Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
66
Artificial Neural Networks
"... Artificial neural networks' are machines (or models of computation) based loosely on the ways in which the brain is believed to work. In this chapter, we discuss some links between graph theory and artificial neural networks. We describe how some combinatorial optimisation tasks may be approach ..."
Abstract
- Add to MetaCart
be approached by using a type of artificial neural network known as a Boltzmann machine. We then focus on `learning' in feedforward artificial neural networks, explaining how the graph structure of a network and the hardness of graph-colouring quantify the complexity of learning.
Commentary Use of artificial neural network in diagnostic pathology
"... Histopathology is almost entirely based on subjec-tive human interpretation of visual images. The dominant role of human interpretation is explained by the complex-ity of the images seen down the microscope and the very efficient processing of this information by the human brain. Evolution has produ ..."
Abstract
- Add to MetaCart
produced Homo sapiens with high resolution vision and a large biological neural network to interpret those data. Pathologists have been trying for some time to combine their human skills in histopatho-logical diagnosis with the advantages offered by comput-er systems. Expert systems are computer programs
Healthy and Pathological Cerebellar Spiking Neural Networks in Vestibulo-Ocular Reflex
"... Abstract -Since the Marr-Albus model, computational neuroscientists have been developing a variety of models of the cerebellum, with different approaches and features. In this work, we developed and tested realistic artificial Spiking Neural Networks inspired to this brain region. We tested in comp ..."
Abstract
- Add to MetaCart
Abstract -Since the Marr-Albus model, computational neuroscientists have been developing a variety of models of the cerebellum, with different approaches and features. In this work, we developed and tested realistic artificial Spiking Neural Networks inspired to this brain region. We tested
Activity dependent degeneration explains hub vulnerability in Alzheimer’s disease,” PLoS
- Article ID e1002582
, 2012
"... Brain connectivity studies have revealed that highly connected ‘hub ’ regions are particularly vulnerable to Alzheimer pathology: they show marked amyloid-b deposition at an early stage. Recently, excessive local neuronal activity has been shown to increase amyloid deposition. In this study we use a ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
a computational model to test the hypothesis that hub regions possess the highest level of activity and that hub vulnerability in Alzheimer’s disease is due to this feature. Cortical brain regions were modeled as neural masses, each describing the average activity (spike density and spectral power
Aggregating Vulnerability Metrics in Enterprise Networks using Attack Graphs
"... Quantifying security risk is an important and yet difficult task in enterprise network security management. While metrics exist for individual software vulnerabilities, there is currently no standard way of aggregating such metrics. We present a model that can be used to aggregate vulnerability metr ..."
Abstract
-
Cited by 6 (5 self)
- Add to MetaCart
Quantifying security risk is an important and yet difficult task in enterprise network security management. While metrics exist for individual software vulnerabilities, there is currently no standard way of aggregating such metrics. We present a model that can be used to aggregate vulnerability
Pathogenesis of Schizophrenic Delusions and Hallucinations: A Neural Model
- Schizophrenia Bulletin
, 1995
"... We implement and study a computational model of Stevens' [1992] theory of the pathogenesis of schizophrenia. This theory hypothesizes that the onset of schizophrenia is associated with reactive synaptic regeneration occurring in brain regions receiving degenerating temporal lobe projections. Co ..."
Abstract
-
Cited by 9 (0 self)
- Add to MetaCart
. Concentrating on one such area, the frontal cortex, we model a frontal module as an associative memory neural network whose input synapses represent incoming temporal projections. Modeling Stevens' hypothesized pathological synaptic changes in this framework results in adverse side effects reminiscent
Pathological neural attractor dynamics in slowly growing gliomas supports an optimal time frame for white matter plasticity. PLoS One (2013
"... Neurological function in patients with slowly growing brain tumors can be preserved even after extensive tumor resection. However, the global process of cortical reshaping and cerebral redistribution cannot be understood without taking into account the white matter tracts. The aim of this study was ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
of the connectivity were altered, mimicking the biological heterogeneity of gliomas. The network performance was quantified by comparing memory pattern recall and the plastic compensatory capacity of the network was analyzed. The model predicts an optimal level of synaptic conductance boost that compensates for tumor
Graph analysis and visualization for brain function characterization using EEG data
- J. Healthcare Eng
, 2010
"... ABSTRACT Over the past few years, there has been an increased interest in studying the underlying neural mechanism of cognitive brain activity as well as in diagnosing certain pathologies. Noninvasive imaging modalities such as functional magnetic resonance imaging (fMRI), positron emission tomogra ..."
Abstract
-
Cited by 2 (1 self)
- Add to MetaCart
of capturing and quantifying its structure and summarizing the information that it contains. Finally, graph visualization reveals the hidden structure of the networks and amplifies human understanding. A number of possible applications of the graph theoretic approach are also listed. A freely available
Pathogenesis of schizophrenic delusions and hallucinations: a neural model
- Schizophrenia Bulletin
, 1996
"... We implement and study a computational model of Stevens ' [1992] theory of the pathogenesis of schizophrenia. This theory hypothesizes that the onset of schizophrenia is associated with reactive synaptic regeneration occurring in brain regions receiving degenerating tem-poral lobe projections. ..."
Abstract
-
Cited by 3 (1 self)
- Add to MetaCart
. Concentrating on one such area, the frontal cortex, we model a frontal module as an associative memory neural network whose input synapses represent incoming temporal projec-tions. Modeling Stevens ' hypothesized pathological synaptic changes in this framework results in adverse side eects reminiscent
Using computational models to relate structural and functional brain connectivity
- Eur J Neurosci. 2011
"... Modern imaging methods allow a non-invasive assessment of both structural and functional brain connectivity. This has lead to the identification of disease-related alterations affecting functional connectivity. The mechanism of how such alterations in functional connectivity arise in a structured ne ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
calculated graph–theoretic measures of functional network topology from numerical simulations of model networks. The effect of the form of local dynamics on the observed network state is quantified by examining the correlation between structural and functional connectivity. We document a profound
Results 1 - 10
of
66
