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At least 523 records · Page 29Linked to original sources
PubMedDate not supplied

Refining competition in the self-organising tree map for unsupervised biofilm image segmentation.

The Self Organising Tree Map (SOTM) neural network is investigated as a means of segmenting micro-organisms from confocal microscope image data. Features describing pixel and regional intensities, phase congruency and spatial proximity are explored in terms of their impact on the segmentation of bacteria and other micro-organisms. The significance of individual features is investigated, and it is proposed that, within the context of micro-biological image segmentation, better object delineation can be achieved if certain features are more dominant in the initial stages of learning. In this way, other features are allowed to become more/less significant as learning progresses: as more knowledge is acquired about the data being segmented. We argue that the efficiency and flexibility of the SOTM in adapting to, and preserving the topology of input space, makes it an appropriate candidate for implementing this idea. We propose a refinement to the competitive search strategy that allows for a more appropriate fusion of signal and proximal features, thereby promoting a segmentation that is more sensitive to the regional associations of different microbial matter. A refined stop criterion is also suggested such that the dynamically generated number of classes becomes more data dependant. Preliminary experiments are presented and it is found that favouring intensity characteristics in the early phases of learning, whilst relaxing proximity constraints in later phases of learning, offers a general mechanism through which we can improve the segmentation of microbial constituents.

Algorithms↗
PubMedDate not supplied

Non-homogenous neural networks with chaotic recursive nodes: connectivity and multi-assemblies structures in recursive processing elements architectures.

This paper addresses recurrent neural architectures based on bifurcating nodes that exhibit chaotic dynamics, with local dynamics defined by first order parametric recursions. In the studied architectures, logistic recursive nodes interact through parametric coupling, they self organize, and the network evolves to global spatio-temporal period-2 attractors that encode stored patterns. The performance of associative memories arrangements is measured through the average error in pattern recovery, under several levels of prompting noise. The impact of the synaptic connections magnitude on architecture performance is analyzed in detail, through pattern recovery performance measures and basin of attraction characterization. The importance of a planned choice of the synaptic connections scale in RPEs architectures is shown. A strategy for minimizing pattern recovery degradation when the number of stored patterns increases is developed. Experimental results show the success of such strategy. Mechanisms for allowing the studied associative networks to deal with asynchronous changes in input patterns, and tools for the interconnection between different associative assemblies are developed. Finally, coupling in heterogeneous assemblies with diverse recursive maps is analyzed, and the associated synaptic connections are equated.

Algorithms↗
PubMedDate not supplied

Optimizing conductance parameters of cortical neural models via electrotonic partitions.

Development of automated methods for fitting computational models to observed biological data is an important challenge of neural modeling. Previous work has focused on generalized search techniques combined with distance measures tuned to specific neural morphologies. We propose general analysis techniques to guide construction of distance measures across a broader range of cell types. Specifically, we evaluate the use of multiple external stimuli to evoke characteristic behaviors of underlying active channel densities on a simple three-compartment model. We also examine the use of frequency analysis to smooth search space distortions induced by temporal shifts in recorded voltage traces. We propose a novel method of parameter optimization that is characterized by linear regression over the conductance densities using channel permissiveness as a basis of ionic current. We derive this method and demonstrate, given known anatomy and kinetics, it will solve all conductance densities in an N compartment model given N spatially distinct membrane potential traces with minimal error. We compare the regression method with the covariance matrix adaptation evolutionary strategy (CMA-ES) over a two-compartment cortical neuron and empirically show that regression over electrotonic partitions solves the cortical model near-optimally. We also show that electronic partitioning significantly improves search performance of CMA-ES on the cortical model.

Algorithms↗
PubMedDate not supplied

Bayesian approach to feature selection and parameter tuning for support vector machine classifiers.

