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PubMed2003

Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissue.

Microarray techniques have made it possible to observe the expression of thousands of genes simultaneously. They have recently been applied to study gene expression patterns in tissue samples. This may lead to highly desirable improvements in the diagnosis and treatment of human diseases. Statistical and machine learning methods have recently been used to classify cancer tissue based on gene expression data. Although some of these methods have achieved a high degree of accuracy, they generally lack transparency in their classification process. This, however, is crucial for the application in the medical field. In order to overcome this obstacle, we used knowledge-based neurocomputing (KBN), since KBN seeks to gain knowledge that is comprehensible to humans. In particular, we applied evolving fuzzy neural networks (EFuNNs) to classify cancer tissue, which is illustrated on the case studies of leukaemia and colon cancer. EFuNNs belong to the evolving connectionist system paradigm (ECOS) that has been recently introduced. They are well suited for adaptive learning and knowledge discovery. Fuzzy logic rules can be extracted from the trained networks and offer knowledge about the classification process in an easily accessible form. These rules point to genes that are strongly associated with specific types of cancer and may be used for the development of new tests and treatment discoveries.

Algorithms↗

PubMed2004

Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features.

A computer-aided diagnosis (CAD) algorithm identifying breast nodule malignancy using multiple ultrasonography (US) features and artificial neural network (ANN) classifier was developed from a database of 584 histologically confirmed cases containing 300 benign and 284 malignant breast nodules. The features determining whether a breast nodule is benign or malignant were extracted from US images through digital image processing with a relatively simple segmentation algorithm applied to the manually preselected region of interest. An ANN then distinguished malignant nodules in US images based on five morphological features representing the shape, edge characteristics, and darkness of a nodule. The structure of ANN was selected using k-fold cross-validation method with k = 10. The ANN trained with randomly selected half of breast nodule images showed the normalized area under the receiver operating characteristic curve of 0.95. With the trained ANN, 53.3% of biopsies on benign nodules can be avoided with 99.3% sensitivity. Performance of the developed classifier was reexamined with new US mass images in the generalized patient population of total 266 (167 benign and 99 malignant) cases. The developed CAD algorithm has the potential to increase the specificity of US for characterization of breast lesions.

Adolescent↗

PubMed2003

Generalized Kohonen's competitive learning algorithms for ophthalmological MR image segmentation.

Kohonen's self-organizing map is a two-layer feedforward competitive learning network. It has been used as a competitive learning clustering algorithm. In this paper, we generalize Kohonen's competitive learning (KCL) algorithm with fuzzy and fuzzy-soft types called fuzzy KCL (FKCL) and fuzzy-soft KCL (FSKCL). These generalized KCL algorithms fuse the competitive learning with soft competition and fuzzy c-means (FCM) membership functions. We then apply these generalized KCLs to MRI and MRA ophthalmological segmentations. These KCL-based MRI segmentation techniques are useful in reducing medical image noise effects using a learning mechanism. They may be particularly helpful in clinical diagnosis. Two real cases with MR image data recommended by an ophthalmologist are examined. First case is a patient with Retinoblastoma in her left eye, an inborn malignant neoplasm of the retina frequently metastasis beyond the lacrimal cribrosa. The second case is a patient with complete left side oculomotor palsy immediately after a motor vehicle accident. Her brain MRI with MRA, skull routine, orbital CT, and cerebral angiography did not reveal brainstem lesions, skull fractures, or vascular anomalies. These generalized KCL algorithms were used in segmenting the ophthalmological MRIs. KCL, FKCL and FSKCL comparisons are made. Overall, the FSKCL algorithm is recommended for use in MR image segmentation as an aid to small lesion diagnosis.

Algorithms↗

PubMed2004

New methods for the computer-assisted 3-D reconstruction of neurons from confocal image stacks.

