Stage Center Theatre Newsletter- Oct. 2006

https://neiudc.neiu.edu/stagecenter/1000/thumbnail.jp

Stage Center Theatre Newsletter- Mar. 2007

https://neiudc.neiu.edu/stagecenter/1004/thumbnail.jp

Stage Center Theatre Newsletter- Dec. 2006

https://neiudc.neiu.edu/stagecenter/1002/thumbnail.jp

Microprocessor-based on-load valve sequencing for a turbo-alternator

An analog on-load valve sequencing system that is used to test the performances of the valves on a turbo-alternator can be replaced by a microcomputer testing system. On the way to the full-scale computerising of turbo-generator control systems, this type of testing system may be used under the control of a full-size supervisory computer. This microprocessor-based testing system provides test sequencing of the valves of an Electro-Hydraulic Governor. The hardware for the interfacing and scaling, and the firmware for the microprocessor are developed for one valve. The extension of the technique to the task of testing multiple valves in sequence is also discussed

Stage Center Theatre Newsletter- Nov. 2006

https://neiudc.neiu.edu/stagecenter/1001/thumbnail.jp

Stage Center Theatre Newsletter- Jan. 2007

https://neiudc.neiu.edu/stagecenter/1003/thumbnail.jp

Adaptive Stimulus Design for Dynamic Recurrent Neural Network Models

We present an adaptive stimulus design method for efficiently estimating the parameters of a dynamic recurrent network model with interacting excitatory and inhibitory neuronal populations. Although stimuli that are optimized for model parameter estimation should, in theory, have advantages over nonadaptive random stimuli, in practice it remains unclear in what way and to what extent an optimal design of time-varying stimuli may actually improve parameter estimation for this common type of recurrent network models. Here we specified the time course of each stimulus by a Fourier series whose amplitudes and phases were determined by maximizing a utility function based on the Fisher information matrix. To facilitate the optimization process, we have derived differential equations that govern the time evolution of the gradients of the utility function with respect to the stimulus parameters. The network parameters were estimated by maximum likelihood from the spike train data generated by an inhomogeneous Poisson process from the continuous network state. The adaptive design process was repeated in a closed loop, alternating between optimal stimulus design and parameter estimation from the updated stimulus-response data. Our results confirmed that, compared with random stimuli, optimally designed stimuli elicited responses with significantly better likelihood values for parameter estimation. Furthermore, all individual parameters, including the time constants and the connection weights, were recovered more accurately by the optimal design method. We also examined how the errors of different parameter estimates were correlated, and proposed heuristic formulas to account for the correlation patterns by an approximate parameter-confounding theory. Our results suggest that although adaptive optimal stimulus design incurs considerable computational cost even for the simplest excitatory-inhibitory recurrent network model, it may potentially help save time in experiments by reducing the number of stimuli needed for network parameter estimation