Long-term attraction in higher order neural networks.

Recent results on the memory storage capacity of higher order neural networks indicate a significant improvement compared to the limited capacity of the Hopfield model. However, such results have so far been obtained under the restriction that only a single iteration is allowed to converge. This paper presents a indirect convergence (long-term attraction) analysis of higher order neural networks. Our main result is that for any kappa(d)

Maffucci's syndrome--the result of neural abnormalities? Evidence of mitogenic neurotransmitters present in enchondromas and soft tissue hemangiomas.

BACKGROUND: Maffucci's syndrome (MS) is distinguished by the enigmatic association of benign cartilaginous bone tumors and soft tissue hemangiomas. METHODS: This study was conducted to define the distribution of nerves and neuropeptides around these tumors. Results were measured by quantitative image analysis of immunohistochemical staining. Four types of tissues were compared: connective tissues around normal muscles, solitary hemangiomas, MS hemangiomas, and MS enchondromas (the last two from a single patient). RESULTS: The number of nerves was found to be quadrupled in both types of hemangiomas as compared to normal connective tissue. A unique feature of MS tissues is the presence of an increased number of nerve fibers not only in the lesions but also in histologically normal margins of resection surrounding the lesions. Furthermore, hemangiomas of both types were found to contain a significantly higher number of calcitonin gene-related peptide-, substance P-, and methionine enkephalin-positive fibers than did normal muscle or its related fibroconnective tissue. These neuropeptides are mitogens, and their presence stimulates the growth of the abnormal blood vessels. Enchondroma fragments from an MS patient contained numerous methionine enkephalin-positive nerves. This neuropeptide is known to act as a growth factor in cartilage proliferation. CONCLUSIONS: A neural abnormality of the neuropeptidergic nervous system seems to relate to the abnormal tumors seen in MS.

Joint modeling and maximum-likelihood estimation of pitch and linear prediction coefficient parameters.

The well-known speech production model is considered, where the speech signal is modeled as the output of an all-pole filter driven either by some white noise sequence (unvoiced speech) or by the sum of a periodic excitation and a noise sequence (voiced speech). Approximate maximum-likelihood (ML) estimation algorithms for the unvoiced case are well known. The ML estimator of the parameters is obtained for the voiced speech model. These parameters consist of the parameters of the periodic excitation (pitch parameters) and the parameters of the filter [linear prediction coefficient (LPC) parameters]. The results of the application of the algorithm on simulated and on real speech data are presented.