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PubMed2000

Age of acquisition effects in adult lexical processing reflect loss of plasticity in maturing systems: insights from connectionist networks.

Early learned words are recognized and produced faster than later learned words. The authors showed that such age of acquisition effects are a natural property of connectionist models trained by back-propagation when patterns are introduced at different points into training and learning of early and late patterns is cumulative and interleaved. Analysis of hidden unit activations indicated that the age of acquisition effect reflects a gradual reduction in network plasticity and a consequent failure to differentiate late items as effectively as early ones. Further simulations examined the effects of vocabulary size, learning rate, sparseness of coding, use of a modified learning algorithm, loss of early items, acquisition of very late items, and lesioning the network. The relationship between age of acquisition and word frequency was explored, including analyses of how the relative influence of these factors is modulated by introducing weight decay.

Adult↗
PubMed2003

Distance, dissimilarity index, and network community structure.

We address the question of finding the community structure of a complex network. In an earlier effort [H. Zhou, Phys. Rev. E 67, 041908 (2003)], the concept of network random walking is introduced and a distance measure defined. Here we calculate, based on this distance measure, the dissimilarity index between nearest-neighboring vertices of a network and design an algorithm to partition these vertices into communities that are hierarchically organized. Each community is characterized by an upper and a lower dissimilarity threshold. The algorithm is applied to several artificial and real-world networks, and excellent results are obtained. In the case of artificially generated random modular networks, this method outperforms the algorithm based on the concept of edge betweenness centrality. For yeast's protein-protein interaction network, we are able to identify many clusters that have well defined biological functions.

Algorithms↗
PubMed2004

Exploring structure-function relationships in neocortical networks by means of neuromodelling techniques.

Determining the neuronal architecture underlying certain visual functions is of fundamental importance for understanding how sensory processing is implemented in the brain. The wealth of anatomical, physiological and biophysical data that is being currently acquired on the neocortex could be used to constrain its functional architecture. However, given the intrinsic complexity and diversity of the data, it is difficult to provide a comprehensive framework to use these data in order to characterize structure-function relationships. Here, we discuss the use of biophysically plausible models of dynamics of neuronal networks, constructed to reflect the known properties of neocortical connectivity and modularity, as a tool to bring together anatomy and physiology. We illustrate the utility and rationale of the neuro-dynamics modelling approach by considering recent studies on the relationship between functional structure of the visual cortex and its response timing, and on the cellular and network origin of neuronal oscillations in the gamma frequency range. We also critically discuss how an interaction between theory and experiments could help this approach to become directly relevant for clinical applications.

Animals↗
PubMed2006

QSAR study of the enantiomeric excess in asymmetric catalytic reactions with topological indices and an artificial neural network.

The relationships between the enantiomer excess of product in catalytic asymmetric reactions and the structures of the catalysts or reagents in several asymmetric reactions were studied using a backpropagation (BP) neural network with topological indices and their chiral expansions. The trained network can be used to screen new asymmetric catalysts, estimate catalytic effects, design reaction environments, and prove or improve the proposed reaction mechanism.

Catalysis↗
PubMed1995

Extraction of fuzzy rules using neural networks with structure level adaptation and its application to diagnosis for hepatobiliary disorders.

First, this paper presents the reasoning and the learning method for fuzzy rules using structure level adaptation of neural networks. In a usual neural network's mechanism, during learning process of rules, we can observe the following two behaviors: Case 1: If a neural network does not have enough neurons to be satisfied to infer, then the input weight vector will have a tendency to fluctuate greatly, even after a certain long period the learning process. In this case, the network needs to generate a new neuron as its parent's attribute is inherited. Case 2: If a neural network has enough neurons to infer, and even if the input weight vector of each neuron will converge to a certain value, then we shall be able to turn out unnecessary neurons from the network in the calculation. In this case, because it is necessary to delete a redundant neuron to the calculation, the neuron is annihilated without affecting the performance of the network. By observing such behaviors, we can generate or annihilate the specified neuron respectively to achieve an overall good system. In the proposed method, we described a procedure to derive the neuron generation/annihilation automatically and applied the procedure to learning system. Next, we apply such procedure to the learning system in which the experimental data related to hepatobiliary disorders is used. We use a real medical database containing the results of ten biochemical terms test for four hepatobiliary disorders. We have 536 case data, including some errors. After the learning, by using 179 data chosen randomly from database, the proposed system converged to a certain small value and this constructed network has the optimal structure for these teaching data. In addition, we get that the fuzzy rules have some meanings related to the degree of the input weight vector, and the fuzzy rules for hepatobiliary disorders are extracted from the learned network with respect to the degree of input weight vector. Moreover, to verify the validity of the diagnosis of the proposed method, the feed-forward calculation was implemented using extracted fuzzy rules for all databases. As a result, the proposed system correctly diagnosed more than 70%.

