical representation of the population activity of hippocampal dentate granule cells. Using neural field equations, the variation in time and space of dentate granule cell activity is derived from the summed synaptic potential and summed action potential responses of a population of granule cells evoked by monosynaptic excitatory input from entorhinal cortical afferents. In this formulation of the problem, we have considered a two-level hierarchy: the synapses of entorhinal cortical axons define the first level of organization, and dentate granule cells, which include these synapses, define the second, higher level of organization. The model is specified by two state field variables, for membrane potential and for synaptic efficacy, respectively, with both evolving according to different time scales. The two state field variables introduce new parameters, physiological and anatomical, which characterize the dentate from the point of view of neuronal and synaptic populations: (1) a set of geometrical constraints corresponding to the morphological properties of granule cells and anatomical characteristics of entorhinal-dentate connections; and (2) a set of neuronal parameters corresponding to physiological mechanisms. Assuming no interaction between granule cells, i.e., neither ephaptic nor synaptic coupling, the model is shown to be mathematically tractable and allows solution of the field equations leading to the determination of activity. This treatment leads to the definition of two state variables, volume of stimulated synapses and firing time, which describe observed activity. Numerical simulations are used to investigate the populational characterization of the dentate by individual parameters: (1) the relationship between the conditions of stimulation of active perforant path fibers, e.g., stimulating intensity, and activity in the granule cell layer; and (2) the influence of geometry on the generation of activity, i.e., the influence of neuron density and synaptic density-connectivity. As an example application of the model, the granule cell population spike is reconstructed and compared with experimental data.

Projection of the magnocellular red nucleus to the region of the accessory abducens nucleus in the rabbit.

The projection of the magnocellular red nucleus (RNm) to the region of the accessory abducens nucleus (AABD) was traced in rabbit using the bidirectional tracer wheat germ agglutinin-horseradish peroxidase (WGA-HRP). In one set of animals, recordings of antidromic responses from RNm neurons elicited by electrical stimulation of the rubrospinal tract were used to localize injections of WGA-HRP for orthograde labeling of RNm terminals. In a different set of animals, horseradish peroxidase was injected into the retractor bulbi muscle to retrogradely label motoneurons of the AABD. The positions of RNm fibers and terminals were examined and compared to the locations and distribution of AABD cell bodies and labeled dendrites. Analyses revealed that along the entire rostrocaudal extent of the AABD, RNm efferents terminate primarily lateral to, or in the lateral aspects of, labeled motoneurons. For the rostral AABD, RNm efferents terminate only lateral to the nucleus. Although the terminals are not positioned to contact cell bodies of the AABD, they could overlap with dendrites that extend in the lateral direction. RNm efferents terminate more extensively within the posterior AABD, overlapping within both dendritic and cell body regions of the nucleus. Even in this posterior region, however, RNm efferents were distributed primarily over the lateral half of the nucleus. These data show that RNm can monosynaptically influence the AABD, through primarily its lateral and posterior aspects. Our findings also show that a major target of RNm efferents is the reticular cell population located lateral to the AABD, suggesting that the RNm also may affect AABD motoneuronal output indirectly through its projection to reticular cells.

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.

A novel network for nonlinear modeling of neural systems with arbitrary point-process inputs.

This paper address the issue of nonlinear model estimation for neural systems with arbitrary point-process inputs using a novel network that is composed of a pre-processing stage of a Laguerre filter bank followed by a single hidden layer with polynomial activation functions. The nonlinear modeling problem for neural systems has been attempted thus far only with Poisson point-process inputs and using cross-correlation methods to estimate low-order nonlinearities. The specific contribution of this paper is the use of the described novel network to achieve practical estimation of the requisite nonlinear model in the case of arbitrary (i.e. non-Poisson) point-process inputs and high-order nonlinearities. The success of this approach has critical implications for the study of neuronal ensembles, for which nonlinear modeling has been hindered by the requirement of Poisson process inputs and by the presence of high-order nonlinearities. The proposed methodology yields accurate models even for short input-output data records and in the presence of considerable noise. The efficacy of this approach is demonstrated with computer-simulated examples having continuous output and point-process output, and with real data from the dentate gyrus of the hippocampus.

Intraocular pharmacokinetics and safety of a humanized monoclonal antibody in rabbits after intravitreal administration of a solution or a PLGA microsphere formulation.