A Bayesian point of view of SVM classifiers allows the definition of a quantity analogous to the evidence in probabilistic models. By maximizing this one can systematically tune hyperparameters and, via automatic relevance determination (ARD), select relevant input features. Evidence gradients are expressed as averages over the associated posterior and can be approximated using Hybrid Monte Carlo (HMC) sampling. We describe how a Nyström approximation of the Gram matrix can be used to speed up sampling times significantly while maintaining almost unchanged classification accuracy. In experiments on classification problems with a significant number of irrelevant features this approach to ARD can give a significant improvement in classification performance over more traditional, non-ARD, SVM systems. The final tuned hyperparameter values provide a useful criterion for pruning irrelevant features, and we define a measure of relevance with which to determine systematically how many features should be removed. This use of ARD for hard feature selection can improve classification accuracy in non-ARD SVMs. In the majority of cases, however, we find that in data sets constructed by human domain experts the performance of non-ARD SVMs is largely insensitive to the presence of some less relevant features. Eliminating such features via ARD then does not improve classification accuracy, but leads to impressive reductions in the number of features required, by up to 75%.

Algorithms↗
PubMed2005

Granular self-organizing map (grSOM) for structure identification.

This work presents a useful extension of Kohonen's Self-Organizing Map (KSOM) for structure identification in linguistic (fuzzy) system modeling applications. More specifically the granular SOM neural model is presented for inducing a distribution of nonparametric fuzzy interval numbers (FINs) from the data. A FIN can represent a local probability distribution function and/or a conventional fuzzy set; moreover, a FIN is interpreted as an information granule. Learning is based on a novel metric distance d(K)(.,.) between FINs. The metric d(K)(.,.) can be tuned nonlinearly by a mass function m(x), the latter attaches a weight of significance to a real number 'x' in a data dimension. Rigorous analysis is based on mathematical lattice theory. A grSOM can cope with ambiguity by processing linguistic (fuzzy) input data and/or intervals. This work presents a simple grSOM variant, namely greedy grSOM, for classification. A genetic algorithm (GA) introduces tunable nonlinearities during training. Extensive comparisons are shown with related work from the literature. The practical effectiveness of the greedy grSOM is demonstrated comparatively in three benchmark classification problems. Statistical evidence strongly suggests that the proposed techniques improve classification performance. In addition, the greedy grSOM induces descriptive decision-making knowledge (fuzzy rules) from the training data.

Fuzzy Logic↗
PubMed2005

Methods for reducing interference in the Complementary Learning Systems model: oscillating inhibition and autonomous memory rehearsal.

The stability-plasticity problem (i.e. how the brain incorporates new information into its model of the world, while at the same time preserving existing knowledge) has been at the forefront of computational memory research for several decades. In this paper, we critically evaluate how well the Complementary Learning Systems theory of hippocampo-cortical interactions addresses the stability-plasticity problem. We identify two major challenges for the model: Finding a learning algorithm for cortex and hippocampus that enacts selective strengthening of weak memories, and selective punishment of competing memories; and preventing catastrophic forgetting in the case of non-stationary environments (i.e. when items are temporarily removed from the training set). We then discuss potential solutions to these problems: First, we describe a recently developed learning algorithm that leverages neural oscillations to find weak parts of memories (so they can be strengthened) and strong competitors (so they can be punished), and we show how this algorithm outperforms other learning algorithms (CPCA Hebbian learning and Leabra at memorizing overlapping patterns. Second, we describe how autonomous re-activation of memories (separately in cortex and hippocampus) during REM sleep, coupled with the oscillating learning algorithm, can reduce the rate of forgetting of input patterns that are no longer present in the environment. We then present a simple demonstration of how this process can prevent catastrophic interference in an AB-AC learning paradigm.

Algorithms↗
PubMed2006

Building bridges between neural models and complex decision making behaviour.

Diffusion processes, and their discrete time counterparts, random walk models, have demonstrated an ability to account for a wide range of findings from behavioural decision making for which the purely algebraic and deterministic models often used in economics and psychology cannot account. Recent studies that record neural activations in non-human primates during perceptual decision making tasks have revealed that neural firing rates closely mimic the accumulation of preference theorized by behaviourally-derived diffusion models of decision making. This article bridges the expanse between the neurophysiological and behavioural decision making literatures specifically, decision field theory [Busemeyer, J. R. & Townsend, J. T. (1993). Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment. Psychological Review, 100, 432-459], a dynamic and stochastic random walk theory of decision making, is presented as a model positioned between lower-level neural activation patterns and more complex notions of decision making found in psychology and economics. Potential neural correlates of this model are proposed, and relevant competing models are also addressed.

