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PubMed2001

Computational approaches to the architecture and operations of the prefrontal cortical circuit for working memory.

1. This article reviews recent progress in the computational studies towards the architecture and operations of the prefrontal cortical circuit, which are keys to understand the mechanisms of working memory processing. 2. The recurrent excitatory connections form closed-loop circuits, which contribute to the sustainment of delay-period activity. These connections subserve the cortical amplification of the activity. 3. Recent experimental studies (Wilson et al. 1994; Rao et al. 1999, 2000) suggested that at least two architectonically distinct types of intracortical inhibition, isodirectional and cross-directional inhibition, play significant roles in the formation of memory fields. 4. Computer simulations of a prefrontal cortical circuit model (Tanaka 1999, 2000a) showed that the isodirectional inhibition in the model regulated the amplitude of memory fields (i.e., the maximum firing rate) while the cross-directional inhibition contributed to the sharpening of the memory fields or the tuning curves. 5. The above characteristics enable the prefrontal cortical circuit to control memory fields, which would be necessary to general working memory processing. It would also be interesting to know whether different subtypes of the interneurons have distinct roles. 6. Another important issue is how neuromodulators contribute to working memory processing. Recent computer simulations by Durstewitz et al. (1999, 2000) showed that stronger dopamine action required stronger intervening input to destroy working memory, suggesting that dopamine contributes to the stabilization of working memory representation. 7. Further elucidation of these issues based on more detailed anatomical data of the cortical circuitry would make the architecture and operations of the prefrontal cortical circuit be more clearly described.

Animals↗
PubMed1998

Manipulations during forced wakefulness have differential impact on sleep architecture, EEG power spectrum, and Fos induction.

We propose a hypothesis suggesting that the most prominent experiences occurring during wakefulness activate specific clusters of neurons related to such experiences. These neurons could possibly then evoke the release of various types of sleep-inducing molecules, thereby causing different patterns of sleep architecture. In this study, we therefore sought to determine whether manipulations of behavior during wakefulness, such as forced wakefulness induced by gentle handling, forced wakefulness associated with a stressful condition such as immobilization, or forced wakefulness associated with excess intake of palatable food, could result in a variation of Fos immunoreactivity in selective brain structures and could also result in different sleep and EEG power density patterns. The results showed that the sleep-wake cycle of rats after all the experimental manipulations was different not only with respect to the control group but also among themselves. Additionally, power spectrum analysis showed an increase of 0.25-4.0 Hz in all experimental manipulations, whereas the 4.25-8.0 Hz increase occurred only in the situation of forced wakefulness plus stress. The Fos induction showed activation of cell clusters in cortical areas and telencephalic centers, in several hypothalamic nuclei, in monoaminergic cell groups, and in brain stem nuclei. The density of Fos-immunoreactive neurons varied in relation to the different paradigms of forced wakefulness. These results suggest that activation of cell clusters in the brain are related to the type of manipulation imposed on the rat during wakefulness and that such variation in cell activation prior to sleep may be associated with sleep architecture and EEG power.

Animals↗
PubMed2001

Accidental sharp force fatalities--beware of architectural glass, not knives.

In a retrospective evaluation of 799 consecutive autopsies of victims of sharp force performed between 1967 and 1996 in Münster and Berlin, only 18 cases (2.3%) were classified as accidents. A typical pattern was present in 15 cases: inebriated adults (1.4-3.6g/l BAC) fell into an architectural glass surface in the form of a door or window (12 cases), an aquarium, a mirrored wardrobe or a telephone cell. Another man fell into a large drinking glass. Many victims in this group showed multiple scratches, abrasions and superficial incisions as well as one or more deep tear/cut/puncture injury. The wound margins can be clean-cut or irregular and abraded. Death was mostly caused by exsanguination except for one case of air embolism and one case of cerebral injury. The fatal injuries were produced by large and dagger-like slivers of glass, by sharp-edged fragments of glass remaining inside the frame or by a portion of glass which fell down and acted in a way similar to a guillotine. Ordinary types of flat glass were involved in all cases and it is not until the impact that sharp fragments or cutting edges are produced. So the motion of the person commonly provides the force necessary for a fatal injury. This was also true for the remaining two cases not involving architectural glass. A farmer suffered cerebral injury from a fall into the long prong of a pitch fork, and the wounding agent was a knife in only one case. A man who stated that he had fallen into the knife in his hand died from pneumonia after inadequate therapy following a single stab injury to the periphery of the left lung and liver. Accidents where the victim is killed by his own knife therefore appear to be extremely rare.