Exact geometrical reconstructions of neuronal architecture are indispensable for the investigation of neuronal function. Neuronal shape is important for the wiring of networks, and dendritic architecture strongly affects neuronal integration and firing properties as demonstrated by modeling approaches. Confocal microscopy allows to scan neurons with submicron resolution. However, it is still a tedious task to reconstruct complex dendritic trees with fine structures just above voxel resolution. We present a framework assisting the reconstruction. User time investment is strongly reduced by automatic methods, which fit a skeleton and a surface to the data, while the user can interact and thus keeps full control to ensure a high quality reconstruction. The reconstruction process composes a successive gain of metric parameters. First, a structural description of the neuron is built, including the topology and the exact dendritic lengths and diameters. We use generalized cylinders with circular cross sections. The user provides a rough initialization by marking the branching points. The axes and radii are fitted to the data by minimizing an energy functional, which is regularized by a smoothness constraint. The investigation of proximity to other structures throughout dendritic trees requires a precise surface reconstruction. In order to achieve accuracy of 0.1 microm and below, we additionally implemented a segmentation algorithm based on geodesic active contours that allow for arbitrary cross sections and uses locally adapted thresholds. In summary, this new reconstruction tool saves time and increases quality as compared to other methods, which have previously been applied to real neurons.

Algorithms↗

PubMed2003

Inhibitory cotransmission or after-hyperpolarizing potentials can regulate firing in recurrent networks with excitatory metabotropic transmission.

Recurrent networks of neurons communicating via excitatory connections are common in the nervous system. In the absence of mechanisms to control firing (collectively termed negative feedback), these networks are likely to be bistable and unable to meaningfully encode input signals. In most recurrent circuits, negative feedback is provided by a specialized subpopulation of interneurons, but such neurons are absent from some systems, which therefore require other forms of negative feedback. One such circuit is found within the enteric nervous system of the intestine, where AH/Dogiel type II neurons are interconnected via excitatory synapses acting through metabotropic receptors to produce slow excitatory postsynaptic potentials (slow EPSPs). Negative feedback in this recurrent network may come from either inhibitory postsynaptic potentials arising from the terminals that produce slow EPSPs or from the after hyperpolarizing potentials (AHPs) characteristic of these neurons. We have examined these possibilities using mathematical analysis, based on the Wilson-Cowan model, and computer simulations. Analysis of steady states showed that, under appropriate conditions, both types of negative feedback can provide robust regulation of firing allowing the networks to encode input signals. Numerical simulations were performed using large, anatomically realistic networks with realistic models for metabotropic transmission and suppression of the AHP. In the presence of constant exogenous input, parameters controlling aspects of synaptic events were varied, confirming the analytical results for static stimuli. The simulated networks also responded to time varying inputs in a manner consistent with known physiology. In addition, simulation revealed that neurons in networks with inhibitory contransmission fired in erratic bursts, a phenomenon observed in neurons in unparalysed tissue. Thus, either inhibitory contransmission or AHPs, or both, can allow recurrent networks of AH/Dogiel type II neurons to encode ongoing inputs in a biologically useful way. These neurons appear to be intrinsic primary afferent neurons (IPANs), which implies that the IPANs in a region act in a coordinated fashion.

Action Potentials↗

PubMed2006

Cortical network modeling: analytical methods for firing rates and some properties of networks of LIF neurons.

The circuitry of cortical networks involves interacting populations of excitatory (E) and inhibitory (I) neurons whose relationships are now known to a large extent. Inputs to E- and I-cells may have their origins in remote or local cortical areas. We consider a rudimentary model involving E- and I-cells. One of our goals is to test an analytic approach to finding firing rates in neural networks without using a diffusion approximation and to this end we consider in detail networks of excitatory neurons with leaky integrate-and-fire (LIF) dynamics. A simple measure of synchronization, denoted by S(q), where q is between 0 and 100 is introduced. Fully connected E-networks have a large tendency to become dominated by synchronously firing groups of cells, except when inputs are relatively weak. We observed random or asynchronous firing in such networks with diverse sets of parameter values. When such firing patterns were found, the analytical approach was often able to accurately predict average neuronal firing rates. We also considered several properties of E-E networks, distinguishing several kinds of firing pattern. Included were those with silences before or after periods of intense activity or with periodic synchronization. We investigated the occurrence of synchronized firing with respect to changes in the internal excitatory postsynaptic potential (EPSP) magnitude in a network of 100 neurons with fixed values of the remaining parameters. When the internal EPSP size was less than a certain value, synchronization was absent. The amount of synchronization then increased slowly as the EPSP amplitude increased until at a particular EPSP size the amount of synchronization abruptly increased, with S(5) attaining the maximum value of 100%. We also found network frequency transfer characteristics for various network sizes and found a linear dependence of firing frequency over wide ranges of the external afferent frequency, with non-linear effects at lower input frequencies. The theory may also be applied to sparsely connected networks, whose firing behaviour was found to change abruptly as the probability of a connection passed through a critical value. The analytical method was also found to be useful for a feed-forward excitatory network and a network of excitatory and inhibitory neurons.