Action Potentials↗
PubMed2004

Discrepancies between BOLD and flow dynamics in primary and supplementary motor areas: application of the balloon model to the interpretation of BOLD transients.

The blood-oxygen-level-dependent (BOLD) signal measured in the brain with functional magnetic resonance imaging (fMRI) during an activation experiment often exhibits pronounced transients at the beginning and end of the stimulus. Such transients could be a reflection of transients in the underlying neural activity, or they could result from transients in cerebral blood flow (CBF), cerebral metabolic rate of oxygen (CMRO2), or cerebral blood volume (CBV). These transients were investigated using an arterial spin labeling (ASL) method that allows simultaneous measurements of BOLD and CBF responses. Responses to a finger-tapping task (40-s stimulus, 80-s rest) were measured in primary motor area (M1) and supplementary motor area (SMA) in five healthy volunteers. In SMA, the average BOLD response was pronounced near the beginning and end of the stimulus, while in M1, the BOLD response was nearly flat. However, CBF responses in the two regions were rather similar, and did not exhibit the same transient features as the BOLD response in SMA. Because this suggests a hemodynamic rather than a neural origin for the transients of the BOLD response in SMA, we used a generalization of the balloon model to test the degree of hemodynamic transients required to produce the measured curves. Both data sets could be approximated with modest differences in the shapes of the CMRO2 and CBV responses. This study illustrates the utility and the limitations of using theoretical models combined with ASL techniques to understand the dynamics of the BOLD response.

Adult↗
PubMed2004

Cluster analysis in kinetic modelling of the brain: a noninvasive alternative to arterial sampling.

In emission tomography, quantification of brain tracer uptake, metabolism or binding requires knowledge of the cerebral input function. Traditionally, this is achieved with arterial blood sampling. We propose a noninvasive alternative via the use of a blood vessel time-activity curve (TAC) extracted directly from dynamic positron emission tomography (PET) scans by cluster analysis. Five healthy subjects were injected with the 5HT(2A)-receptor ligand [(18)F]-altanserin and blood samples were subsequently taken from the radial artery and cubital vein. Eight regions-of-interest (ROI) TACs were extracted from the PET data set. Hierarchical K-means cluster analysis was performed on the PET time series to extract a cerebral vasculature ROI. The number of clusters was varied from K = 1 to 10 for the second of the two-stage method. Determination of the correct number of clusters was performed by the 'within-variance' measure and by 3D visual inspection of the homogeneity of the determined clusters. The cluster-determined input curve was then used in Logan plot analysis and compared with the arterial and venous blood samples, and additionally with one of the currently used alternatives to arterial blood sampling, the Simplified Reference Tissue Model (SRTM) and Logan analysis with cerebellar TAC as an input. There was a good agreement (P < 0.05) between the values of Distribution Volume (DV) obtained from the K-means-clustered input function and those from the arterial blood samples. This work acts as a proof-of-principle that the use of cluster analysis on a PET data set could obviate the requirement for arterial cannulation when determining the input function for kinetic modelling of ligand binding, and that this may be a superior approach as compared to the other noninvasive alternatives.

Adult↗
PubMed2004

Functional MRI of auditory verbal working memory: long-term reproducibility analysis.

Although functional MRI (fMRI) has shown to be a tool with great potential to study the normal and diseased human brain, the large variability in the detected hemodynamic responses across sessions and across subjects hinders a wider application. To investigate the long-term reproducibility of fMRI activation of verbal working memory (WM), eight normal subjects performed an auditory version of the 2-back verbal WM task while fMRI images were acquired. The experiment was repeated nine times with the same settings for image acquisition and fMRI task. Data were analyzed using SPM99 program. Single-session activation maps and multi-subject session-specific activation maps were generated. Regions of interest (ROIs) associated to specific components of verbal WM were defined based on the voxels' coordinates in Talairach space. Visual observation of the multi-subject activation maps showed similar activation patterns, and quantitative analysis showed small coefficients of variance of activation within ROIs over time, suggesting small longitudinal variability of activation. Visual observation of the activation maps of individual sessions demonstrated striking variation of activation across sessions and across subjects, and quantitative analysis demonstrated larger contribution from between-subject variation to overall variation than that from within-subject variation. We concluded that by multi-subject analysis of data from a relatively small number of subjects, reasonably reproducible activation for the 2-back verbal WM paradigm can be achieved. The level of reproducibility encourages the application of this fMRI paradigm to the evaluation of cognitive changes in future investigations. The quantitative estimation of the proportions of within-subject and between-subject variabilities in the overall variability may be helpful for the design of future studies.

