Nerve terminal GABAA receptors activate Ca2+/calmodulin-dependent signaling to inhibit voltage-gated Ca2+ influx and glutamate release

-Aminobutyric acid type A (GABAA) receptors, a family of Cl-permeable ion channels, mediate fast synaptic inhibition as postsynaptically enriched receptors for -aminobutyric acid at GABAergic synapses. Here we describe an alternative type of inhibition mediated byGABAA receptors present on neocortical glutamatergic nerve terminals and examine the underlying signaling mechanism(s). By monitoring the activity of the presynaptic CaM kinase II/synapsin I signaling pathway in isolated nerve terminals, we demonstrate that GABAA receptor activation correlated with an increase in basal intraterminal [Ca2]i. Interestingly, this activation of GABAA receptors resulted in a reduction of subsequent depolarization-evoked Ca2 influx, which thereby led to an inhibition of glutamate release. To investigate how the observed GABAA receptor-mediated modulation operates, we determined the sensitivity of this process to the Na-K-2Cl cotransporter 1 antagonist bumetanide, as well as substitution of Ca2 with Ba2, or Ca2/calmodulin inhibition by W7. All of these treatments abolished the modulation by GABAA receptors. Application of selective antagonists of voltage-gated Ca2 channels (VGCCs) revealed that the GABAA receptor-mediated modulation of glutamate release required the specific activity of L- and R-type VGCCs. Crucially, the inhibition of release by these receptors was abolished in terminals isolated from R-type VGCC knock-out mice. Together, our results indicate that a functional coupling between nerve terminal GABAA receptors and L- or R-type VGCCs is mediated by Ca2/calmodulin-dependent signaling. This mechanism provides a GABA-mediated control of glutamatergic synaptic activity by a direct inhibition of glutamate release.

Invasive neural prosthesis for neural signal detection and nerve stimulation

This paper specifically examines the implantation of a microelectrode array into the median nerve of the left arm of a healthy male volunteer. The objective was to establish a bi-directional link between the human nervous system and a computer, via a unique interface module. This is the first time that such a device has been used with a healthy human. The aim of the study was to assess the efficacy, compatibility, and long term operability of the neural implant in allowing the subject to perceive feedback stimulation and for neural activity to be detected and processed such that the subject could interact with remote technologies. A case study demonstrating real-time control of an instrumented prosthetic hand by means of the bi-directional link is given. The implantation did not result in infection, and scanning electron microscope images of the implant post extraction have not indicated significant rejection of the implant by the body. No perceivable loss of hand sensation or motion control was experienced by the subject while the implant was in place, and further testing of the subject following the removal of the implant has not indicated any measurable long term defects. The implant was extracted after 96 days. Copyright © 2004 John Wiley & Sons, Ltd.

The 'glial' glutamate transporter, EAAT2 (Glt-1) accounts for high affinity glutamate uptake into adult rodent nerve endings

The excitatory amino acid transporters (EAAT) removes neurotransmitters glutamate and aspartate from the synaptic cleft. Most CNS glutamate uptake is mediated by EAAT2 into glia, though nerve terminals show evidence for uptake, through an unknown transporter. Reverse-transcriptase PCR identified the expression of EAAT1, EAAT2, EAAT3 and EAAT4 mRNAs in primary cultures of mouse cortical or striatal neurones. We have used synaptosomes and glial plasmalemmal vesicles (GPV) from adult mouse and rat CNS to identify the nerve terminal transporter. Western blotting showed detectable levels of the transporters EAAT1 (GLAST) and EAAT2 (Glt-1) in both synaptosomes and GPVs. Uptake of [3H]D-aspartate or [3H]L-glutamate into these preparations revealed sodium-dependent uptake in GPV and synaptosomes which was inhibited by a range of EAAT blockers: dihydrokainate, serine-o-sulfate, l-trans-2,4-pyrrolidine dicarboxylate (PDC) (+/-)-threo-3-methylglutamate and (2S,4R )-4-methylglutamate. The IC50 values found for these compounds suggested functional expression of the 'glial, transporter, EAAT2 in nerve terminals. Additionally blockade of the majority EAAT2 uptake sites with 100 micro m dihydrokainate, failed to unmask any functional non-EAAT2 uptake sites. The data presented in this study indicate that EAAT2 is the predominant nerve terminal glutamate transporter in the adult rodent CNS.