Poly(lactic-co-glycolic) acid (PLGA) bioresorbable microspheres are used for controlled-release drug delivery and are particularly promising for ocular indications. The objective of the current study was to evaluate the pharmacokinetics and safety of a recombinant human monoclonal antibody (rhuMAb HER2) in rabbits after bolus intravitreal administration of a solution or a PLGA-microsphere formulation. On Day 0, forty-eight male New Zealand white rabbits (2.3-2.6 kg) were immobilized with intramuscular ketamine/xylazine, and the test materials were injected directly into the vitreous compartment. Group 1 animals received rhuMAb HER2 in 50:50 lactide: glycolide PLGA microspheres; Group 2 animals received rhuMAb HER2 in solution (n = 24/group). The dose for each eye was 25 microg (50 microl). After dosing, animals were sacrificed at 2 min, and on 1, 2, 4, 7, 14, 23, 29, 37, 44, 50, and 56 days (n = 2/timepoint/group). Safety assessment included direct ophthalmoscopy, clinical observations, body weight, and hematology and clinical chemistry panels. At necropsy, vitreous and plasma were collected for pharmacokinetics and analysis for antibodies to rhuMAb HER2, and the vitreal pellet (Group 1) was prepared for histologic evaluation. All animals completed the study per protocol-both treatments were well tolerated, and no suppurative or mixed inflammatory cell reaction was observed in the vitreal samples (Group 1) at any of the time points examined. Antibodies to rhuMAb HER2 were detected in plasma samples by Day 7 in both treatment groups, but infrequently in vitreous samples. There were no safety implications associated with this immune response. The in vitro characterization of the PLGA microspheres provided reasonable projections of the in vivo rhuMAb HER2 release kinetics (Group 1). The total amount of antibody that was released was similar in vitro (25.9%) and in vivo (32.4%). RhuMAb HER2 (Group 2) was cleared slowly from the vitreous compartment, with initial and terminal half-lives of 0.9 and 5.6 days, respectively. The volume of distribution approximated the vitreous volume in a rabbit eye.

Application of a novel modeling method to the nonstationary properties of potentiation in the rabbit hippocampus.

This paper presents the first application of a novel methodology for nonstationary nonlinear modeling to neurobiological data consisting of extracellular population field potentials recorded from the dendritic layer of the dentate gyrus of the rabbit hippocampus under conditions of stimulus-induced potentiation. The experimental stimulus was a Poisson random sequence with a mean rate of 5 impulses/s applied to the perforant path, which was sufficient to induce a progressive potentiation of perforant path-evoked granule cell response. The modeling method utilizes a novel artificial neural network architecture, which is based on the general time-varying Volterra model. The artificial neural network is composed of parallel subnets of three-layer perceptrons with polynomial activation functions, with the output of each subnet modulated by an appropriate time function that models the system nonstationarities and gives the summative output its time-varying characteristics. For the specific application presented herein these time functions are sigmoidal functions with trainable slopes and inflection points. A possible mapping between the nonstationary components of the model and the mechanisms underlying potentiation changes in the hippocampus is discussed.

17beta-estradiol enhances NMDA receptor-mediated EPSPs and long-term potentiation.

Gonadal steroid hormones influence CNS functioning through a variety of different mechanisms. To test the hypothesis that estrogen modulates synaptic plasticity in the hippocampus, in vitro hippocampal slices from 2-mo-old Sprague-Dawley male rats were used to determine the effect of 17beta-estradiol on both N-methyl-D-aspartate (NMDA) receptor-mediated excitatory postsynaptic potentials (EPSPs) through intracellular recordings and long-term potentiation (LTP) through extracellular recordings. Intracellular EPSPs and extracellular field EPSPs (fEPSPs) were recorded from CA1 pyramidal cells by stimulating Schaffer collateral fibers. In intracellular experiments, slices were perfused with medium containing bicuculline (5 microM) and low Mg2+ (0.1 mM) to enhance the NMDA receptor-mediated currents and 6, 7-dinitroquinoxaline-2,3-dione (DNQX) (10 microM) to block the alpha-amino-3-hydroxy-5-methyl-4-isoxazoleproprianate (AMPA) receptor-mediated component. The effects of 17beta-estradiol on NMDA receptor-mediated activity were excitatory; concentrations >10 nM induced seizure activity, and lower concentrations (1 nM) markedly increased the amplitude of NMDA-mediated EPSPs (both the first and second responses increased during paired pulse stimulation by 180 and 197%, respectively). In extracellular experiments, slices perfused with 17beta-estradiol (100 pM) exhibited a pronounced, persisting, and significant enhancement of LTP of both the fEPSP slope (192%) and fEPSP amplitude (177%) compared with control slices (fEPSP slope = 155%; fEPSP amplitude = 156%) 30 min after high-frequency stimulation. These data demonstrate that estrogen enhances NMDA receptor-mediated currents and promotes an enhancement of LTP magnitude.

