Fast- and slow-rising AMPA receptor-mediated EPSCs occur at central synapses. Fast-rising EPSCs are thought to be mediated by rapid local release of glutamate. However, two controversial mechanisms have been proposed to underlie slow-rising EPSCs: prolonged local release of transmitter via a fusion pore, and spillover of transmitter released rapidly from distant sites. We have investigated the mechanism underlying slow-rising EPSCs and the diffusion coefficient of glutamate in the synaptic cleft (Dglut) at cerebellar mossy fiber-granule cell synapses using a combination of diffusion modeling and patch-clamp recording. Simulations show that modulating Dglut has different effects on the peak amplitudes and time courses of EPSCs mediated by these two mechanisms. Slowing diffusion with the macromolecule dextran slowed slow-rising EPSCs and had little effect on their amplitude, indicating that glutamate spillover underlies these currents. Our results also suggest that under control conditions Dglut is approximately 3-fold lower than in free solution.

The amplitude and shape of EPSC waveforms are thought to be important determinants of information processing and storage in the brain, yet relatively little is known about the origins of EPSC variability or how it affects synaptic signaling. We investigated the stochastic determinants of AMPA receptor-mediated EPSC variability at cerebellar mossy fiber-granule cell (MF-GC) connections by combining multiple-probability fluctuation analysis (MPFA) and deconvolution methods. The properties of MF connections with a single release site and the effects of the rapidly equilibrating competitive antagonist kynurenic acid on EPSCs suggest that receptors are not saturated by glutamate during a quantal event and that quanta sum linearly over a wide range of release probabilities. MPFA revealed an average of five vesicular release sites per MF-GC connection. Our results show that the time course of vesicular release is rapid (decay, ϭ 75 s) and independent of release probability, introducing little jitter in the shape or timing of the quantal component of the EPSC at physiological temperature. Moreover, the peak vesicular release rate per release site after an action potential (AP) (ϳ3 ms Ϫ1 ) is substantially higher than previously reported for central synapses. Interaction of amplitude fluctuations arising from quantal release and quantal size with the slower, low variability spillover-mediated current produce substantial variability in EPSC shape. Our simulations of MF-GC transmission suggest that quantal variability and transmitter spillover extend the voltage from which AP threshold can be crossed, improving reliability, and that fast vesicular release allows precise signaling across MF connections with heterogeneous weights.

Native AMPA receptors (AMPARs) exhibit rapid and profound desensitization in the sustained presence of glutamate. Desensitization therefore contributes to short-term depression at synapses in which glutamate accumulates. At synapses that do not exhibit desensitization-dependent depression, AMPARs are thought to be protected against prolonged or repetitive exposure to synaptically released glutamate. At the cerebellar mossy fiber to granule cell (GC) synapse, in which high release probability and glutamate spillover produce a substantial buildup of glutamate concentration in the cleft ([Glut] cleft ) during high-frequency transmission, only moderate desensitization of the phasic AMPAR EPSC occurs. To investigate how such currents are produced, we examined the kinetic properties of synaptic AMPARs in GCs using glutamate uncaging. Photolysis of 4-methoxy-7-nitroindolinyl-caged L-glutamate with large illumination spots produced step-like increases in [Glut] cleft that could be used to systematically probe AMPAR kinetics. At low levels of activation, synaptic AMPARs exhibited little desensitization. With larger activations, the desensitization time course became faster, but the level of desensitization was only weakly dependent on receptor occupancy. Indeed, a substantial desensitization-resistant current component remained (17%) in saturating glutamate. Photolysis with small illumination spots produced brief [Glut] cleft waveforms and transient AMPAR activations, similar to the EPSC current components. Paired-pulse uncaging with such spots revealed little desensitization after spillover-like activations and modest depression after activations that mimicked quantal and spillover components together. Our results show that GC AMPARs exhibit a resistance to desensitization at low occupancies and that this property is crucial for sustaining highfrequency transmission at a synapse in which glutamate accumulates.

In this paper, the general methodology for experimental verification/validation of C4ISR and other sensors' performance, is presented, based on Bayesian inference, in general, and binary sensors, in particular. This methodology, called Bayesian Truthing, defines Performance Metrics for binary sensors in: physics, optics, electronics, medicine, law enforcement, C3ISR, QC, ATR (Automatic Target Recognition), terrorism related events, and many others. For Bayesian Truthing, the sensing medium itself is not what is truly important; it is how the decision process is affected.

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When reaching toward a target, human subjects use slower movements to achieve higher accuracy, and this can be accompanied by increased limb impedance (stiffness, viscosity) that stabilizes movements against motor noise and external perturbation. In arthropods, the activity of common inhibitory motor neurons influences limb impedance, so we hypothesized that this might provide a mechanism for speed and accuracy control of aimed movements in insects. We recorded simultaneously from excitatory leg motor neurons and from an identified common inhibitory motor neuron (CI 1 ) in locusts that performed natural aimed scratching movements. We related limb movement kinematics to recorded motor activity and demonstrate that imposed alterations in the activity of CI 1 influenced these kinematics. We manipulated the activity of CI 1 by injecting depolarizing or hyperpolarizing current or killing the cell using laser photoablation. Naturally higher levels of inhibitory activity accompanied faster movements. Experimentally biasing the firing rate downward, or stopping firing completely, led to slower movements mediated by changes at several joints of the limb. Despite this, we found no effect on overall movement accuracy. We conclude that inhibitory modulation of joint stiffness has effects across most of the working range of the insect limb, with a pronounced effect on the overall velocity of natural movements independent of their accuracy. Passive joint forces that are greatest at extreme joint angles may enhance accuracy and are not affected by motor inhibition.

Experiments can be complex and produce large volumes of heterogeneous data, which make their execution, analysis, independent replication and meta-analysis difficult. We propose a mathematical model for experimentation and analysis in physiology that addresses these problems. We show that experiments can be composed from time-dependent quantities, and be expressed as purely mathematical equations. Our structure for representing physiological observations can carry information of any type and therefore provides a precise ontology for a wide range of observations. Our framework is concise, allowing entire experiments to be defined unambiguously in a few equations. In order to demonstrate that our approach can be implemented, we show the equations that we have used to run and analyse two non-trivial experiments describing visually stimulated neuronal responses and dynamic clamp of vertebrate neurons. Our ideas could provide a theoretical basis for developing new standards of data acquisition, analysis and communication in neurophysiology.

A common problem across science and engineering is that aspects of models have to be estimated from observed data. An instance of this familiar to control engineers is system identification. Bayesian inference is a principled way to estimate parameters: exploiting Bayes' theorem, an equational probabilistic model is "inverted", yielding a probability distribution for the unknown parameters given the observations. This paper presents Ebba, a declarative language for probabilistic modelling where models can be used both "forwards" for probabilistic computation and "backwards" for parameter estimation. The novel aspect of Ebba is its implementation: a shallow, arrows-based, embedding. This provides a clear semantical account and ensures that only models that support estimation can be expressed. As arrow-like notions have proved useful in modelling dynamical systems, this might also suggest an approach to an integrated language for modelling dynamical systems and parameter estimation.