[Does consciousness reside in computer?--On the neuro-computing point of view].
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Animals↗
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Results for “Neural Networks, Computer”
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Model of thalamocortical slow-wave sleep oscillations and transitions to activated States.
During natural slow-wave sleep (SWS) in nonanesthetized cats, silent (down) states alternate with active (up) states; the down states are absent during rapid-eye-movement sleep and waking. Oscillations (<1 Hz) in SWS and transformation to an activated awake state were investigated with intracellular recordings in vivo and with computational models of the corticothalamic system. Occasional summation of the miniature EPSPs during the hyperpolarized (silent) phase of SWS oscillation activated the persistent sodium current and depolarized the membrane of cortical pyramidal (PY) cells sufficiently for spike generation. In the model, this triggered the active phase, which was maintained by lateral PY-PY excitation and persistent sodium current. Progressive depression of the excitatory interconnections and activation of Ca2+-dependent K+ current led to termination of the 20-25 Hz activity after 500-1000 msec. Including thalamocortical (TC) and thalamic reticular neurons in the model increased the duration of the active epochs up to 1-1.5 sec and introduced waning spindle sequences. An increase in acetylcholine activity, which is associated with activated states, was modeled by the reduction in the K+ leak current in PY and TC cells and by a decrease in intracortical PY-PY synaptic conductances. These changes eliminated the hyperpolarizing phases of network activity and transformed cortical neurons to tonic firing at 15-20 Hz. During the transition from SWS to the activated state, the input resistance of cortical neurons gradually increased and, in a fully activated state, reached the same or even higher values as during silent phases of SWS oscillations. The model describes many essential features of SWS and activated states in the thalamocortical system as well as the transition between them.
Action Potentials↗
Asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS).
This paper presents an asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS) that directly extends the SuPFuNIS model by permitting signal and weight fuzzy sets to be modeled by asymmetric Gaussian membership functions. The asymmetric subsethood-product network admits both numeric as well as linguistic inputs. Input nodes, which act as tunable feature fuzzifiers, fuzzify numeric inputs with asymmetric Gaussian fuzzy sets; and linguistic inputs are presented as is. The antecedent and consequent labels of standard fuzzy if-then rules are represented as asymmetric Gaussian fuzzy connection weights of the network. The model uses mutual subsethood based activation spread and a product aggregation operator that works in conjunction with volume defuzzification in a gradient descent learning framework. Despite the increase in the number of free parameters, the proposed model performs better than SuPFuNIS, on various benchmarking problems, both in terms of the performance accuracy and architectural economy and compares excellently with other various existing models with a performance better than most of them.
Algorithms↗
Deterministic projection by growing cell structure networks for visualization of high-dimensionality datasets.
Recent advances in clinical proteomics data acquisition have led to the generation of datasets of high complexity and dimensionality. We present here a visualization method for high-dimensionality datasets that makes use of neuronal vectors of a trained growing cell structure (GCS) network for the projection of data points onto two dimensions. The use of a GCS network enables the generation of the projection matrix deterministically rather than randomly as in random projection. Three datasets were used to benchmark the performance and to demonstrate the use of this deterministic projection approach in real-life scientific applications. Comparisons are made to an existing self-organizing map projection method and random projection. The results suggest that deterministic projection outperforms existing methods and is suitable for the visualization of datasets of very high dimensionality.
Algorithms↗
Learning multiple causes by competition enhanced least mean square error reconstruction.
In this paper we studied a self-organization principle that input should be best reconstructed from a factorial distributed hidden representation, which has been addressed in the literature recently. An auto-encoder network is trained by the Least Mean Square Error Reconstruction (LMSER) while the redundance in the representation is reduced by a proposed anti-Hebbian scheme, in which a penalty term called Receptive Field Overlapping Index (RFOI) is combined into the objective function for enhancing competition among nodes in the network. Our learning scheme provides a way for balancing the cooperation and competition necessary for the self-organization process thus realizes the multiple causes model, which accounts for an observed data by combining assertions from the discovered causes or features in the data. Our experiment results demonstrate again the powerful information processing capability inherent to the popular weighted sum followed by sigmoid squashing. Comparing with previous probability theory based multiple causes models, our scheme is much easier to implement and quite reliable.
