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At least 1,369 records · Page 76Linked to original sources
PubMed1999

A computational model of neglect dyslexia.

This paper presents a straightforward theory of neglect (a pre-attentive salience system selects objects' centers for the focus of an attentional "spotlight") as a computational model. This construction permits simulations of model "lesions" and allows checking unequivocally the model's implications. The current model can account for some of the common patterns of observations of neglect dyslexia: that errors increase with word length and that short words may be read as "too long". Most importantly, it also accounts for a dissociation between neglect dyslexia and neglect on line bisection tasks. A two stage model can account for the patterns of performance seen in many neglect subjects, but more important is the demonstration that a theory's implementation as a computational model permits unambiguous evaluation of the theory's implications.

Computer Simulation↗
PubMedDate not supplied

A generalized Hebbian rule for activity-dependent synaptic modifications.

In this paper our previous model of activity-dependent synaptic modification is extended and applied to a model neuron with active dendrites and is used in computer simulations to examine in detail the dependence of synaptic modifications on the interval between the onset of excitatory postsynaptic potentials (EPSPs) and postsynaptic action potentials (APs). The EPSP amplitude is increased when the action potentials occur within 20 ms after EPSPs and is reduced when the action potentials occur within 20 ms before EPSPs. Furthermore, the absolute value of changes in the EPSP amplitude tends to increase as the interval between APs and EPSPs decreases. A learning rule for synaptic modifications described in this paper may, hence, further generalize the Hebbian rule which requires conjunctive presynaptic and postsynaptic activity for synaptic modification to occur. Functional roles for such a generalized Hebbian rule are also considered.

Action Potentials↗
PubMed2003

Multivariate autoregressive modeling of fMRI time series.

We propose the use of multivariate autoregressive (MAR) models of functional magnetic resonance imaging time series to make inferences about functional integration within the human brain. The method is demonstrated with synthetic and real data showing how such models are able to characterize interregional dependence. We extend linear MAR models to accommodate nonlinear interactions to model top-down modulatory processes with bilinear terms. MAR models are time series models and thereby model temporal order within measured brain activity. A further benefit of the MAR approach is that connectivity maps may contain loops, yet exact inference can proceed within a linear framework. Model order selection and parameter estimation are implemented by using Bayesian methods.

Algorithms↗
PubMed2003

Region of interest based analysis of functional imaging data.

fMRI analysis techniques are presented that test functional hypotheses at the region of interest (ROI) level. An SPM-compatible Matlab toolbox has been developed that allows the creation of subject-specific ROI masks based on anatomical markers and the testing of functional hypotheses on the regional response using multivariate time-series analysis techniques. The combined application of subject-specific ROI definition and region-level functional analysis is shown to appropriately compensate for intersubject anatomical variability, offering finer localization and increased sensitivity to task-related effects than standard techniques based on whole-brain normalization and voxel or cluster-level functional analysis, while providing a more direct link between discrete brain region hypotheses and the statistical analyses used to test them.

Artifacts↗
PubMedDate not supplied

Models of receptive-field dynamics in visual cortex.

The position, size, and shape of the receptive field (RF) of some cortical neurons change dynamically, in response to artificial scotoma conditioning (Pettet & Gilbert, 1992) and to retinal lesions (Chino et al., 1992; Darian-Smith & Gilbert, 1995) in adult animals. The RF dynamics are of interest because they show how visual systems may adaptively overcome damage (from lesions, scotomas, or other failures), may enhance processing efficiency by altering RF coverage in response to visual demand, and may perform perceptual learning. This paper presents an afferent excitatory synaptic plasticity rule and a lateral inhibitory synaptic plasticity rule--the EXIN rules (Marshall, 1995)--to model persistent RF changes after artificial scotoma conditioning and retinal lesions. The EXIN model is compared to the LISSOM model (Sirosh et al., 1996) and to a neuronal adaptation model (Xing & Gerstein, 1994). The rules within each model are isolated and are analyzed independently, to elucidate their roles in adult cortical RF dynamics. Based on computer simulations, the EXIN lateral inhibitory synaptic plasticity rule and the LISSOM lateral excitatory synaptic plasticity rule produced the best fit with current neurophysiological data on visual cortical plasticity in adult animals (Chino et al., 1992; Pettet & Gilbert, 1992; Darian-Smith & Gilbert, 1995) including (1) the retinal position and shape of the expanding RFs; (2) the corticotopic direction in which responsiveness returns to the silenced cortex; (3) the direction of RF shifts; (4) the amount of change in response to blank stimuli; and (5) the lack of dynamic RF changes during conditioning with a retinal lesion in one eye and the unlesioned eye kept open, in adult animals. The effects of the LISSOM lateral inhibitory synaptic plasticity rule during artificial scotoma conditioning are in conflict with those of the other two LISSOM synaptic plasticity rules. A novel "complementary scotoma" conditioning experiment, in which stimulation of two complementary regions of visual space alternates repeatedly, is proposed to differentiate the predictions of the EXIN and LISSOM rules.