Accidental Falls↗
PubMed1998

Alterations of the architecture of subendocardial arterioles in patients with hypertrophic cardiomyopathy and impaired coronary vasodilator reserve: a possible cause for myocardial ischemia.

OBJECTIVES: The study was designed to investigate the architecture of subendocardial arterioles of patients with hypertrophic cardiomyopathy (HCM) and angina pectoris with respect to coronary vasodilator reserve. BACKGROUND: There is growing evidence that the coronary microvasculature is abnormal in HCM. Arterioles, which mainly regulate intramyocardial blood flow, are especially suspect. METHODS: Thirteen patients with HCM (50.1+/-12.6 years old, mean value +/- SD) were studied after exclusion of any relevant coronary stenoses. Subendocardial arterioles (density [n/mm2], wall area [microm2], percent lumen area [%lumen], periarteriolar collagen area [microm2]), myocyte diameter (microm) and interstitial collagen fraction (Vv%) were evaluated by means of stereologic morphometry of transvenous biopsy samples. Coronary blood flow was measured quantitatively with the inert chromatographic argon method at basal conditions and after dipyridamole (0.5 mg/kg body weight over 4 min intravenously), and coronary vasodilator reserve was calculated as the ratio of coronary resistance at basal conditions and after pharmacologic vasodilation. Data from five normotensive subjects (45.4+/-11 years old, p = NS) served as control data. RESULTS: Arteriolar density was diminished by 38% (p = 0.004) and %lumen by 13% (p = 0.009) in patients with HCM compared with control subjects. Coronary reserve was impaired in patients with HCM (2.28+/-0.6 vs. 5.34+/-1.49, p = 0.003) because of higher coronary resistance after vasodilation (0.48+/-0.14 vs. 0.22+/-0.06 mm Hg x min x 100 g/ml, p = 0.004). Coronary vasodilator reserve correlated with arteriolar density (r = +0.47, p = 0.045) and with %lumen (r = 0.65, p = 0.003). CONCLUSIONS: In HCM, the architecture of preterminal subendocardial arterioles is altered by a reduced total cross-sectional lumen area, corresponding to an impaired coronary vasodilator capacity that may predispose to myocardial ischemia.

Adult↗
PubMedDate not supplied

A spiking neural network architecture for nonlinear function approximation.

Multilayer perceptrons have received much attention in recent years due to their universal approximation capabilities. Normally, such models use real valued continuous signals, although they are loosely based on biological neuronal networks that encode signals using spike trains. Spiking neural networks are of interest both from a biological point of view and in terms of a method of robust signaling in particularly noisy or difficult environments. It is important to consider networks based on spike trains. A basic question that needs to be considered however, is what type of architecture can be used to provide universal function approximation capabilities in spiking networks? In this paper, we propose a spiking neural network architecture using both integrate-and-fire units as well as delays, that is capable of approximating a real valued function mapping to within a specified degree of accuracy.

Action Potentials↗
PubMed2003

On motion detection through a multi-layer neural network architecture.

A neural network model called lateral interaction in accumulative computation for detection of non-rigid objects from motion of any of their parts in indefinite sequences of images is presented. Some biological evidences inspire the model. After introducing the model, the complete multi-layer neural architecture is offered in this paper. The architecture consists of four layers that perform segmentation by gray level bands, accumulative charge computation, charge redistribution by gray level bands and moving object fusion. The lateral interaction in accumulative computation associated learning algorithm is also introduced. Some examples that explain the usefulness of the system we propose are shown at the end of this article.

