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PubMedDate not supplied

[Interaction of specific and nonspecific afferentation as a basis for neuronal integration upon completion of temporary connections].

In experiments on alert and non-immobilized rabbits, slow potentials and reactions of the visual area neurones were recorded in the course of elaboration of conditioned inhibition and of a defensive conditioned reflex to rhythmic light flashes of various frequencies without isochronous pain reinforcement. Analysis of the data obtained showed that the ability of the neurones to follow the light rhythm is due not so much to the properties of the cells themselves as to the morphofunctional properties of the system in which they are included and to the interaction between excitatory and inhibitory influences. Pain stimulation at all its frequencies weakens the inhibitory, hyperpolarization processes in the cortical visual area. At the same time under the influence of pain the first phase of activation in response to light flashes is fragmented in a number of neurones. Judging by the authors' and published data, such modulation of neuronal responses in the visual area under pain effect reflects a convergence of modality-specific and of activating modality-non-specific afferentations of the visual analyser. The result of such convergence is reproduced in a conditioned way.

Animals↗
PubMedDate not supplied

[Statistical analysis of interneuronal functional connections during conditioned reflex elaboration].

A milk conditioned reflex to a low frequency electrical stimulation of the visual pathways was elaborated in cats. Multineuronal activity in the visual and sensorimotor cortical areas was recorded in the chronic experiment. Statistical analysis of interneuronal intraanalyser and interanalyzer connections was made by constructing crosscorrelograms on the "Plurimat-C" computer. Comparison of the results of the interneuronal interaction in untrained and trained cats has shown an increase of one-way and two-way connections of both the intraanalyser and interanalyser types. Neurones with medium amplitude spikes changed their net properties more than other units acquiring ingoing and outgoing connections in both studied areas of the learned animals. Acquisition was achieved in all the neurones mainly due to increased number of connections from the sensorimotor analyser to the visual one. The problems of formation of backward conditioned connections and "local" conditioned reflex are discussed.

Animals↗
PubMed1981

Classical conditioning in a simple withdrawal reflex in Aplysia californica.

The ability of Aplysia and other gastropod molluscs to exhibit complex behaviors that can be modified by associative learning has encouraged us to search for an elementary behavior controlled by a simple and well analyzed neural circuit that also can be modified by this type of learning. Toward that end, we have now produced classical conditioning in the defensive siphon and gill withdrawal reflex of Aplysia. We used as a conditioned stimulus (CS) a light tactile stimulus to the siphon, which produces weak siphon and gill withdrawal. As the unconditioned stimulus (US), we used a strong electric shock to the tail, which produces a massive withdrawal reflex. Specific temporal pairing of the CS and US endowed the CS with the ability of triggering enhanced withdrawal of both the siphon and the gill. Random or unpaired presentations of the CS and US, as well as presentations of the CS or US alone, produced either no enhancement or significantly less enhancement than paired presentations of the CS and US. The conditioning is acquired rapidly (within 15 trials) and is retained for several days. The conditioned response is abolished completely by removal of the abdominal ganglion and many of the neurons involved in the conditioning have been identified in this ganglion previously. These include the sensory neurons and several interneurons in the CS pathway and the siphon and gill motor neurons of the conditioned and unconditioned response pathways. Moreover, the sensory neurons of the US pathway have been identified in the pleural ganglia. As a result of its simplicity, it should be possible in this reflex to specify neurons that are causally related to the conditioned response. Since this reflex also exhibits nonassociative learning, it also may be possible to compare associative and nonassociative learning on a mechanistic level.

Animals↗
PubMed1980

[Associative connections of the parietal region of the cerebral cortex in cats].

The interneuronal connections of the parietal associative cortex were studied in cat using the methods of retrograde axonal transport of horseradish peroxidase (HRP) and terminal degeneration (Fink-Heimer). Data on laminar topography of the afferents within area 7, and the nature of axonal terminals from visual, auditory and somatic sensory fields of cortex were obtained. The places of concentration of associative neurons and their morphological characteristic are described.

Animals↗
PubMed1995

[Oligodendrocyte lineage].