Biliary Tract Diseases↗
PubMed2003

Prediction of disordered regions in proteins from position specific score matrices.

We describe here the results of using a neural network based method (DISOPRED) for predicting disordered regions in 55 proteins in the 5(th) CASP experiment. A set of 715 highly resolved proteins with regions of disorder was used to train the network. The inputs to the network were derived from sequence profiles generated by PSI-BLAST. A post-filter was applied to the output of the network to prevent regions being predicted as disordered in regions of confidently predicted alpha helix or beta sheet structure. The overall two-state prediction accuracy for the method is very high (90%) but this is highly skewed by the fact that most residues are observed to be ordered. The overall Matthews' correlation coefficient for the submitted predictions is 0.34, which gives a more realistic impression of the overall accuracy of the method, though still indicates significant predictive power.

Computational Biology↗
PubMed1993

A simple network model simulates hippocampal place fields: II. Computing goal-directed trajectories and memory fields.

Place cells have been described as the computational elements of a neuronal cognitive mapping system that encodes and stores relationships among spatial stimuli (O'Keefe & Nadel, 1978). Furthermore, place cells seem to encode remembered locations because neural activity is maintained when the visual stimuli that influence place field location are vastly degraded, such as when cues are removed or the lights are turned off (O'Keefe & Speakman, 1987; Quirk, Muller, & Kubie, 1990). A feed-forward network model that mapped visual input onto a representation of location simulated some basic properties of hippocampal place fields, including resistance to disruption after partial cue removal (Shapiro & Hetherington, 1993). However, the stimulated place fields required visual input for their activation. We now report that a network that incorporates feedback (a) computed correct trajectories toward simulated goals and (b) simulated place fields that persist in the absence of visual input. The simulation suggests that feedback properties can provide a computational account of O'Keefe and Speakman's data.

Animals↗
PubMedDate not supplied

Application of orthogonal neural network to craniomaxillary reconstruction.

Traditionally, the orthopaedist, according to their past experience, reconstructs damaged area while the operation is in progress. This may prolong the operation and cause the wound to become infected. Most importantly it is difficult to precisely match the skeletal defect. A well-disciplined network of prediction re-fabricates the damaged area through automation. This research is based on the CT image file, which is the product of X-ray computed tomography (CT), and computes the skeletal positions around the damaged area through image processing and boundary detection. The skeletal positions are inputted into the orthogonal neural network and discipline the network so that it possesses the scattering characteristic of bone. The network then calculates skeletal positions in the damaged area and revises the former CT image file to rebuild a 3D model. Accordingly, in comparison with a manual sketch, the orthogonal neural network forecast is more geometrically precise. Moreover, the forecast satisfies the second order derivative, which is a continuous function, and the edge of the fabricated bone is therefore kept smoother.

Biomedical Engineering↗
PubMed2005

Progressive transformation of hippocampal neuronal representations in "morphed" environments.

Hippocampal neural codes for different, familiar environments are thought to reflect distinct attractor states, possibly implemented in the recurrent CA3 network. A defining property of an attractor network is its ability to undergo sharp and coherent transitions between pre-established (learned) representations when the inputs to the network are changed. To determine whether hippocampal neuronal ensembles exhibit such discontinuities, we recorded in CA3 and CA1 when a familiar square recording enclosure was morphed in quantifiable steps into a familiar circular enclosure while leaving other inputs constant. We observed a gradual noncoherent progression from the initial to the final network state. In CA3, the transformation was accompanied by significant hysteresis, resulting in more similar end states than when only square and circle were presented. These observations suggest that hippocampal cell assemblies are capable of incremental plastic deformation, with incongruous information being incorporated into pre-existing representations.

Action Potentials↗
PubMed2006

Computational inference of neural information flow networks.

Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.

Action Potentials↗
PubMed2004

Chaotic itinerancy as a mechanism of irregular changes between synchronization and desynchronization in a neural network.