Algorithms↗
A distributed environment for the integration of multiple high-performance decision support systems into clinical workflow.
Current studies conclude that clinical decision support systems can help reduce serious medical errors. The importance of Causal Probabilistic Networks (CPNs) for constructing such systems is already well-known. However, the computational complexity of probabilistic inference, which results in unacceptably high response times, can hinder acceptance and integration into clinician workflow. This paper investigates the optimization and parallelization potential of complex CPN-based medical decision support systems and evaluates the results of implementing a parallel, high performance version of an existing decision support system concerning proper antibiotic treatment therapy. Furthermore, it discusses distributed computing techniques for making multiple high performance decision support systems available at the time and location of decision making, by exploiting computing resources residing inside, as well as outside the hospital walls optimally.
Algorithms↗
Genomic approaches for reconstructing gene networks.
Functional knowledge of individual genes encoding components of the cell signaling, metabolic and regulatory pathways is crucial to our understanding of physiology and pathophysiology. A central challenge in functional genomics is the creation of a working map delineating how eukaryotic cells coordinate and govern patterns of gene expression. This coordination is often depicted as an intertwined network or circuit of genes that alternately activate and repress each other. Multiple bioinformatic and high-throughput experimental approaches exist to aid in the reconstruction of gene networks. Albeit far from being complete, the ability to recreate gene networks from experimental data facilitates the systematic dissection of cell function at the molecular and genetic level. In this review, several different genomic technologies are discussed, and example studies that are promoting new discoveries and hypotheses are detailed.
Cluster Analysis↗
A radial basis function model of muscle stimulated with irregular inter-pulse intervals.
Paralysed muscle, or skeletal muscle which is to be used for cardiac assistance, may be given an artificial function if it is electrically stimulated to contract and the response can be adequately controlled. To design a controller, a model of the muscle or system is usually required. The most commonly used models are analogues, originating from A.V. Hill's model. However muscles exhibit many nonlinear and time-varying phenomena which, if they are to be modelled, make the analogue complex and cumbrous to work with. The system may further be complicated by pathological changes and secondary effects of stimulation. We propose that such a system can be modelled by nonlinear networks ('neural networks'). The radial basis function network (RBF) has two advantages over the better-known multi-layer perceptron (MLP). We describe the use of an RBF network to model rabbit muscle that is supramaximally stimulated at irregular inter-pulse intervals.
Animals↗
Clustering proteins into families using artificial neural networks.
An artificial neural network was used to cluster proteins into families. The network, composed of 7 x 7 neurons, was trained with the Kohonen unsupervised learning algorithm using, as inputs, matrix patterns derived from the bipeptide composition of 447 proteins, belonging to 13 different families. As a result of the training, and without any a priori indication of the number or composition of the expected families, the network self-organized the activation of its neurons into topologically ordered maps in which almost all the proteins (96.7%) were correctly clustered into the corresponding families. In a second computational experiment, a similar network was trained with one family of the previous learning set (76 cytochrome c sequences). The new neural map clustered these proteins into 25 different neurons (five in the first experiment), wherein phylogenetically related sequences were positioned close to each other. This result shows that the network can adapt the clustering resolution to the complexity of the learning set, a useful feature when working with an unknown number of clusters. Although the learning stage is time consuming, once the topological map is obtained, the classification of new proteins is very fast. Altogether, our results suggest that this novel approach may be a useful tool to organize the search for homologies in large macromolecular databases.
Algorithms↗
An effective structure learning method for constructing gene networks.
MOTIVATION: Bayesian network methods have shown promise in gene regulatory network reconstruction because of their capability of capturing causal relationships between genes and handling data with noises found in biological experiments. The problem of learning network structures, however, is NP hard. Consequently, heuristic methods such as hill climbing are used for structure learning. For networks of a moderate size, hill climbing methods are not computationally efficient. Furthermore, relatively low accuracy of the learned structures may be observed. The purpose of this article is to present a novel structure learning method for gene network discovery. RESULTS: In this paper, we present a novel structure learning method to reconstruct the underlying gene networks from the observational gene expression data. Unlike hill climbing approaches, the proposed method first constructs an undirected network based on mutual information between two nodes and then splits the structure into substructures. The directional orientations for the edges that connect two nodes are then obtained by optimizing a scoring function for each substructure. Our method is evaluated using two benchmark network datasets with known structures. The results show that the proposed method can identify networks that are close to the optimal structures. It outperforms hill climbing methods in terms of both computation time and predicted structure accuracy. We also apply the method to gene expression data measured during the yeast cycle and show the effectiveness of the proposed method for network reconstruction.