Adaptation, Physiological↗
PubMedDate not supplied

New methods for ligand-based virtual screening: use of data fusion and machine learning to enhance the effectiveness of similarity searching.

Similarity searching using a single bioactive reference structure is a well-established technique for accessing chemical structure databases. This paper describes two extensions of the basic approach. First, we discuss the use of group fusion to combine the results of similarity searches when multiple reference structures are available. We demonstrate that this technique is notably more effective than conventional similarity searching in scaffold-hopping searches for structurally diverse sets of active molecules; conversely, the technique will do little to improve the search performance if the actives are structurally homogeneous. Second, we make the assumption that the nearest neighbors resulting from a similarity search, using a single bioactive reference structure, are also active and use this assumption to implement approximate forms of group fusion, substructural analysis, and binary kernel discrimination. This approach, called turbo similarity searching, is notably more effective than conventional similarity searching.

Artificial Intelligence↗
PubMed2001

A model of cerebellar computations for dynamical state estimation.

The cerebellum is a neural structure that is essential for agility in vertebrate movements. Its contribution to motor control appears to be due to a fundamental role in dynamical state estimation, which also underlies its role in various non-motor tasks. Single spikes in vestibular sensory neurons carry information about head state. We show how computations for optimal dynamical state estimation may be accomplished when signals are encoded in spikes. This provides a novel way to design dynamical state estimators, and a novel way to interpret the structure and function of the cerebellum.

Acceleration↗
PubMedDate not supplied

A biophysical model of synaptic delay learning and temporal pattern recognition in a cerebellar Purkinje cell.

It has been suggested that information in the brain is encoded in temporal spike patterns which are decoded by a combination of time delays and coincidence detection. Here, we show how a multi-compartmental model of a cerebellar Purkinje cell can learn to recognise temporal parallel fibre activity patterns by adapting latencies of calcium responses after activation of metabotropic glutamate receptors (mGluRs). In each compartment of our model, the mGluR signalling cascade is represented by a set of differential equations that reflect the underlying biochemistry. Phosphorylation of the mGluRs changes the concentration of receptors which are available for activation by glutamate and thereby adjusts the time delay between mGluR stimulation and voltage response. The adaptation of a synaptic delay as opposed to a weight represents a novel non-Hebbian learning mechanism that can also implement the adaptive timing of the classically conditioned eye-blink response.

Action Potentials↗
PubMed2006

Becoming syntactic.

Psycholinguistic research has shown that the influence of abstract syntactic knowledge on performance is shaped by particular sentences that have been experienced. To explore this idea, the authors applied a connectionist model of sentence production to the development and use of abstract syntax. The model makes use of (a) error-based learning to acquire and adapt sequencing mechanisms and (b) meaning-form mappings to derive syntactic representations. The model is able to account for most of what is known about structural priming in adult speakers, as well as key findings in preferential looking and elicited production studies of language acquisition. The model suggests how abstract knowledge and concrete experience are balanced in the development and use of syntax.

Adolescent↗
PubMed2000

A model for intradendritic computation of binocular disparity.

Many complex cells in mammalian primary visual cortex are finely tuned to binocular disparity. In the prevailing model, several binocular simple cells drive each disparity-tuned complex cell. However, some cat complex cells receive direct LGN input, and binocular simple cells are rare in macaque. In our biophysically detailed compartmental model, active dendrites of a single neuron perform the multiple simple-cell-like subunit computations that underlie both orientation and disparity tuning. The responses of our detailed model could be predicted by a simple algebraic formula closely related to an 'energy' model. Adding inhibitory synapses led to sharper, more contrast-invariant tuning curves. Thus active dendrites could contribute to binocular-disparity tuning in complex cells.

Animals↗
PubMed2006

Schizophrenia, neurobiology and the methodology of systemic modeling.

