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PubMed2006

Task and content modulate amygdala-hippocampal connectivity in emotional retrieval.

The ability to remember emotional events is crucial for adapting to biologically and socially significant situations. Little is known, however, about the nature of the neural interactions supporting the integration of mnemonic and emotional information. Using fMRI and dynamic models of effective connectivity, we examined regional neural activity and specific interactions between brain regions during a contextual memory retrieval task. We independently manipulated emotional context and relevance of retrieved emotional information to task demands. We show that retrieval of emotionally valenced contextual information is associated with enhanced connectivity from hippocampus to amygdala, structures crucially involved with encoding of emotional events. When retrieval of emotional information is relevant to current behavior, amygdala-hippocampal connectivity increases bidirectionally, under modulatory influences from orbitofrontal cortex, a region implicated in representation of affective value and behavioral guidance. Our findings demonstrate that both memory content and behavioral context impact upon large scale neuronal dynamics underlying emotional retrieval.

Amygdala↗
PubMed2007

Realistically coupled neural mass models can generate EEG rhythms.

We study the generation of EEG rhythms by means of realistically coupled neural mass models. Previous neural mass models were used to model cortical voxels and the thalamus. Interactions between voxels of the same and other cortical areas and with the thalamus were taken into account. Voxels within the same cortical area were coupled (short-range connections) with both excitatory and inhibitory connections, while coupling between areas (long-range connections) was considered to be excitatory only. Short-range connection strengths were modeled by using a connectivity function depending on the distance between voxels. Coupling strength parameters between areas were defined from empirical anatomical data employing the information obtained from probabilistic paths, which were tracked by water diffusion imaging techniques and used to quantify white matter tracts in the brain. Each cortical voxel was then described by a set of 16 random differential equations, while the thalamus was described by a set of 12 random differential equations. Thus, for analyzing the neuronal dynamics emerging from the interaction of several areas, a large system of differential equations needs to be solved. The sparseness of the estimated anatomical connectivity matrix reduces the number of connection parameters substantially, making the solution of this system faster. Simulations of human brain rhythms were carried out in order to test the model. Physiologically plausible results were obtained based on this anatomically constrained neural mass model.

Brain Mapping↗
PubMedDate not supplied

Prediction of anti-HIV-1 activity of a series of tetrapyrrole molecules.

Anti-HIV-1 activities of 20 tetrapyrroles (hematoporphyrin derivatives, meso-tetraphenylporphyrins, a chlorin, and a phthalocyanine) were predicted based on their molecular structures using artificial neural networks. The molecular structures were optimized by HyperChem program using MM+ molecular mechanics and conformational search for the global minimum conformer. Eighty-seven theoretical descriptors were calculated for characterization of molecular structures. The network architecture was optimized, and suitable descriptors were selected applying a novel variable selection method. The 3DNET program was used for the calculation of descriptors and for neural network computations. The reliability of models was tested by randomization of biological activity data, leave-one-out, leave-n-out cross-validation, and external validation process. The predictive ability of the artificial neural network was compared to other model building methods, like multiple linear regressions and partial least squares projection to latent structures. For prediction of anti-HIV-1 activity, the artificial neural network gave the best results at cross-validation processes and at external validation as well. We built four nonlinear models with good predictive ability in all validation steps, which can be applied to predict the anti-HIV-1 activity of tetrapyrrole-type compounds in a much better way than with any other three-dimensional quantitative structure-activity relationship methods published to date.

Algorithms↗
PubMedDate not supplied

Subfield variations in hippocampal processing-components of a spatial navigation system.

The hippocampus is a part of the brain strongly linked to spatial exploration. Within it exist 'place cells' which fire preferentially when an animal is in certain regions of physical space. Recent research has shown that these place cells and their corresponding representations of space behave differently in the CA3 and CA1 subfields of the hippocampus. We review this research and show, by simulation, that these differences can be explained by a combination of known physiological features of the hippocampus and proposed variations in the rate of synaptic plasticity and connection strength between different information pathways. We suggest possible reasons for these differences, namely use of the CA1 cell field for current spatial exploration, and CA3 for longer term spatial memory.