The mechanisms leading to cell diversification in the Vertebrate central nervous system are still poorly understood. We have analyzed neural differentiation potentialities of the embryonic chick optic nerve. In the adult, the optic nerve is made up of astrocytes and oligodendrocytes ensheathing retinal axons, but it is entirely devoid of neuronal cell bodies. Using explant cultures and specific cell type markers, we demonstrate that in fact the embryonic optic nerve contains cells endowed with neuronal potentialities but is initially devoid of a potential for oligodendrogenesis. Studies by other groups in rodents suggest that oligodendrocyte precursors may be initially restricted to the ventral region of the developing spinal cord. Taken together, these results indicate that early in development, oligodendrocyte precursors are not distributed homogeneously in the neuroepithelium. Preliminary results in our laboratory show that the specification of the oligodendrocyte lineage in the chick spinal cord may depend on ventral signals from the notochord.

Animals↗
PubMed2002

Irregularity of parenchymal echo patterns of liver analyzed with a neural network and risk of hepatocellular carcinoma in liver cirrhosis.

OBJECTIVE: In this study, we scored the hepatic parenchymal echo patterns as the coarse score (CS) analyzed with a neural network in cirrhosis patients and calculated the variations in CS as the coefficient of variation, and evaluate their usefulness as predictor of the development of hepatocellular carcinoma (HCC). METHODS: The relationship between the degree of variation in CS and histopathological findings was assessed in 10 autopsied livers fixed in formalin. The degree of intrahepatic variation in CS was calculated as the coefficient of variation of CS (CVCS). Irregular regeneration of liver cells in autopsied livers was classified into two categories, slight and severe. A total of 56 cirrhosis patients were prospectively followed to evaluate the predictors of HCC. RESULTS: A significant positive correlation was observed between CVCS and the coefficient of variation of the diameter of the regenerative nodules. Coefficient variation of nodule diameter and CVCS in patients in whom irregular regeneration was severe were significantly higher than those in patients in whom irregular regeneration was mild (p < 0.05). Concerning the relationship between hepatitis virus markers and CS or CVCS, CVCS was significantly higher in those who were hepatitis C virus antibody positive and those who were hepatitis B surface antigen negative (p < 0.01). Using a combination of CS and CVCS, the incidence of HCC, as determined by the Kaplan-Meier method, was significantly higher in patients whose CS was > or = 1.5 and CVCS was > or = 15%, as compared with that in patients whose CS was <1.5 and CVCS was <15% (p < 0.01) and whose CS was > or = 1.5 and CVCS was <15% (p < 0.05). Multivariate analysis of the predictors for HCC using the Cox's proportional hazards model showed a significant correlation between the risk of development of HCC and CVCS, CS and serum alpha-fetoprotein level. CONCLUSIONS: CVCS and CS can be calculated from liver echo patterns and are useful for identifying a high-risk group for HCC.

Alanine Transaminase↗
PubMed2004

Pyrolysis patterns of 5 close Corynebacterium species analyzed by artificial neural networks.

In the present study, an artificial neural network was trained with the Stuttgart Neural Networks Simulator, in order to identify Corynebacterium species by analyzing their pyrolysis patterns. An earlier study described the combination of pyrolysis, gas chromatography and atomic emission detection we used on whole cell bacteria. Carbon, sulfur and nitrogen were detected in the pyrolysis compounds. Pyrolysis patterns were obtained from 52 Corynebacterium strains belonging to 5 close species. These data were previously analyzed by Euclidean distances calculation followed by Unweighted Pair Group Method of Averages, a clustering method. With this early method, strains from 3 of the 5 species (C. xerosis, C. freneyi and C. amycolatum) were correctly characterized even if the 29 strains of C. amycolatum were grouped into 2 subgroups. Strains from the 2 remaining species (C. minutissimum and C. striatum) cannot be separated. To build an artificial neural network, able to discriminate the 5 previous species, the pyrolysis data of 42 selected strains were used as learning set and the 10 remaining strains as testing set. The chosen learning algorithm was Back-Propagation with Momentum. Parameters used to train a correct network are described here, and the results analyzed. The obtained artificial neural network has the following cone-shaped structure: 144 nodes in input, 25 and 9 nodes in 2 successive hidden layers, and then 5 outputs. It could classify all the strains in their species group. This network completes a chemotaxonomic method for Corynebacterium identification.

Algorithms↗
PubMed1999

DNA ploidy and cell cycle distribution of breast cancer aspirate cells measured by image cytometry and analyzed by artificial neural networks for their prognostic significance.