We investigate the dynamic character of a network of electrotonically coupled cells consisting of class I point neurons, in terms of a finite dimensional dynamical system. We classify a subclass of class I point neurons, called class I* point neurons. Based on this classification, we use a reduced Hindmarsh-Rose (H-R) model, which consists of two dynamical variables, to construct a network model consisting of electrotonically coupled H-R neurons. Although biologically simple, the system is sufficient to extract the essence of the complex dynamics, which the system may yield under certain physiological conditions. The network model produces a transitory behavior as well as a periodic motion and spatio-temporal chaos. The transitory dynamics that the network model exhibits is shown numerically to be chaotic itinerancy. The transitions appear between various metachronal waves and all-synchronization states. The network model shows that this transitory dynamics can be viewed as a chaotic switch between synchronized and desynchronized states. Despite the use of spatially discrete point neurons as basic elements of the network, the overall dynamics exhibits scale-free activity including various scales of spatio-temporal patterns.

Action Potentials↗
PubMed2003

Recent advances in the prediction of blood-brain partitioning from molecular structure.

The widely distributed software tools Cerius2 and ACD/log D Suite have been used to develop a new method for the prediction of the ratio of concentrations of a drug in the brain and blood (BB,quantified as log BB) from structure. The performances of all known blood-brain partitioning prediction methods are compared to give an up-to-date account on their accuracy, limitations, and usefulness. It is demonstrated that the new log BB prediction method is superior to other methods with regard to low-to-medium throughput log BB prediction, whereas the C2-ADME log BB two-dimensional (2D) method seems to offer the best compromise between speed and accuracy for ultra-high throughput processing of large compound databases for log BB prediction.

Blood-Brain Barrier↗
PubMed1997

Symbol grounding: a bridge from artificial life, to artificial intelligence.

This paper develops a bridge from AL issues about the symbol-matter relation to AI issues about symbol-grounding by focusing on the concepts of formality and syntactic interpretability. Using the DNA triplet-amino acid specification relation as a paradigm, it is argued that syntactic properties can be grounded as high-level features of the non-syntactic interactions in a physical dynamical system. This argument provides the basis for a rebuttal of John Searle's recent assertion that syntax is observer-relative (1990, 1992). But the argument as developed also challenges the classic symbol-processing theory of mind against which Searle is arguing, as well as the strong AL thesis that life is realizable in a purely computational medium. Finally, it provides a new line of support for the autonomous systems approach in AL and AI (Varela & Bourgine 1992a, 1992b).

Animals↗
PubMed2005

Fully automatic shape modelling using growing cell.

In this paper, we present a new framework for shape modelling and analysis: we suggest to look at the problem from a pattern recognition point of view, and claim that under this prospective several advantages are achieved. The modelling of a surface with a point distribution model is seen as an unsupervised clustering problem, and tackled by using growing cell structures. The adaptation of a model to new shapes is studied as a classification task, and provides a straightforward solution to the point correspondence problem in active shape modelling. The method is illustrated and tested in 3D synthetic datasets and applied to the modelling of brain ventricles in an elderly population.

Cerebral Ventricles↗
PubMed2006

[Computer assisted radiological diagnostics of arthritic joint alterations].

Computer assisted diagnosis (CAD) schemes are currently used in the field of musculoskeletal diseases to quantitatively assess vertebral fractures, joint space narrowing, andr erosion. Most systems work semi-automatically, i.e. they are operator dependent in the selection of anatomical landmarks. Fully automatic programs are currently under development. Some CAD products have already been successfully used in clinical trials.

Absorptiometry, Photon↗
PubMed2002

Scene segmentation by spike synchronization in reciprocally connected visual areas. I. Local effects of cortical feedback.

To investigate scene segmentation in the visual system we present a model of two reciprocally connected visual areas using spiking neurons. Area P corresponds to the orientation-selective subsystem of the primary visual cortex, while the central visual area C is modeled as associative memory representing stimulus objects according to Hebbian learning. Without feedback from area C, a single stimulus results in relatively slow and irregular activity, synchronized only for neighboring patches (slow state), while in the complete model activity is faster with an enlarged synchronization range (fast state). When presenting a superposition of several stimulus objects, scene segmentation happens on a time scale of hundreds of milliseconds by alternating epochs of the slow and fast states, where neurons representing the same object are simultaneously in the fast state. Correlation analysis reveals synchronization on different time scales as found in experiments (designated as tower, castle, and hill peaks). On the fast time scale (tower peaks, gamma frequency range), recordings from two sites coding either different or the same object lead to correlograms that are either flat or exhibit oscillatory modulations with a central peak. This is in agreement with experimental findings, whereas standard phase-coding models would predict shifted peaks in the case of different objects.

Animals↗