Modeling of nonlinear nonstationary dynamic systems with a novel class of artificial neural networks.

This paper introduces a novel neural-network architecture that can be used to model time-varying Volterra systems from input-output data. The Volterra systems constitute a very broad class of stable nonlinear dynamic systems that can be extended to cover nonstationary (time-varying) cases. This novel architecture is composed of parallel subnets of three-layer perceptrons with polynomial activation functions, with the output of each subnet modulated by an appropriate time function that gives the summative output its time-varying characteristics. The paper shows the equivalence between this network architecture and the class of time-varying Volterra systems, and demonstrates the range of applicability of this approach with computer-simulated examples and real data. Although certain types of nonstationarities may not be amenable to this approach, it is hoped that this methodology will provide the practical tools for modeling some broad classes of nonlinear, nonstationary systems from input-output data, thus advancing the state of the art in a problem area that is widely viewed as a daunting challenge.

Spatial distribution of potentiated synapses in hippocampus: dependence on cellular mechanisms and network properties.

Long-term potentiation (LTP) of synaptic transmission, studied intensively in reduced brain preparations such as hippocampal brain slices, is the leading candidate for the cellular/molecular basis of learning and memory. Serious consideration of LTP as underlying information storage in the intact brain, however, requires understanding how LTP can be induced selectively at specific synaptic sites in a neural system when the mechanisms underlying LTP are regulated by other structural and functional properties of the same neural system. In the studies reported here, we tested the hypothesis that different patterns of activity within the same population of entorhinal cortical afferents could lead to a selective potentiation of spatially distinct populations of synapses across different regions of the hippocampus, including those activated multisynaptically. We focused specifically on potentiation of direct, monosynaptic entorhinal input to dentate granule cells, which expresses an NMDA receptor-dependent LTP, and on potentiation of indirect, disynaptic entorhinal input to CA3 pyramidal cells, which is transmitted by the mossy fiber projection of dentate granule cells and expresses an NMDA receptor-independent LTP. The principal findings of these experiments show that lower stimulation frequencies (10-20 Hz) of entorhinal cortical axons selectively induce LTP of mossy fiber input to CA3 transsynaptically via excitation of dentate granule cells, and that patterns of stimulation of that mimic neuronal firing in the entorhinal cortex during endogenous theta rhythm (five-impulse bursts at 200 Hz, interburst intervals of 200 msec) induce LTP both monosynaptically for input to dentate granule cells and transsynaptically for mossy fiber input to CA3.

Novel expression mechanism for synaptic potentiation: alignment of presynaptic release site and postsynaptic receptor.

A combination of experimental and modeling approaches was used to study cellular-molecular mechanisms underlying the expression of short-term potentiation (STP) and long-term potentiation (LTP) of glutamatergic synaptic transmission in the hippocampal slice. Electrophysiological recordings from dentate granule cells revealed that high-frequency stimulation of perforant path afferents induced a robust STP and LTP of both (+/-)-alpha-amino-3-hydroxy-5-methylisoxazole-4-propionic acid (AMPA) and N-methyl-D-aspartic acid (NMDA) receptor-mediated synaptic responses. However, the decay time constant for STP of the AMPA receptor-mediated excitatory postsynaptic potential was approximately 6 min, whereas the decay time constant for STP of the NMDA receptor-mediated excitatory postsynaptic potential was only 1 min. In addition, focal application of agonists during the expression of STP revealed that the magnitude of conductance change elicited by NMDA application was significantly enhanced, whereas the magnitude of conductance change elicited by application of AMPA remained constant. These findings are most consistent with a postsynaptic mechanism of STP and LTP. Different putative mechanisms were evaluated formally using a computational model that included diffusion of glutamate within the synaptic cleft, different kinetic properties of AMPA and NMDA receptor/channels, and geometric relations between presynaptic release sites and postsynaptic receptor/channels. Simulation results revealed that the only hypothesis consistent with experimental data is that STP and LTP reflect a relocation of AMPA receptor/channels in the postsynaptic membrane such that they become more closely "aligned" with presynaptic release sites. The same mechanism cannot account for STP or LTP of NMDA receptor-mediated responses; instead, potentiation of the NMDA receptor subtype is most consistent with an increase in receptor sensitivity or number.