Inverse model control using recurrent networks

This paper illustrates how internal model control of nonlinear processes can be achieved by recurrent neural networks, e.g. fully connected Hopfield networks. It is shown that using results developed by Kambhampati et al. (1995), that once a recurrent network model of a nonlinear system has been produced, a controller can be produced which consists of the network comprising the inverse of the model and a filter. Thus, the network providing control for the nonlinear system does not require any training after it has been trained to model the nonlinear system. Stability and other issues of importance for nonlinear control systems are also discussed.

Input/output linearization using dynamic recurrent neural networks

This paper brings together two areas of research that have received considerable attention during the last years, namely feedback linearization and neural networks. A proposition that guarantees the Input/Output (I/O) linearization of nonlinear control affine systems with Dynamic Recurrent Neural Networks (DRNNs) is formulated and proved. The proposition and the linearization procedure are illustrated with the simulation of a single link manipulator.

The relative order and inverses of recurrent networks

Differential geometry is used to investigate the structure of neural-network-based control systems. The key aspect is relative order—an invariant property of dynamic systems. Finite relative order allows the specification of a minimal architecture for a recurrent network. Any system with finite relative order has a left inverse. It is shown that a recurrent network with finite relative order has a local inverse that is also a recurrent network with the same weights. The results have implications for the use of recurrent networks in the inverse-model-based control of nonlinear systems.

Dynamic recurrent neural network for system identification and control

A dynamic recurrent neural network (DRNN) that can be viewed as a generalisation of the Hopfield neural network is proposed to identify and control a class of control affine systems. In this approach, the identified network is used in the context of the differential geometric control to synthesise a state feedback that cancels the nonlinear terms of the plant yielding a linear plant which can then be controlled using a standard PID controller.

Stable receding horizon control based on recurrent networks

The last decade has seen the re-emergence of artificial neural networks as an alternative to traditional modelling techniques for the control of nonlinear systems. Numerous control schemes have been proposed and have been shown to work in simulations. However, very few analyses have been made of the working of these networks. The authors show that a receding horizon control strategy based on a class of recurrent networks can stabilise nonlinear systems.

Approximation of non-autonomous dynamic systems by continuous time recurrent neural networks

This work provides a framework for the approximation of a dynamic system of the form x˙=f(x)+g(x)u by dynamic recurrent neural network. This extends previous work in which approximate realisation of autonomous dynamic systems was proven. Given certain conditions, the first p output neural units of a dynamic n-dimensional neural model approximate at a desired proximity a p-dimensional dynamic system with n>p. The neural architecture studied is then successfully implemented in a nonlinear multivariable system identification case study.

Inverting recurrent neural networks for internal model control of nonlinear systems

In this paper, we show how a set of recently derived theoretical results for recurrent neural networks can be applied to the production of an internal model control system for a nonlinear plant. The results include determination of the relative order of a recurrent neural network and invertibility of such a network. A closed loop controller is produced without the need to retrain the neural network plant model. Stability of the closed-loop controller is also demonstrated.

A practical approach for training dynamic recurrent neural networks: use of Apriori information

Presents a technique for incorporating a priori knowledge from a state space system into a neural network training algorithm. The training algorithm considered is that of chemotaxis and the networks being trained are recurrent neural networks. Incorporation of the a priori knowledge ensures that the resultant network has behaviour similar to the system which it is modelling.

Internal model control of nonlinear systems through the inversion of recurrent neural networks

Recurrent neural networks can be used for both the identification and control of nonlinear systems. This paper takes a previously derived set of theoretical results about recurrent neural networks and applies them to the task of providing internal model control for a nonlinear plant. Using the theoretical results, we show how an inverse controller can be produced from a neural network model of the plant, without the need to train an additional network to perform the inverse control.