Acoustic Stimulation↗
PubMed2004

Noninvasive in vivo demonstration of the connections of the human parahippocampal gyrus.

MR tractography techniques provide a method for noninvasively studying white matter pathways in vivo. In this study we have used diffusion tensor imaging (DTI) and the fast marching tractography (FMT) algorithm to plot the structural connectivity of the human parahippocampal gyrus (PHG) in 10 healthy subjects, using seed points selected in the anterior parahippocampal gyrus. Our results demonstrate connectivity between the parahippocampal gyrus and the anterior temporal lobe, orbitofrontal areas, posterior temporal lobe and extrastriate occipital lobe via the lingual and fusiform gyri. We also demonstrate for the first time noninvasively direct connectivity between the parahippocampal gyrus and the hippocampus itself. These results agree with previous histological tract-tracing studies in animals. The connections demonstrated between neocortical areas and the hippocampus via the parahippocampal gyrus may provide the structural basis for theoretical models of memory and higher visual processing.

Adult↗
PubMed2004

A voxel-based approach to gray matter asymmetries.

Voxel-based morphometry (VBM) was used to analyze gray matter (GM) asymmetries in a large sample (n = 60) of male and female professional musicians with and without absolute pitch (AP). We chose to examine these particular groups because previous studies using traditional region-of-interest (ROI) analyses have shown differences in hemispheric asymmetry related to AP and gender. Voxel-based methods may have advantages over traditional ROI-based methods since the analysis can be performed across the whole brain with minimal user bias. After determining that the VBM method was sufficiently sensitive for the detection of differences in GM asymmetries between groups, we found that male AP musicians were more leftward lateralized in the anterior region of the planum temporale (PT) than male non-AP musicians. This confirmed the results of previous studies using ROI-based methods that showed an association between PT asymmetry and the AP phenotype. We further observed that male non-AP musicians revealed an increased leftward GM asymmetry in the postcentral gyrus compared to female non-AP musicians, again corroborating results of a previously published study using ROI-based methods. By analyzing hemispheric GM differences across our entire sample, we were able to partially confirm findings of previous studies using traditional morphometric techniques, as well as more recent, voxel-based analyses. In addition, we found some unusually pronounced GM asymmetries in our musician sample not previously detected in subjects unselected for musical training. Since we were able to validate gender- and AP-related brain asymmetries previously described using traditional ROI-based morphometric techniques, the results of our analyses support the use of VBM for examinations of GM asymmetries.

Brain↗
PubMed2004

Detection and detrending in fMRI data analysis.

This article addresses the impact that colored noise, temporal filtering, and temporal detrending have on the fMRI analysis situation. Specifically, it is shown why the detection of event-related designs benefit more from pre-whitening than blocked designs in a colored noise structure. Both theoretical and empirical results are provided. Furthermore, a novel exploratory method for producing drift models that efficiently capture trends and drifts in the fMRI data is introduced. A comparison to currently employed detrending approaches is presented. It is shown that the novel exploratory model is able to remove a major part of the slowly varying drifts that are abundant in fMRI data. The value of such a model lies in its ability to remove drift components that otherwise would have contributed to a colored noise structure in the voxel time series.

Brain↗
PubMed2004

Nonstationary cluster-size inference with random field and permutation methods.