Action Potentials↗

PubMed2001

A fast nonlinear method for parametric imaging of myocardial perfusion by dynamic (13)N-ammonia PET.

UNLABELLED: A parametric image of myocardial perfusion (mL/min/g) is a quantitative image generated by fitting a tracer kinetic model to dynamic (13)N-ammonia PET data on a pixel-by-pixel basis. There are several methods for such parameter estimation problems, including weighted nonlinear regression (WNLR) and a fast linearizing method known as Patlak analysis. Previous work showed that sigmoidal networks can be used for parameter estimation of mono- and biexponential models. The method used in this study is a hybrid of WNLR and sigmoidal networks called nonlinear regression estimation (NRE). The purpose of the study is to compare NRE with WNLR and Patlak analysis for parametric imaging of perfusion in the canine heart by (13)N-ammonia PET. METHODS: A simulation study measured the statistical performance of NRE, WNLR, and Patlak analysis for a probabilistic model of time-activity curves. Four canine subjects were injected with 740 MBq (13)N-ammonia and scanned dynamically. Images were reconstructed with filtered backprojection and resliced into short-axis cuts. Parametric images of a single midventricular plane per subject were generated by NRE, WNLR, and Patlak analysis. Small regions of interest (ROIs) were drawn on each parametric image (8 ROIs per subject for a total of 32). RESULTS: For the simulation study, the median absolute value of the relative error for a perfusion value of 1.0 mL/min/g was 16.6% for NRE, 17.9% for WNLR, 19.5% for Patlak analysis, and 14.5% for an optimal WNLR method (computable by simulation only). All methods are unbiased conditioned on a wide range of perfusion values. For the canine studies, the least squares line fits comparing NRE (y) and Patlak analysis (z) with WNLR (x) for all 32 ROIs were y = 1.02x - 0.028 and z = 0.90x + 0.019, respectively. Both NRE and Patlak analysis generate 128 x 128 parametric images in seconds. CONCLUSION: The statistical performance of NRE is competitive with WNLR and superior to Patlak analysis for parametric imaging of myocardial perfusion. NRE is a fast nonlinear alternative to Patlak analysis and other fast linearizing methods for parametric imaging. NRE should be applicable to many other tracers and tracer kinetic models.

Ammonia↗

PubMedDate not supplied

A neural network trained to identify the presence of myocardial infarction bases some decisions on clinical associations that differ from accepted clinical teaching.

An artificial neural network trained to identify the presence of myocardial infarction has been shown to function with a high degree of accuracy. The effects on network diagnosis of some of the clinical input variables used by this network have previously been shown to be distributed over two distinct maxima. Analysis of the basis for this distribution by studying the specific patterns in which these variables had significantly different impacts on network diagnosis revealed that the differential impacts were due to the contexts in which the variables whose effects were bimodally distributed were placed. These contexts were defined by the values of the other input data used by the network. In a number of instances, the clinical relationships implied by these associations were divergent from prior knowledge about factors predictive of myocardial infarction. One implication of these findings is that this network, which has been shown to perform with a high degree of diagnostic accuracy, may be doing so by identifying relationships between inputted information that are divergent from accepted teaching.

Adult↗

PubMed2003

Dynamical and integrative cell signaling: challenges for the new biology.

