Browsing by Author "Yang, X."
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Item Auditory Representations of Acoustic Signals(1991) Yang, X.; Wang, K.; Shamma, S.A.; ISRAn analytically tractable framework is presented to describe neural processing in the early stages of the auditory system. Algorithms are developed to assess the integrity of the acoustic spectrum at all processing stages. The algorithms employ wavelet representations, multiresolution processing, and the method of convex projections to reconstruct close replica of the input stimulus. Reconstructions using natural speech sounds demonstrate minimal loss of information along the auditory pathway. Furthermore, close inspections of the final auditory patterns reveals spectral enhancements and noise suppression that have close perceptual correlates. Finally, the auditory representations are shown to be versatile for many applications, including automatic speech recognition and low bit-rate data compression.Item Detection and Classification of Neural Signals and Identification of Neural Networks(1989) Yang, X.; Shamma, S.; ISRThis thesis aims to develop the theoretical and experimental means to study the nature of the neural networks of the nervous system. The most important parameters in a neural network are its synaptic connectivities (connection weights). Once the unknown connectivities in the nervous system are discovered, appropriate neural network models can be designed and used to mimic their action. To study the functional connectivity, reliable recording and identification of the simultaneous activities of a group of neurons is essential. In the first part of this thesis, a system for neural spike detection and classification is presented, which does not require a priori assumptions about spike shape or timing. The system consists of two subsystems. The learning subsystem, comprising a Haar transform detection scheme, a feature learning phase and a template learning phase, extracts templates for each separable spike class. The real-time detection and classification subsystem identifies spikes in the noisy neural trace and sorts them into classes, according to the templates and the statistics of the background noise. Three fast algorithms are proposed for the real-time sorting subsystem, and comparisons are made among different schemes. Performance of the system is illustrated by using it to classify spike in segments of neural activity recorded extracellularly from monkey motor cortex and from guinea pig and ferret auditory cortices. The system is implemented without human supervision and therefore is suitable for real-time multichannel recording. In the second part, analytical and experimental methods are provided for estimating synaptic connectivities from simultaneous recordings of multiple neurons (after separation). The results are based on detailed, yet flexible neuron models in which spike trains are modeled as general doubly stochastic point processes. The expressions derived can be used with nonstationary or stationary records, and can be readily extended from pairwise to multineuron estimates. Furthermore, we show analytically how the estimates are improved as more neurons are sampled, and derive the appropriate normalizations to eliminate stimulus-related correlations. Finally, we illustrate the use and interpretation of the analytical expressions on simulated spike trains and neural networks, and give explicit confidence measures on the estimates.Item Identification of Connectiviity in Neural Networks.(1989) Yang, X.; Shamma, S.A.; ISRAnalytical and experimental methods are provided for estimating synaptic connectivities from simultaneous recordings of multiple neurons. The results are based on detailed, yet flexible neuron models in which spike trains are modeled as general doubly stochastic point processes. The expressions derived can be used with non-stationary or stationary records, and can be readily extended from pair-wise to multi-neuron estimates. Furthermore, we show analytically how the estimates are improved as more neurons are sampled, and derive the appropriate normalizations to eliminate stimulus-related correlations. Finally, we illustrate the use and interpretation of the analytical expressions on simulated spike trains and neural networks, and give explicit confidence measures on the estimates.Item Minimum Mean Square Error Estimation of Connectivity in Biological Neural Networks(1991) Yang, X.; Shamma, S.A.; ISRA minimum mean square error (MMSE) estimation scheme is employed to identify the synaptic connectivity in neural networks. This new approach can substantially reduce the amount of data and the computational cost involved in the conventional correlation methods, and is suitable for both nonstationary and stationary neuronal firings. Two algorithms are proposed to estimate the synaptic connectivities recursively, one for nonlinear filtering, the other for linear filtering. In addition, the lower and upper bounds for the MMSE estimator are determined. It is shown that the estimators are consistent in quadratic mean. We also demonstrate that the conventional crossinterval histogram is an asymptotic linear MMSE estimator with an inappropriate initial value. Finally, simulations of both the nonlinear and linear (Kalman filter) estimates demonstrate that the true connectivity values are approached asymptotically.Item A Totally Automated Neural Spike Detection and Classification Scheme: A Preliminary Software System.(1986) Shamma, S.A.; Yang, X.; ISRA system for neural spike detection and classification is presented, which does not require a priori assumptions about spike presence or spike templates, and assumes only that the background noise has a Gaussian distribution. The system is divided into two parts: a learning subsystem and a real-time detection and classification subsystem. The former extracts templates of spikes for every class which includes a feature learning phase and a template learning phase. The latter picks up spikes in the noisy trace and sorts them out into classes, based on the templates that the learning subsystem provides and the statistics of the background noise. Performance of the system is illustrated by using it to classify spikes in a segment of neural activity recorded from monkey motor cortex. The system is implemented without human supervision so that it can be extended for multi-channel recording without loss of real-time property.