Algorithms↗
Fast remapping of sensory stimuli onto motor actions on the basis of contextual modulation.
Higher organisms can establish complex associations between sensory events and motor responses. More remarkable than their complexity, however, is that the resulting sensory-motor maps can be selectively interchanged. For example, a person who speaks English and Spanish can read aloud "con once, sin once," going effortlessly from one language to the other. What is the neural basis of this capacity? Here, a network model is presented in which multiple maps between sensory stimuli and motor actions are possible, but only one of them, depending on behavioral context, is implemented at any given time. The key is a nonlinear representation in which the gain of sensory responses is regulated by context information. Neuronal responses can indeed show variations in gain, as has been documented in the case of proprioceptive signals such as eye and head position, which can modulate visually triggered activity. However, in contrast to these, the contextual cues used here need not bear any relationship to the physical attributes of the stimuli; in particular, spatial location is irrelevant. The model thus postulates the existence of sensory neurons that are nonlinearly modulated by arbitrary context signals, a plausible and testable prediction. The proposed mechanism allows a network of neurons to effectively change the functional connectivity between its inputs and outputs and may partially explain how animals can quickly adapt their behavior to varying environmental conditions.
Computer Simulation↗
Computational algorithms and neuronal network models underlying decision processes.
Animals or humans often encounter such situations in which they must choose their behavioral responses to be made in the near or distant future. Such a decision is made through continuous and bidirectional interactions between the environment surrounding the brain and its internal state or dynamical processes. Therefore, decision making may provide a unique field of researches for studying information processing by the brain, a biological system open to information exchanges with the external world. To make a decision, the brain must analyze pieces of information given externally, past experiences in a similar situation, possible behavioral responses, and predicted outcomes of the individual responses. In this article, we review results of recent experimental and theoretical studies of neuronal substrates and computational algorithms for decision processes.
Algorithms↗
Application of artificial neural networks for reduction of false-positive detections in digital chest radiographs.
A methodology based on the fuzzy set theory and the convolution neural network (CNN) architecture is proposed to tackle the problem of reducing false-positive rate in automatic lung nodule detection. The CNN which simulates human visual mechanism was trained by a supervised back-propagation algorithm based on fuzzy membership functions. The training and testing database consists of image blocks (each 32 x 32 pixels) of suspected lung nodule areas (nodule candidates) which were generated from our pre-scanning program [1]. A linguistic label was assigned to each nodule candidate of the training set, then the label was converted to a membership value through a pre-defined membership function and used as teaching signal (desired outputs) during the network learning. Before the nodule candidate was fed to the network input, it was pre-processed to reduce the complex background noise and the contrast discrepancy resulted from film development. During the network testing phase, a defuzzification process was applied to decipher the trained network's output triggered by the nodule candidate in the testing set. Finally, a Receiver Operating Characteristic (ROC) analysis was used to evaluate the CNN's performance based on the defuzzified output of the testing database. Preliminary results showed an average Az (the performance index) of 0.84 which is equivalent to 0.80 true-positive detection (sensitivity) with an average 2-3 false-positive detections per chest image.
Computer Simulation↗
CrossNets: high-performance neuromorphic architectures for CMOL circuits.
