Satish S. Nair

Extracellular Glutamate: Functional Compartments Operate in Different Concentration Ranges

Extracellular glutamate of glial origin modulates glial and neuronal glutamate release and synaptic plasticity. Estimates of the tonic basal concentration of extracellular glutamate range over three orders of magnitude (0.02–20 μM) depending on the technology employed to make the measurement. Based upon binding constants for glutamate receptors and transporters, this range of concentrations translates into distinct physiological and pathophysiological roles for extracellular glutamate. Here we speculate that the difference in glutamate measurements can be explained if there is patterned membrane surface expression of glutamate release and transporter sites creating extracellular subcompartments that vary in glutamate concentration and are preferentially sampled by different technologies.

PDE modeling and control of a flexible two-link manipulator

A partial differential equation (PDE) model for a flexible two-link manipulator is derived and transformed to a form appropriate for the development of stable control designs. Stable control of this nonlinear infinite dimensional two-link system is then achieved by a novel control design developed using passivity and Lyapunov-based methods. A two-link hardware experimental setup is used to validate the analytical PDE model and the proposed stable control design scheme.

Professional Skills in the Engineering Curriculum

Faculty from the Department of Electrical and Computer Engineering and the College of Education at the University of Missouri (MU), Columbia, developed a novel course for engineering graduate students emphasizing pedagogy and professional skills. The two-semester course sequence, titled “Preparing Engineering Faculty and Professionals,” includes readings from books that cover several different areas: How People Learn, with focus on the latest findings from cognitive science and their applicability to teaching; The 7 Habits of Highly Effective People for discussion of other professional skills; and The World is Flat for discussion of global trends and its effects on professionals. Other components of the course include lectures by guest speakers on topics ranging from how universities work and how to run successful research centers to leadership traits for engineers. A pilot survey of students at the end of the two-course sequence revealed that students had acquired little knowledge about pedagogy and professional skills from other courses in their undergraduate and graduate engineering curriculum; this course addresses such deficits by raising awareness and knowledge of these skills.

Modeling and compensation of low-velocity friction with bounds

A systematic model-free methodology for the identification and compensation of friction is proposed and is shown to be viable for a class of dynamic systems. Design of the proposed identifier for friction uses Gaussian networks and incorporates explicit performance bound information. The identifier is then used in a particular compensation strategy that provides error bound information. The proposed identification and control designs have been validated using a hardware example case system. The methodology for identifying friction is systematic and uses minimal knowledge of the dynamics which is particularly attractive for a large class of low-dimensional dynamic systems with friction.

Decision making using multiple models

Abstract Many real world business situations require classification decisions that must often be made on the basis of judgment and past performance. In this paper, we propose a decision framework that combines multiple models or techniques in a complementary fashion to provide input to managers who make such decisions on a routine basis. We illustrate the framework by specifically using five different classification techniques – neural networks, discriminant analysis, quadratic discriminant analysis (QDA), k -nearest neighbor (KNN), and multinomial logistic regression analysis (MNL). Application of the decision framework to an actual retail department store data shows that it is most useful in those cases where uncertainty is high and a priori classification cannot be made with a high degree of reliability. The proposed framework thus enhances the value of exception reporting, and provides managers additional insights into the phenomenon being studied.

Identification and control experiments using neural designs

Neural designs are reported for system identification and control using static and dynamic gradient update schemes. Real-time implementation of the designs using a hardware example case system illustrates the inherent capability of neural networks to handle nonlinearities, learn, and perform control effectively for a real world system, based on minimal system information. The advantages of dynamic schemes over static ones are highlighted and a neural control design with feedforward and feedback components that facilitates incorporation of available knowledge about a system is described.<<ETX>>

Coregulation of Ion Channel Conductances Preserves Output in a Computational Model of a Crustacean Cardiac Motor Neuron

Similar activity patterns at both neuron and network levels can arise from different combinations of membrane and synaptic conductance values. A strategy by which neurons may preserve their electrical output is via cell type-dependent balances of inward and outward currents. Measurements of mRNA transcripts that encode ion channel proteins within motor neurons in the crustacean cardiac ganglion recently revealed correlations between certain channel types. To determine whether balances of intrinsic currents potentially resulting from such correlations preserve certain electrical cell outputs, we developed a nominal biophysical model of the crustacean cardiac ganglion using biological data. Predictions from the nominal model showed that coregulation of ionic currents may preserve the key characteristics of motor neuron activity. We then developed a methodology of sampling a multidimensional parameter space to select an appropriate model set for meaningful comparison with variations in correlations seen in biological datasets.

Rapid Homeostatic Plasticity of Intrinsic Excitability in a Central Pattern Generator Network Stabilizes Functional Neural Network Output

Neurons and networks undergo a process of homeostatic plasticity that stabilizes output by integrating activity levels with network and cellular properties to counter longer-term perturbations. Here we describe a rapid compensatory interaction among a pair of potassium currents, IA and IKCa, that stabilizes both intrinsic excitability and network function in the cardiac ganglion of the crab, Cancer borealis. We determined that mRNA levels in single identified neurons for the channels which encode IA and IKCa are positively correlated, yet the ionic currents themselves are negatively correlated, across a population of motor neurons. We then determined that these currents are functionally coupled; decreasing levels of either current within a neuron causes a rapid increase in the other. This functional interdependence results in homeostatic stabilization of both the individual neuronal and the network output. Furthermore, these compensatory increases are mechanistically independent, suggesting robustness in the maintenance of neural network output that is critical for survival. Together, we generate a complete model for homeostatic plasticity from mRNA to network output where rapid post-translational compensatory mechanisms acting on a reservoir of channels proteins regulated at the level of gene expression provide homeostatic stabilization of both cellular and network activity.

Impact of infralimbic inputs on intercalated amygdala neurons: A biophysical modeling study

Intercalated (ITC) amygdala neurons regulate fear expression by controlling impulse traffic between the input (basolateral amygdala; BLA) and output (central nucleus; Ce) stations of the amygdala for conditioned fear responses. Previously, stimulation of the infralimbic (IL) cortex was found to reduce fear expression and the responsiveness of Ce neurons to BLA inputs. These effects were hypothesized to result from the activation of ITC cells projecting to Ce. However, ITC cells inhibit each other, leading to the question of how IL inputs could overcome the inter-ITC inhibition to regulate the responses of Ce neurons to aversive conditioned stimuli (CSs). To investigate this, we first developed a compartmental model of a single ITC cell that could reproduce their bistable electroresponsive properties, as observed experimentally. Next, we generated an ITC network that implemented the experimentally observed short-term synaptic plasticity of inhibitory inter-ITC connections. Model experiments showed that strongly adaptive CS-related BLA inputs elicited persistent responses in ITC cells despite the presence of inhibitory interconnections. The sustained CS-evoked activity of ITC cells resulted from an unusual slowly deinactivating K(+) current. Finally, over a wide range of stimulation strengths, brief IL activation caused a marked increase in the firing rate of ITC neurons, leading to a persistent decrease in Ce output, despite inter-ITC inhibition. Simulations revealed that this effect depended on the bistable properties and synaptic heterogeneity of ITC neurons. These results support the notion that IL inputs are in a strategic position to control extinction of conditioned fear via the activation of ITC neurons.

Low velocity friction compensation

A model-free design methodology is reported for identification and stable adaptive control of a class of systems with state dependent parasitic effects such as friction. The methodology is constructive, incorporates modeling error bound information, and ensures stable and convergent performance. The identifier and control designs are applicable to a class of low-dimensional dynamic systems with the cited parasitic effects. Validation of the designs is provided using two hardware example cases.