Motion Perception↗
PubMed1997

HAVNET: A New Neural Network Architecture for Pattern Recognition.

A new artificial neural network architecture, specifically designed for two-dimensional binary pattern recognition, is introduced. The network employs a unique similarity metric, based on the Hausdorff distance, to determine the degree of match between an input pattern and a learned representation. Use of this metric in the network leads to behaviour that is more consistent with human performance than that generated by similarity metrics currently in use in other artificial neural networks. A detailed description of the architecture, the learning equations, and the recall equations for the network are presented. An extension of the network is also described in which each class of learned objects is represented by multiple two-dimensional aspects. This extension greatly increases the utility of the network for tasks like character recognition and three-dimensional vision. The network is employed on an example pattern recognition task to demonstrate its application, with very good results. Copyright 1996 Elsevier Science Ltd.

Journal Article↗
PubMed1997

Pattern Categorization and Generalization with a Virtual Neuromolecular Architecture.

A multilevel neuromolecular computing architecture has been developed that provides a rich platform for evolutionary learning. The architecture comprises a network of neuron-like modules with internal dynamics modeled by cellular automata. The dynamics are motivated by the hypothesis that molecular processes operative in real neurons (in particular processes connected with second messenger signals and cytoskeleton-membrane interactions) subserve a signal integrating function. The objective is to create a repertoire of special purpose dynamic pattern processors through an evolutionary search algorithm and then to use memory manipulation algorithms to select combinations of processors from the repertoire that are capable of performing coherent pattern recognition/neurocontrol tasks. The system consists of two layers of cytoskeletally controlled (enzymatic) neurons and two layers of memory access neurons (called reference neurons) divided into a collection of functionally comparable subnets. Evolutionary learning can occur at the intraneuronal level through variations in the cytoskeletal structures responsible for the integration of signals in space and time, through variations in the location of elements that represent readin or readout proteins, and through variations in the connectivity of the neurons. The memory manipulation algorithms that orchestrate the repertoire of neuronal processors also use evolutionary search procedures. The network is capable of performing complicated pattern categorization tasks and of doing so in a manner that balances specificity and generalization. Copyright 1996 Elsevier Science Ltd.

Journal Article↗
PubMed1998

Mapping of neural networks onto the memory-processor integrated architecture.

In this paper, an effective memory-processor integrated architecture, called memory-based processor array for artificial neural networks (MPAA), is proposed. The MPAA can be easily integrated into any host system via memory interface. Specifically, the MPA system provides an efficient mechanism for its local memory accesses allowed by row and column bases, using hybrid row and column decoding, which is suitable for computation models of ANNs such as the accessing and alignment patterns given for matrix-by-vector operations. Mapping algorithms to implement the multilayer perceptron with backpropagation learning on the MPAA system are also provided. The proposed algorithms support both neuron and layer level parallelisms which allow the MPAA system to operate the learning phase as well as the recall phase in the pipelined fashion. Performance evaluation is provided by detailed comparison in terms of two metrics such as the cost and number of computation steps. The results show that the performance of the proposed architecture and algorithms is superior to those of the previous approaches, such as one-dimensional single-instruction multiple data (SIMD) arrays, two-dimensional SIMD arrays, systolic ring structures, and hypercube machines.

Journal Article↗
PubMed1999

A model of computation in neocortical architecture.

We propose that the specific architecture of the neocortex reflects the organization principles of neocortical computation. In this paper, we place the anatomically defined concept of columns into a functional context. It is provided by a large-scale computational hypothesis on visual recognition, which includes both, rapid parallel forward recognition, independent of any feedback prediction, and a feedback controlled refinement system. Short epochs of periodic clocking define a global reference time and introduce a discrete time for cortical processing which enables the combination of parallel categorization and sequential refinement. The presented model differs significantly from conventional neural network architectures and suggests a novel interpretation of the role of gamma oscillations and cognitive binding.

