PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Neural Networks, Computer”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 379 records · Page 21Linked to original sources
PubMed2001

The callosal dilemma: explaining diaschisis in the context of hemispheric rivalry via a neural network model.

It is often suggested that a major factor in diaschisis is the loss of transcallosal excitation to the intact hemisphere from the lesioned one. However, there is long-standing disagreement in the broader experimental literature about whether transcallosal interhemispheric influences in the human brain are primarily excitatory or inhibitory. Some experimental data are apparently better explained by assuming inhibitory callosal influences. Past neural network models attempting to explore this issue have encountered the same dilemma: in intact models, inhibitory callosal influences best explain strong cerebral lateralization like that occurring with language, but in lesioned models, excitatory callosal influences best explain experimentally observed hemispheric activation patterns following brain damage. We have now developed a single neural network model that can account for both types of data, i.e., both diaschisis and strong hemisphere specialization in the normal brain, by combining excitatory callosal influences with subcortical cross-midline inhibitory interactions. The results suggest that subcortical competitive processes may be a more important factor in cerebral specialization than is generally recognized.

Brain Injuries↗
PubMed2001

On the inter-relations between artificial and physiological neural networks.

This paper discusses the inter-relations between findings on the physiological neural network (PNN) and artificial neural networks (ANN). It discusses the interaction of progress in both PNN and ANN for the purpose of borrowing from ANN's mathematical understandings to establish pointers for further explorations to better understand the PNN, and also for the reciprocal transferring of knowledge from PNN findings to improve ANN schemes. Such improvements in ANN are essential for better handling the needs of the information technology (IT) explosion in dealing with huge data bases and where data often defy analysis and are incomplete and fuzzy. On the other hand, principles and elements of ANN designs that appear to be important and successful can serve as guides for identifying them in the PNN, to be subsequently confirmed by bioanalytical tests. Hence progress in PNN is obviously essential for progress in ANN, as is progress in ANN helpful in PNN modeling, though its laboratory confirmation is still a far lengthier process. We discuss certain specific ANN schemes with respect to the above inter-relations with PNN. We feel that the progress in both PNN and ANN research provides a major link between the thrust in information technology developments and the thrust in biological science research, which are most probably the two major focus areas of research at the dawn of the 21st century.

Animals↗
PubMed2004

Postnatal development of prefrontal inhibitory circuits and the pathophysiology of cognitive dysfunction in schizophrenia.

The typical appearance of the clinical features of schizophrenia during late adolescence or early adulthood suggests that adolescence-related neurodevelopmental events may contribute to the pathophysiology of this disorder. Here the role that GABA-mediated inhibition in the dorsal lateral prefrontal cortex (DLPFC) plays in regulating working memory, a core cognitive process that matures late and that is disturbed in schizophrenia, is reviewed. Recent studies are summarized that demonstrate (1) that certain pre- and postsynaptic markers of GABA neurotransmission in the monkey DLPFC exhibit striking changes during adolescence, and (2) that these same markers are markedly altered in the DLPFC of subjects with schizophrenia. The implications of these findings for treatment and prevention strategies are discussed.

Adolescent↗
PubMed2004

Fair localization of function via multi-lesion analysis.

Acknowledging that causal localization of function in a processing network requires a multi-lesion analysis, this paper presents a rigorous and efficient method for defining and calculating the functional contributions of network elements as well as their interactions. The method's applicability to biological networks is demonstrated in the investigation of spatial attention in cats via lesion and reversible deactivation experiments.

Acoustic Stimulation↗
PubMed2004

Connectivity and dynamics of neural information processing.

In this article, we systematically review the current literature on neural connectivity and dynamics, or equivalently, structure and function. In particular, we discuss how changes in the connectivity of a neural network affect the spatiotemporal network dynamics qualitatively. The three major criteria of comparison are, first, the local dynamics at the network nodes which includes fixed point dynamics, oscillatory and chaotic dynamics; second, the presence of time delays via propagation along connecting pathways; and third, the properties of the connectivity matrix such as its statistics, symmetry, and translational invariance. Since the connection topology changes when anatomical scales are traversed, so will the corresponding network dynamics change. As a consequence different types of networks are encountered on different levels of neural organization.