Progress in the pharmacological treatment of schizophrenia is dependent on the extent of our understanding of the brain as the basis of this disease. Detailed examination of neurobiological data shows that only a systemic approach will integrate this wealth of information. For this reason, the steps involved in model building should be clarified, as further progress will necessitate closer cooperation between neuropsychiatrists, neurobiologists and systems scientists.

Brain↗
PubMed1998

Evaluation of esophageal cytology using a neural net-based interactive scanning system (the PAPNET system): its possible role in screening for esophageal and gastric carcinoma.

A neural net-based, semiautomated, interactive computerized cell analysis system (The PAPNET system, Neuromedical Systems, Suffern, NY) was used to examine cells from 138 esophageal smears obtained by lavage, brushings, or balloon from as many patients. From each smear, trained human observers examined 128 cell images selected by the machine. Abnormal cells were identified in all 35 patients with cancer, whether esophageal, gastric, oral, or metastatic. Further, in 11 smears, the displayed images allowed the recognition of effects of radiotherapy and, in 14 smears, the diagnosis of a specific tumor type, such as squamous cell carcinoma (8 patients) or adenocarcinoma (6 patients). In 3 additional cases, the diagnosis of "carcinoma, not further specified," was established. One case of esophageal carcinoma in situ, not previously recognized on a smear or in the biopsy specimen, and one case of gastric adenocarcinoma, not recognized in the smear, were identified in PAPNET-generated images. The possible application of the apparatus to the triage of smears and population screening for esophageal and gastric carcinoma precursors is discussed.

Adenocarcinoma↗
PubMed2005

PROFcon: novel prediction of long-range contacts.

MOTIVATION: Despite the continuing advance in the experimental determination of protein structures, the gap between the number of known protein sequences and structures continues to increase. Prediction methods can bridge this sequence-structure gap only partially. Better predictions of non-local contacts between residues could improve comparative modeling, fold recognition and could assist in the experimental structure determination. RESULTS: Here, we introduced PROFcon, a novel contact prediction method that combines information from alignments, from predictions of secondary structure and solvent accessibility, from the region between two residues and from the average properties of the entire protein. In contrast to some other methods, PROFcon predicted short and long proteins at similar levels of accuracy. As expected, PROFcon was clearly less accurate when tested on sparse evolutionary profiles, that is, on families with few homologs. Prediction accuracy was highest for proteins belonging to the SCOP alpha/beta class. PROFcon compared favorably with state-of-the-art prediction methods at the CASP6 meeting. While the performance may still be perceived as low, our method clearly pushed the mark higher. Furthermore, predictions are already accurate enough to seed predictions of global features of protein structure.

Algorithms↗
PubMed2005

Pcons5: combining consensus, structural evaluation and fold recognition scores.

MOTIVATION: The success of the consensus approach to the protein structure prediction problem has led to development of several different consensus methods. Most of them only rely on a structural comparison of a number of different models. However, there are other types of information that might be useful such as the score from the server and structural evaluation. RESULTS: Pcons5 is a new and improved version of the consensus predictor Pcons. Pcons5 integrates information from three different sources: the consensus analysis, structural evaluation and the score from the fold recognition servers. We show that Pcons5 is better than the previous version of Pcons and that it performs better than using only the consensus analysis. In addition, we also present a version of Pmodeller based on Pcons5, which performs significantly better than Pcons5. AVAILABILITY: Pcons5 is the first Pcons version available as a standalone program from http://www.sbc.su.se/~bjorn/Pcons5. It should be easy to implement in local meta-servers.

Algorithms↗
PubMed2006

Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data.

MOTIVATION: Multilayer perceptrons (MLP) represent one of the widely used and effective machine learning methods currently applied to diagnostic classification based on high-dimensional genomic data. Since the dimensionalities of the existing genomic data often exceed the available sample sizes by orders of magnitude, the MLP performance may degrade owing to the curse of dimensionality and over-fitting, and may not provide acceptable prediction accuracy. RESULTS: Based on Fisher linear discriminant analysis, we designed and implemented an MLP optimization scheme for a two-layer MLP that effectively optimizes the initialization of MLP parameters and MLP architecture. The optimized MLP consistently demonstrated its ability in easing the curse of dimensionality in large microarray datasets. In comparison with a conventional MLP using random initialization, we obtained significant improvements in major performance measures including Bayes classification accuracy, convergence properties and area under the receiver operating characteristic curve (A(z)). SUPPLEMENTARY INFORMATION: The Supplementary information is available on http://www.cbil.ece.vt.edu/publications.htm

Biomarkers, Tumor↗