Journal Article↗
PubMed1999

On redundancy in neural architecture: dynamics of a simple module-based neural network and initial-state independence.

This article discusses the relationship between redundancy in neural architecture and activity (cell output or internal state) dynamics with a simple module-based neural network. In the network, a single neural cell with self-feedback is employed as a module sub-network, and all module sub-networks are connected via inter-module connections. In general, the activity dynamics of a single neural cell with positive self-feedback may have two minima in its energy surface, and the minimum the cell state converges to depends on the initial states. However, in the module-based network with all the same intra-module connections, an independence from initial states becomes conspicuous as the number of modules increases due to the architectural redundancy. Simulation and analytical studies on the network dynamics illustrate that the cell states always converge to a global minimum irrelevantly of the initial cell-states, and they never go to a local minimum when a sufficient number of modules are employed.

Journal Article↗
PubMed1999

Exploiting inherent relationships in RNN architectures.

We provide the relationship between the learning rate and the slope of a nonlinear activation function of a neuron within the framework of nonlinear modular cascaded systems realised through Recurrent Neural Network (RNN) architectures. This leads to reduction in the computational complexity of learning algorithms which continuously adapt the weights of such architectures, because there is a smaller number of independent parameters to optimise. Results are provided for the Gradient Descent (GD) learning algorithm and the Extended Recursive Least Squares (ERLS) algorithm, using a general nonlinear activation function of a neuron. The results obtained degenerate into the corresponding results for single RNNs, when considering only one module in such cascaded systems.

Journal Article↗
PubMedDate not supplied

A method for the automatic characterization of bone architecture in 3D mice microtomographic images.

We developed an automatic method to characterize mice bone architecture from three-dimensional (3D) microtomographic images. The distal metaphyses of the femur of mice were imaged using 3D synchrotron radiation microtomography at the European Synchrotron Radiation Facility (ID19) with a voxel size of 6.65 mum. Within each reconstructed volume, a region of interest was defined and trabecular and cortical bones were automatically separated. Then, 3D morphologic and topologic model-independent parameters quantifying the 3D bone architecture were computed in both regions. The technique was applied to study the response of the C57BL/6J@Ico strain of mice submitted to a model of bone loss by hind limb unloading produced by tail-suspension.

Animals↗
PubMedDate not supplied

Design methods and architectural issues of integrated medical image data base systems.

The past 20 years have seen tremendous changes in medical imaging techniques. New modalities and protocols are expanding the available digital image data at a rapid rate. Yet a framework for gathering, managing, and using multimodal image information is an integrated database environment is missing. The purpose of this paper is to present the experience of implementing an integrated medical image database system at UCSF. We discuss the general system architecture, software design methods, and specific database tools and illustrate them with application examples. Two immediate issues conforming the building of medical image database systems are: lack of supporting infrastructure and inability to index images by contest. To circumvent these problems, the evolutionary medical image database system being implemented at UCSF is based on a three-tiered client-server architecture: client medical workstations, database application servers, and a hospital-integrated picture archiving and communication system (HIP-PACS). The approach used to integrate content-based retrieval and knowledge base techniques within the existing HI-PACS to make the whole database system useful in medicine.

Adult↗
PubMed1999

Epidermal remodelling in psoriasis (III): a hexagonally-arranged cylindrical papilla model reveals the nature of psoriatic architecture.