Chromosomal abnormalities are commonly associated with cancer, and their importance in the pathogenesis of the disease has been well recognized. Also recognized in recent years is the possibility that, together with chromosomal abnormalities, DNA ploidy of breast cancer aspirate cells, measured by image cytometric techniques, may correlate with prognosis of the disease. Here, we have examined the use of an artificial neural network to predict: 1) subclinical metastatic disease in the regional lymph nodes and 2) histological assessment, through the analysis of data obtained by image cytometric techniques of fine needle aspirates of breast tumors. The cellular features considered were: 1) DNA ploidy measured in terms of nuclear DNA content as well as by cell cycle distribution; 2) size of the S-phase fraction; and 3) nuclear pleomorphism. A further objective of the study was to analyze individual markers in terms of impact significance on predicting outcome in both cases. DNA ploidy, indicated by cell cycle distribution, was found markedly to influence the prediction of nodal spread of breast cancer, and nuclear pleomorphism to a lesser degree. Furthermore, a comparison between histological assessment and artificial neural network prediction shows a closer correlation between the neural approach and the development of further metastases as indicated in subsequent follow-up, than does histological assessment. These data demonstrate that artificial neural networks are capable of providing powerful and reliable indicators of possible lymph node metastasis, using measurements of cellular features alone.

Breast Neoplasms↗
PubMed1996

Predicting the presence of plasma heparin using neural networks to analyze coagulation screening assay optical profiles.

A method for predicting the presence of heparin from coagulation screening assays is described and data are presented. This method incorporates the use of a multilayer perceptron trained through an error back-propagation algorithm in analyzing clotting optical data profiles. This method may lead to the identification of abnormalities from screening assays that might otherwise go undetected, or require additional testing to isolate.

Artificial Intelligence↗
PubMedDate not supplied

Neural network method to analyze data compression in DNA and RNA sequences.

Neural network computations on RNA sequences are used to demonstrate that data compression is possible in these sequences. The result implies that a certain discrimination should be achievable between structured vs random regions. The technique is illustrated by computing the compressibility of short RNA sequences such as tRNA. The method should be valuable in measuring the information content of DNA, including noncoding DNA, which has been shown to display certain properties resembling natural language attributes.

Base Sequence↗
PubMed2001

Classification of wheat varieties: use of two-dimensional gel electrophoresis for varieties that can not be classified by matrix assisted laser desorpiton/ionization-time of flight-mass spectrometry and an artificial neural network.

Analyzing a gliadin extract by matrix assisted laser desorption/ionization-time of flight-mass spectrometry (MALDI-TOF-MS) combined with an artificial neural network (ANN) is a suitable method for identification of wheat varieties. However, the ANN can not distinguish between all different wheat varieties. Two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) was applied to three pairs of wheat varieties, which can not be classified correctly by ANN. By 2-D PAGE the varieties in the three pairs can be discriminated and these six wheat varieties can be separated from each other, which could not be separated by MALDI-TOF-MS and NN.

Electrophoresis, Gel, Two-Dimensional↗
PubMed2005

Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity.

Analyzing the dependencies between spike trains is an important step in understanding how neurons work in concert to represent biological signals. Usually this is done for pairs of neurons at a time using correlation-based techniques. Chornoboy, Schramm, and Karr (1988) proposed maximum likelihood methods for the simultaneous analysis of multiple pair-wise interactions among an ensemble of neurons. One of these methods is an iterative, continuous-time estimation algorithm for a network likelihood model formulated in terms of multiplicative conditional intensity functions. We devised a discrete-time version of this algorithm that includes a new, efficient computational strategy, a principled method to compute starting values, and a principled stopping criterion. In an analysis of simulated neural spike trains from ensembles of interacting neurons, the algorithm recovered the correct connectivity matrices and interaction parameters. In the analysis of spike trains from an ensemble of rat hippocampal place cells, the algorithm identified a connectivity matrix and interaction parameters consistent with the pattern of conjoined firing predicted by the overlap of the neurons' spatial receptive fields. These results suggest that the network likelihood model can be an efficient tool for the analysis of ensemble spiking activity.

Action Potentials↗
PubMed1996

A neural network approach to analyzing health care information.

The article describes an investigation into the use of artificial neural networks for the analysis of health information. The Kohonen self-organizing map technique was used to group 39 European countries according to data extracted from the World Health Organization's Health for All database. The groups were seen to exhibit significantly different characteristics with respect to life expectancy, probability of dying before 5 years of age, infant mortality rate, standardized death rate (SDR) for diseases of the circulatory system, and SDR for external causes of injury and poisoning. Results obtained using the technique were subsequently confirmed by the use of traditional statistical tests.

Cardiovascular Diseases↗