Algorithms↗
PubMed2006

Mirror neurons and imitation: a computationally guided review.

Neurophysiology reveals the properties of individual mirror neurons in the macaque while brain imaging reveals the presence of 'mirror systems' (not individual neurons) in the human. Current conceptual models attribute high level functions such as action understanding, imitation, and language to mirror neurons. However, only the first of these three functions is well-developed in monkeys. We thus distinguish current opinions (conceptual models) on mirror neuron function from more detailed computational models. We assess the strengths and weaknesses of current computational models in addressing the data and speculations on mirror neurons (macaque) and mirror systems (human). In particular, our mirror neuron system (MNS), mental state inference (MSI) and modular selection and identification for control (MOSAIC) models are analyzed in more detail. Conceptual models often overlook the computational requirements for posited functions, while too many computational models adopt the erroneous hypothesis that mirror neurons are interchangeable with imitation ability. Our meta-analysis underlines the gap between conceptual and computational models and points out the research effort required from both sides to reduce this gap.

Animals↗
PubMed2006

Parallel and distributed neural models of the ideomotor principle: an investigation of imitative cortical pathways.

Humans' capacity to imitate has been extensively investigated through a wide-range of behavioral and developmental studies. Yet, despite the huge amount of phenomenological evidence gathered, we are still unable to relate this behavioral data to any specific neural substrate. In this paper, we investigate how principles from psychology can be the result of neural computations and therefore attempt to bridge the gap between monkey neurophysiology and human behavioral data, and hence between these two complementary disciplines. Specifically, we address the principle of ideomotor compatibility, by which 'observing the movements of others influences the quality of one's own performance' and develop two neural models which account for a set of related behavioral studies [Brass, M., Bekkering, H., Wohlschläger, A., & Prinz, W. (2000). Compatibility between observed and executed finger movements: comparing symbolic, spatial and imitative cues. Brain and Cognition 44, 124-143]. We show that the ideomotor effect could be the result of two distinct cognitive pathways, which can be modeled by means of biologically plausible neural architectures. Furthermore, we propose a novel behavioral experiment to confirm or refute either of the two model pathways.

Animals↗
PubMed1999

Cognition and control in schizophrenia: a computational model of dopamine and prefrontal function.

Behavioral deficits suffered by patients with schizophrenia in a wide array of cognitive domains can be conceptualized as failures of cognitive control, due to an impaired ability to internally represent, maintain, and update context information. A theory is described that postulates a single neurobiological mechanism for these disturbances, involving dysfunctional interactions between the dopamine neurotransmitter system and the prefrontal cortex. Specifically, it is hypothesized that in schizophrenia, there is increased noise in the activity of the dopamine system, leading to abnormal "gating" of information into prefrontal cortex. The theory is implemented as an explicit connectionist computational model that incorporates the roles of both dopamine and prefrontal cortex in cognitive control. A simulation is presented of behavioral performance in a version of the Continuous Performance Test specifically adapted to measure critical aspects of cognitive control function. Schizophrenia patients exhibit clear behavioral deficits on this task that reflect impairments in both the maintenance and updating of context information. The simulation results suggest that the model can successfully account for these impairments in terms of abnormal dopamine activity. This theory provides a potential point of contact between research on the neurobiological and psychological aspects of schizophrenia, by illustrating how a particular physiological disturbance might lead to precise and quantifiable consequences for behavior.

Cognition Disorders↗
PubMed2005

Computation in the olfactory system.

Computational models are increasingly essential to systems neuroscience. Models serve as proofs of concept, tests of sufficiency, and as quantitative embodiments of working hypotheses and are important tools for understanding and interpreting complex data sets. In the olfactory system, models have played a particularly prominent role in framing contemporary theories and presenting novel hypotheses, a role that will only grow as the complexity and intricacy of experimental data continue to increase. This review will attempt to provide a comprehensive, functional overview of computational ideas in olfaction and outline a computational framework for olfactory processing based on the insights provided by these diverse models and their supporting data.