A geometric model of hyperproliferative psoriatic epidermis that is remarkably similar to psoriatic skin in vivo has been proposed. This model is based on hexagonally-arranged cylindrical dermal papillae, and it is constructed under the assumption that the total number of viable epidermal cells is proportional to the number of cells within proliferative compartment. Quantitative analyses indicate that at least two distinct conditions are consistent with this assumption, corresponding to psoriasis with and without a granular layer. Psoriasis without a granular layer (G-minus psoriasis) represents an expanding state, and psoriasis with a granular layer (G-plus psoriasis) represents a stationary and/or regressing state. There is also another relationship between G-minus and G-plus psoriasis, that they are exchangeable with each other during the disease evolution. For example, consistent with the regressing nature of G-plus psoriasis, G-minus psoriasis is exchangeable with G-plus psoriasis with less papillary height until the papilla has a certain height (360 microm). On the other hand, G-minus psoriasis with the papillae of more than the critical height is exchangeable with G-plus psoriasis with more papillary height, which ultimately results in a papillomatous architecture. G-minus psoriasis with the critical papillary height (360 microm) is exchangeable with G-plus psoriasis with the same papillary height. Collectively, the model reveals a self-organizing mechanism for the characteristically-or-dered psoriatic architecture, one that is under dynamic control of epidermal remodelling.

Epidermis↗
PubMed2003

Automated monitoring of medical protocols: a secure and distributed architecture.

The control of the right application of medical protocols is a key issue in hospital environments. For the automated monitoring of medical protocols, we need a domain-independent language for their representation and a fully, or semi, autonomous system that understands the protocols and supervises their application. In this paper we describe a specification language and a multi-agent system architecture for monitoring medical protocols. We model medical services in hospital environments as specialized domain agents and interpret a medical protocol as a negotiation process between agents. A medical service can be involved in multiple medical protocols, and so specialized domain agents are independent of negotiation processes and autonomous system agents perform monitoring tasks. We present the detailed architecture of the system agents and of an important domain agent, the database broker agent, that is responsible of obtaining relevant information about the clinical history of patients. We also describe how we tackle the problems of privacy, integrity and authentication during the process of exchanging information between agents.

Artificial Intelligence↗
PubMedDate not supplied

Metareasoning and meta-level learning in a hybrid knowledge-based architecture.

Ahybrid knowledge-based architecture integrates different problem solvers for the same (sub)task through a control unit operating at a meta-level, the metareasoner, which coordinates the use of, and the communication between, the different problem solvers. A problem solver is defined to be an association between a knowledge intensive (sub)task, an inference mechanism and a knowledge domain view operated by the inference mechanism in order to perform the (sub)task. Important issues in a hybrid system are the metareasoning and learning aspects. Metareasoning encompasses the functions performed by the metareasoner, while learning reflects the ability of the system to evolve on the basis of its experiences in problem solving. Learning occurs at different levels, learning at the meta-level and learning at the level of the specific problem solvers. Meta-level learning reflects the ability of the metareasoner to improve the overall performance of the hybrid system by improving the efficiency of meta-level tasks. Meta-level tasks include the initial planning of problem solving strategies and the dynamic adaptation of chosen strategies depending on new events occurring dynamically during problem solving. In this paper we concentrate on metareasoning and meta-level learning in the context of a hybrid architecture. The theoretical arguments presented in the paper are demonstrated in practice through a hybrid knowledge-based prototype system for the domain of breast cancer histopathology.

Algorithms↗
PubMed2002

The architecture of growing compact bone in the dog: visualization by 3D-reconstruction of histological sections.

The three-dimensional architecture of growing canine compact bone was investigated by generating a composite virtual model including both cellular and extracellular elements. Serial trichrome-stained histological sections were prepared from samples of the diaphysis of the humerus of six week old Beagle puppies. Identical fields of vision were recorded in consecutive sections, preprocessed in Adobe Photoshop 5.5, and then transferred into the program AVS/Express for 3D-reconstruction and visualization. Based on two different but integrated reconstruction techniques a hybrid model was created, depicting shape and orientation of bony trabeculae as well as the distribution of osteoblasts. This virtual model could be explored and navigated in with real time interactivity, and thus disclosed the specific architectural characteristics of immature compact bone. In young puppies, bone tissue of the corpus humeri forms a labyrinth of communicating osseous walls which are covered with a multitude of osteoblasts. These results are discussed in relation to findings in adult dogs.

Animals↗