The exponential, Moore's Law, progress of electronics may be continued beyond the 10-nm frontier if the currently dominant CMOS technology is replaced by hybrid CMOL circuits combining a silicon MOSFET stack and a few layers of parallel nanowires connected by self-assembled molecular electronic devices. Such hybrids promise unparalleled performance for advanced information processing, but require special architectures to compensate for specific features of the molecular devices, including low voltage gain and possible high fraction of faulty components. Neuromorphic networks with their defect tolerance seem the most natural way to address these problems. Such circuits may be trained to perform advanced information processing including (at least) effective pattern recognition and classification. We are developing a family of distributed crossbar network (CrossNet) architectures that permit the combination of high connectivity neuromorphic circuits with high component density. Preliminary estimates show that this approach may eventually allow us to place a cortex-scale circuit with about 10(10) neurons and about 10(14) synapses on an approximately 10 x 10 cm(2) silicon wafer. Such systems may provide an average cell-to-cell latency of about 20 nsec and, thus, perform information processing and system training (possibly including self-evolution after initial training) at a speed that is approximately six orders of magnitude higher than in its biological prototype and at acceptable power dissipation.
Biomimetic Materials↗
Pruning of a large network by optimal brain damage and surgeon: an example from biological sequence analysis.
Optimal Brain Damage (OBD) and Optimal Brain Surgeon (OBS) represent two popular pruning procedures; however, pruning large networks trained on voluminous data sets using these methods easily becomes intractable. We present a number of approximations and discuss practical issues in real-world pruning, and use as an example a network trained to predict protein coding regions in DNA sequences. The efficiency of OBS on large networks is compared to OBD, and it turns out that OBD is preferable to OBS, since more weights can be removed using less computational effort.
Base Sequence↗
Objective evaluation of two markers of HIV-1 infection (p24 antigen concentration and CD4+ cell counts) by a self organizing neural network.
The aim of the present work is to obtain groups of patients with similar profiles of p24 antigen concentration and of CD4+ cell counts. These two markers were chosen because their evaluation represents a significant step in the clinical follow up of HIV-1 infected subjects. The detection methods for p24 antigen concentration and for CD4+ cell counts are well assessed and guarantee easy reproducibility of data obtained in different laboratories. A set of observations with the same time intervals were derived from a continuous function obtained for each patient by a back-propagation neural net trained on the raw data from the patient. The classifications were obtained by a Kohonen neural net trained in three ways: with p24 antigen profiles only, with CD4+ cell count profiles only and with both sets of profiles. The results show that the clustering fashion of the two parameters closely resembles the clustering fashion of CD4+ only, rather than that of p24Ag, both with reference to cluster formation and with reference to distances between clusters.
Acquired Immunodeficiency Syndrome↗
Fast evaluation of fluctuations in biochemical networks with the linear noise approximation.
Biochemical networks in single cells can display large fluctuations in molecule numbers, making mesoscopic approaches necessary for correct system descriptions. We present a general method that allows rapid characterization of the stochastic properties of intracellular networks. The starting point is a macroscopic description that identifies the system's elementary reactions in terms of rate laws and stoichiometries. From this formulation follows directly the stationary solution of the linear noise approximation (LNA) of the Master equation for all the components in the network. The method complements bifurcation studies of the system's parameter dependence by providing estimates of sizes, correlations, and time scales of stochastic fluctuations. We describe how the LNA can give precise system descriptions also near macroscopic instabilities by suitable variable changes and elimination of fast variables.
Artifacts↗
Attractor dynamics in a modular network model of neocortex.
Starting from the hypothesis that the mammalian neocortex to a first approximation functions as an associative memory of the attractor network type, we formulate a quantitative computational model of neocortical layers 2/3. The model employs biophysically detailed multi-compartmental model neurons with conductance based synapses and includes pyramidal cells and two types of inhibitory interneurons, i.e., regular spiking non-pyramidal cells and basket cells. The simulated network has a minicolumnar as well as a hypercolumnar modular structure and we propose that minicolumns rather than single cells are the basic computational units in neocortex. The minicolumns are represented in full scale and synaptic input to the different types of model neurons is carefully matched to reproduce experimentally measured values and to allow a quantitative reproduction of single cell recordings. Several key phenomena seen experimentally in vitro and in vivo appear as emergent features of this model. It exhibits a robust and fast attractor dynamics with pattern completion and pattern rivalry and it suggests an explanation for the so-called attentional blink phenomenon. During assembly dynamics, the model faithfully reproduces several features of local UP states, as they have been experimentally observed in vitro, as well as oscillatory behavior similar to that observed in the neocortex.
Action Potentials↗
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