Animals↗
PubMed2004

"Dynamic" connectivity in neural systems: theoretical and empirical considerations.

The study of functional interdependences between brain regions is a rapidly growing focus of neuroscience research. This endeavor has been greatly facilitated by the appearance of a number of innovative methodologies for the examination of neurophysiological and neuroimaging data. The aim of this article is to present an overview of dynamical measures of interdependence and contrast these with statistical measures that have been more widely employed. We first review the motivation, conceptual basis, and experimental approach of dynamical measures of interdependence and their application to the study of neural systems. A consideration of boot-strap "surrogate data" techniques, which facilitate hypothesis testing of dynamical measures, is then used to clarify the difference between dynamical and statistical measures of interdependence. An overview of some of the most active research areas such as the study of the "synchronization manifold," dynamical interdependence in neurophysiology data and the putative role of nonlinear desynchronization is then given. We conclude by suggesting that techniques based on dynamical interdependence--or "dynamical connectivity"--show significant potential for extracting meaningful information from functional neuroimaging data.

Brain↗
PubMed2004

Relating fMRI and PET signals to neural activity by means of large-scale neural models.

This article reviews the four ways by which large-scale, neurobiologically realistic modeling can be used in conjunction with functional neuroimaging data, especially that obtained by functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), to help investigators understand the neural bases for sensorimotor and cognitive functions. The conceptually distinct purposes served are:(1) formulating and implementing specific hypotheses about how neuronal populations mediate a task, which will be illustrated using models of visual and auditory object processing; (2) determining how well an experimental design paradigm or analysis method works, which will be illustrated by examining event-related fMRI; (3) investigating the meaning in neural terms of macro-level concepts, which will be illustrated using functional connectivity; and (4) combining different types of macroscopic data with one another, which will be illustrated using transcranial magnetic stimulation (TMS) and PET.

Brain↗
PubMed2005

Signal propagation and logic gating in networks of integrate-and-fire neurons.

Transmission of signals within the brain is essential for cognitive function, but it is not clear how neural circuits support reliable and accurate signal propagation over a sufficiently large dynamic range. Two modes of propagation have been studied: synfire chains, in which synchronous activity travels through feedforward layers of a neuronal network, and the propagation of fluctuations in firing rate across these layers. In both cases, a sufficient amount of noise, which was added to previous models from an external source, had to be included to support stable propagation. Sparse, randomly connected networks of spiking model neurons can generate chaotic patterns of activity. We investigate whether this activity, which is a more realistic noise source, is sufficient to allow for signal transmission. We find that, for rate-coded signals but not for synfire chains, such networks support robust and accurate signal reproduction through up to six layers if appropriate adjustments are made in synaptic strengths. We investigate the factors affecting transmission and show that multiple signals can propagate simultaneously along different pathways. Using this feature, we show how different types of logic gates can arise within the architecture of the random network through the strengthening of specific synapses.

Action Potentials↗
PubMed2000

Effects of delay on the type and velocity of travelling pulses in neuronal networks with spatially decaying connectivity.

We study a one-dimensional model of integrate-and-fire neurons that are allowed to fire only one spike, and are coupled by excitatory synapses with delay. At small delay values, this model describes a disinhibited cortical slice. At large delay values, the model is a reduction of a model of thalamic networks composed of excitatory and inhibitory neurons, in which the excitatory neurons show the post-inhibitory rebound mechanism. The velocity and stability of propagating continuous pulses are calculated analytically. Two pulses with different velocities exist if the synaptic coupling is larger than a minimal value; the pulse with the lower velocity is always unstable. Above a certain critical value of the constant delay, continuous pulses lose stability via a Hopf bifurcation, and lurching pulses emerge. The parameter regime for which lurching occurs is strongly affected by the synaptic footprint (connectivity) shape. A bistable regime, in which both continuous and lurching pulses can propagate. may occur with square or Gaussian footprint shapes but not with an exponential footprint shape. A perturbation calculation is used in order to calculate the spatial lurching period and the velocity of lurching pulses at large delay values. For strong synaptic coupling, the velocity of the lurching pulse is governed by the tail of the synaptic footprint shape. Moreover, the velocities of continuous and lurching pulses have the same functional dependencies on the strength of the synaptic coupling strength gsyn: they increase logarithmically with gsyn for an exponential footprint shape, they scale like (In gsyn)1/2 for a Gaussian footprint shape, and they are bounded for a square footprint shape or any shape with a finite support. We find analytically how the axonal propagation velocity reduces the velocity of continuous pulses; it does not affect the critical delay. We conclude that the differences in velocity and shape between the front of thalamic spindle waves in vitro and cortical paroxysmal discharges stem from their different effective delays.