Because of their increased sensitivity to spatially extended signals, cluster-size tests are widely used to detect changes and activations in brain images. However, when images are nonstationary, the cluster-size distribution varies depending on local smoothness. Clusters tend to be large in smooth regions, resulting in increased false positives, while in rough regions, clusters tend to be small, resulting in decreased sensitivity. Worsley et al. proposed a random field theory (RFT) method that adjusts cluster sizes according to local roughness of images [Worsley, K.J., 2002. Nonstationary FWHM and its effect on statistical inference of fMRI data. Presented at the 8th International Conference on Functional Mapping of the Human Brain, June 2-6, 2002, Sendai, Japan. Available on CD-ROM in NeuroImage 16 (2) 779-780; Hum. Brain Mapp. 8 (1999) 98]. In this paper, we implement this method in a permutation test framework, which requires very few assumptions, is known to be exact [J. Cereb. Blood Flow Metab. 16 (1996) 7] and is robust [NeuroImage 20 (2003) 2343]. We compared our method to stationary permutation, stationary RFT, and nonstationary RFT methods. Using simulated data, we found that our permutation test performs well under any setting examined, whereas the nonstationary RFT test performs well only for smooth images under high df. We also found that the stationary RFT test becomes anticonservative under nonstationarity, while both nonstationary RFT and permutation tests remain valid under nonstationarity. On a real PET data set we found that, though the nonstationary tests have reduced sensitivity due to smoothness estimation variability, these tests have better sensitivity for clusters in rough regions compared to stationary cluster-size tests. We include a detailed and consolidated description of Worsley nonstationary RFT cluster-size test.

Brain↗
PubMed2004

Singularities in diffusion tensor fields and their relevance in white matter fiber tractography.

The technique of diffusion tensor tractography utilizes directions of maximum diffusion to reconstruct pathways of white matter structures in the brain. Critically, successful tracking of these white matter pathways depends on well-defined maximal diffusion directional information. By examination of diffusion tensor field properties in the human brain, we demonstrate that the geometry of tracked pathways is influenced by points in the field where the maximum diffusion direction is poorly defined. In common with tensor fields describing other mathematical and physical phenomena, such as 3D surface differential geometry and gravitational fields, we refer to these points as singularities. Here we describe an automated procedure for detecting singularities and demonstrate that these occur where there is (i) fiber crossing, (ii) pathways passing close to one another within a voxel (partial volume effect), and (iii) noise propagation into low anisotropy regions. In order to highlight the relevance of singularities in tracking white matter structures, we determined their effect on computation of the cortico-spinal pathway.

Algorithms↗
PubMed2004

Estimation and classification of fMRI hemodynamic response patterns.

In this paper, we propose an approach to modeling functional magnetic resonance imaging (fMRI) data that combines hierarchical polynomial models, Bayes estimation, and clustering. A cubic polynomial is used to fit the voxel time courses of event-related design experiments. The coefficients of the polynomials are estimated by Bayes estimation, in a two-level hierarchical model, which allows us to borrow strength from all voxels. The voxel-specific Bayes polynomial coefficients are then transformed to the times and magnitudes of the minimum and maximum points on the hemodynamic response curve, which are in turn used to classify the voxels as being activated or not. The procedure is demonstrated on real data from an event-related design experiment of visually guided saccades and shown to be an effective alternative to existing methods.

Bayes Theorem↗
PubMed2004

An empirical investigation into the number of subjects required for an event-related fMRI study.

Optimising the number of subjects required for an event-related functional imaging study is critical for ensuring sufficient statistical power. We report an empirical investigation of this issue by employing a resampling approach to the data of 58 subjects drawn from four previous GO/NOGO studies. Using voxelwise measures and setting the activation map from the complete sample to be a "gold standard", analyses revealed the statistical power to be surprisingly low at typical sample sizes (n = 20). However, voxels that were significantly active from smaller samples tended to be true positives, that is, they were typically active in the gold standard map and correlated well with the gold standard activation measure. The numerous false negatives that resulted from the lower SNR of the smaller samples drove the poor statistical power of those samples. Splitting the sample into two groups provided a test of the reproducibility of activation maps that was assessed using an alternative measure that quantified the distances between centres-of-mass of activated areas. These analyses revealed that although the voxelwise overlap may be poor, the locations of activated areas provide some optimism for studies with typical sample sizes. With n = 20 in each of two groups, it was found that the centres-of-mass for 80% of activated areas fell within 25 mm of each other. The reported analyses, by quantifying the spatial reproducibility for various sample sizes performing a typical event-related cognitive task, thus provide an empirical measure of the disparity to be expected in comparing activation maps.

Adult↗