Action Potentials↗
PubMed2001

Marker-less systems for tracking working postures--results from two experiments.

Two experiments are performed to examine the usability of different marker-less approaches in image analysis and computer vision for automatic registration of OWAS (Ovako working posture analysing system) postures from video film. In experiment 1, a parametric method based on image analysis routines is developed both for separating the subject from its background and for relating the shapes of the extracted subject to OWAS postures. All 12 analysed images were correctly classified by the method. In experiment 2 a computer neural network is taught to relate postures of a subject to OWAS postures. When the network was trained with 53 images the rest of the set of 138 images was correctly classified. The experiments described in this paper show promising results regarding the use of image analysis and computer vision for tracking and assessing working postures. However, further research is needed including tests of different human models, neural networks, and template matching for making the OWAS method more useful in identifying and evaluating potentially harmful working postures.

Humans↗
PubMed1993

Neural strategies to handle routing in computer networks.

In this paper, the authors adopt a neural approach to deal with the problem of routing in a packet switching network. The aim is to define a routing strategy which will combine the advantages of both the centralized and the distributed approaches. The neural approach presented is based on the idea of inserting a neural network (N/N) into each node in the computer network which will be responsible for computing the route between its node and the immediately adjacent one. Two distributed routing solutions are presented in the paper based on an optimizing network and a mapping network. The routing obtainable and the implementation resources needed for the two approaches are evaluated. Finally, the performance offered by the neural strategies proposed is compared with that offered by classical distributed and centralized routing solutions. As a parameter of merit, the effect of overloading caused by the additional traffic present in each solution is used.

Algorithms↗
PubMed1999

Artificial neural networks for computer-aided modelling and optimisation in micellar electrokinetic chromatography.

The separation process in capillary micellar electrochromatography (MEKC) can be modelled using artificial neural networks (ANNs) and optimisation of MEKC methods can be facilitated by combining ANNs with experimental design. ANNs have shown attractive possibilities for non-linear modelling of response surfaces in MEKC and it was demonstrated that by combining ANN modelling with experimental design, the number of experiments necessary to search and find optimal separation conditions can be reduced significantly. A new general approach for computer-aided optimisation in MEKC has been proposed which, because of its general validity, can also be applied in other separation techniques.

Chromatography, Micellar Electrokinetic Capillary↗
PubMed2006

Distributed computing methodology for training neural networks in an image-guided diagnostic application.

Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used.

Algorithms↗
PubMed1994

Artificial neural networks in computer-assisted classification of heart sounds in patients with porcine bioprosthetic valves.

The paper describes the design, training and testing of a three-layer feed-forward back-propagation neural network for the classification of bioprosthetic valve closure sounds. Forty-seven patients with a porcine bioprosthetic valve inserted in the aortic position were involved in the study. Twenty-four of them had a normal bioprosthetic valve, and the other 23 had a degenerated valve. Five features extracted from the Fourier spectra and 12 linear predictive coding (LPC) coefficients of the sounds were used separately as the input of two neural-network classifiers. The performance of the classifiers was tested using the leave-one-out method. Results show that correct classifications were 85 per cent using the spectral features, and 89 per cent using the LPC coefficients. The study confirms the potential of artificial networks for the classification of bioprosthetic valve closure sounds. Clinical use of this method, however, still requires further investigation.

Aortic Valve↗
PubMed2005

Modelling brain emergent behaviours through coevolution of neural agents.

Recently, many research efforts focus on modelling partial brain areas with the long-term goal to support cognitive abilities of artificial organisms. Existing models usually suffer from heterogeneity, which constitutes their integration very difficult. The present work introduces a computational framework to address brain modelling tasks, emphasizing on the integrative performance of substructures. Moreover, implemented models are embedded in a robotic platform to support its behavioural capabilities. We follow an agent-based approach in the design of substructures to support the autonomy of partial brain structures. Agents are formulated to allow the emergence of a desired behaviour after a certain amount of interaction with the environment. An appropriate collaborative coevolutionary algorithm, able to emphasize both the speciality of brain areas and their cooperative performance, is employed to support design specification of agent structures. The effectiveness of the proposed approach is illustrated through the implementation of computational models for motor cortex and hippocampus, which are successfully tested on a simulated mobile robot.