Algorithms↗
PubMed2003

A preprocessing method for improving data mining techniques. Application to a large medical diabetes database.

The Knowledge Discovery in Databases (KDD) methodology seems to be attractive on the analyze of large clinical databases. In the KDD process, the preprocessing step (data cleaning and handling of missing values) is paramount since it conditions the quality of the results obtained by data mining procedures and represents about 80% of the whole project time. The aims of the present study were to analyze this step and provide tools to handle inconsistent data and missing values. We have broken down the process into 3 main stages: data cleaning--explanatory study of missing values--choice of the procedure used for handling missing values. The data cleaning stage was based on a system of logical rules to correct mistakes and on cluster analysis to discard the poorly filled files. The missing-data mechanism was analyzed by means of multivariate statistical procedures. Two methods to deal with missing values were compared: imputation by the most common value (mode) and imputation using decision trees. This study was performed on a large medical diabetes database (23,601 patients) including numerous missing values. A system of logical rules allowed to correct mistakes on essential parameters (for example, the type of diabetes). Cluster analysis allowed to identify 10% of poorly filled files. After multivariate analysis, the missing-data mechanism could be considered as random. For variables with low number of missing values (< 10%) and categories (< 4), imputation using decision trees provided better results than imputation by mode.

Data Interpretation, Statistical↗
PubMedDate not supplied

[Long-latency responses to conditioned stimulus in the cat motor cortex can be generated through the enhancement of efficiency excitatory collaterals of pyramidal neuron].

The long-latency excitatory components are the characteristic feature of neuronal responses to conditional stimuli in the motor cortex of the cat. The data presented suggest that the neuronal machine that generates these reactions is that, generating long-latency epileptiform discharges in epileptogenic cortex. The long-latency component generation is based on NMDA-receptor activation in the recurrent excitatory collaterals of the cortical pyramidal neurons. The response delay is dependent on initial activation of inhibitory GABA(A) receptors. The emergence of the late components in the course of motor learning take place as a result of efficiency enhancement of recurrent collaterals synaptic linkage with pyramidal neurons.

Animals↗
PubMed1993

Deviation code is a prospective candidate of the communication between adapting neurons.

The low Firing Rate (FR) of pyramidal cells (Thorpe et al., 1989, Pettigrew et al., 1968) prevents the use of rate coding. The Deviation Code (DC), in which the deviation from the mean is the signal, is a candidate code that may be used in neurobiological and formal neural networks. The neurons which use this code could have similar properties as the model neurons frequently used by the neural network (NN) theories. The biological verification of this type of coding is required. This coding might be relevant in the physiological investigation of learning rules and can help to understand the organization of different nuclei of the nervous system.

Adaptation, Physiological↗
PubMed1995

Beyond computer-based clinical reminders: improvement of the total service quality by small-group based organizational learning in primary care.

In order to support continuous development of the services in a patient-centered primary care organization, a Computer-Supported Co-operative Work (CSCW) system has been developed. The system structures and supports organizational learning as a daily activity in parallel to clinical work. It is based on coordinated small-group activities in which strategic planning is integrated with monitoring and change of patient management. Implications for the future role of information systems in primary care are discussed.

Ambulatory Care Information Systems↗