Animals↗
PubMed2006

Variational calculation of the limit cycle and its frequency in a two-neuron model with delay.

We consider a model system of two coupled Hopfield neurons, which is described by delay differential equations taking into account the finite signal propagation and processing times. When the delay exceeds a critical value, a limit cycle emerges via a supercritical Hopf bifurcation. First, we calculate its frequency and trajectory perturbatively by applying the Poincaré-Lindstedt method. Then, the perturbation series are resummed by means of the Shohat expansion in good agreement with numerical values. However, with increasing delay, the accuracy of the results from the Shohat expansion worsens. We thus apply variational perturbation theory (VPT) to the perturbation expansions to obtain more accurate results, which moreover hold even in the limit of large delays.

Animals↗
PubMed2002

The role of intra-thalamic and thalamocortical circuits in action selection.

We previously proposed that the basal ganglia (BG) play a crucial role in action selection. Quantitative analysis and simulation of a computational model of the intrinsic BG demonstrated that its output was consistent with this proposition. Here we build on that model by embedding it into a wider circuit containing the motor thalamocortical loop and thalamic reticular nucleus (TRN). Simulation of this extended model showed that the additions gave five main results which are desirable in a selection/switching mechanism. First, low salience actions (i.e. those with low urgency) could be selected. Second, the range of salience values over which actions could be switched between was increased. Third, the contrast between the selected and non-selected actions was enhanced via improved differentiation of outputs from the BG. Fourth, transient increases in the salience of a non-selected action were prevented from interrupting the ongoing action, unless the transient was of sufficient magnitude. Finally, the selection of the ongoing action persisted when a new closely matched salience action became active. The first result was facilitated by the thalamocortical loop; the rest were dependent on the presence of the TRN. Thus, we conclude that the results are consistent with these structures having clearly defined functions in action selection.

Algorithms↗
PubMed2001

Recognition of environmental and genetic effects on barley phenolic fingerprints by neural networks.

Through computational analysis of high-performance liquid chromatography (HPLC) traces we find correlations between secondary metabolites and growth conditions of six varieties of barley. Using artificial neural networks, it was possible to classify chromatograms for which the varieties were fertilized by nitrogen and treated by fungicide. For each variety of barley we could also differentiate it from the others. Surprisingly, all these classification tasks could be solved successfully by a simple network with no hidden units. When adding to the methodology pruning of the network weights, we were able to reduce the set of peaks in the chromatograms and obtain a necessary subset from which the growth conditions and differentiation may be decided. In some instances, more complex networks with hidden units could lead to a further reduction of the number of peaks used. In most cases, far more than half of the peaks are redundant. We find that it requires fewer information-rich peaks to perform the variety differentiation tasks than to recognize any of the growth conditions. Analysis of the network weights reveals correlations between weighted combinations of peaks.

Chromatography↗
PubMed2001

Cortical spreading depression and the pathogenesis of brain disorders: a computational and neural network-based investigation.

This paper reviews our recent studies of the role of cortical spreading depression (CSD) in the pathogenesis of brain disorders. Our investigation is a computational one, involving the development and utilization of a complex neuro-metabolic model of the interactions assumed to occur in the cortex during the passage of multiple CSD waves. Incorporating these neuro-metabolic changes of CSD within a neural network model of normoxic cortex produces cortical activation patterns during the passage of a CSD wave that, projected onto the visual fields, resemble the visual hallucinations observed during the migraine aura. When focal ischemia is simulated with the model, the evoked CSD waves are found to affect the expansion of the infarction into the ischemic penumbra. Our findings support the hypothesis that CSD does play an important pathogenic role in these and other neurological disorders, and suggest additional experimental studies that may further substantiate it.

Animals↗