Years of careful experimental analysis have revealed that signaling molecules are organized into complex networks of biochemical reactions exquisitely regulated in time and space to provide a cell with high-fidelity information about an extremely noisy and volatile environment. A new view of signaling networks as systems consisting of multiple complex elements interacting in a multifarious fashion is emerging, a view that conflicts with the single-gene or protein-centric approach common in biological research. The postgenomic era has brought about a different, network-centric methodology of analysis, suddenly forcing researchers toward the opposite extreme of complexity, where the networks being explored are, to a certain extent, intractable and uninterpretable. Both the cartoons of simple pathways and the very large "hair-ball" diagrams of large intracellular networks are also representations of static worlds, superficially devoid of dynamics and chemistry. These representations are often viewed as being analogous to stably linked computer and neural networks rather than dynamically changing networks of chemical interactions, where the notions of concentration, compartmentalization, and diffusion may be the primary determinants of connectivity. Arguably, the systems biology approach, relying on computational modeling coupled with various experimental techniques and methodologies, will be an essential component of analysis of the behavior of signal transduction pathways. Combining the dynamical view of rapidly evolving responses and the structural view arising from high-throughput analyses of the interacting species will be the best approach toward efforts toward greater understanding of intracellular signaling processes.

Cell Communication↗

PubMed2007

Is there a tipping point in neuronal ensembles during learning?

Learning is important for humans and can be disrupted by disease. However, the essence of how learning may be represented within a neuronal network is still elusive. Spike trains generated by neurons have been demonstrated to carry information which is relevant for learning. The present study uses well-established mutual information (MI) analysis techniques to better understand learning within neuronal ensembles. Spike trains in tetrode recordings from the dorso-lateral striatum were used for computing MI as rats learnt a T-maze procedural task. We demonstrate that in in-vivo recordings the growth of MI is reflected in the behavioral response as learning proceeds. These changes in MI are seen to correspond to three phases, a low MI value, namely early learning, a rapid increase in MI value, task-acquisition and stabilization of MI, over-training. Over multiple training sessions, small changes in MI within the neuronal network suddenly produce a big change in ensemble MI during the task acquisition phase. This phase represents the "tipping point" in the neuronal network where the MI growth builds habits during motor learning in the striatum.

Action Potentials↗

PubMed2003

Modulation of excitatory synaptic coupling facilitates synchronization and complex dynamics in a biophysical model of neuronal dynamics.

In this paper, complex dynamical synchronization in a non-linear model of a neural system is studied, and the computational significance of the behaviours is explored. The local neural dynamics is determined by voltage- and ligand-gated ion channels and feedback between densely interconnected excitatory and inhibitory neurons. A mesoscopic array of local networks is modelled by introducing coupling between the local networks via weak excitatory-to-excitatory connectivity. It is shown that with modulation of this long-range synaptic coupling, the system undergoes a transition from independent oscillations to stable chaotic synchronization. Between these states exists a 'weakly' stable state associated with complex, intermittent behaviour in the temporal domain and clusters of synchronous regions in the spatial domain. The paper concludes with a discussion of the putative relevance of such processes in the brain, including the role of neuromodulatory systems and the mechanisms underlying sensory perception, adaptation, computation and complexity.

Action Potentials↗

PubMed2005

Inferring network activity from synaptic noise.

During intense network activity in vivo, cortical neurons are in a high-conductance state, in which the membrane potential (V(m)) is subject to a tremendous fluctuating activity. Clearly, this "synaptic noise" contains information about the activity of the network, but there are presently no methods available to extract this information. We focus here on this problem from a computational neuroscience perspective, with the aim of drawing methods to analyze experimental data. We start from models of cortical neurons, in which high-conductance states stem from the random release of thousands of excitatory and inhibitory synapses. This highly complex system can be simplified by using global synaptic conductances described by effective stochastic processes. The advantage of this approach is that one can derive analytically a number of properties from the statistics of resulting V(m) fluctuations. For example, the global excitatory and inhibitory conductances can be extracted from synaptic noise, and can be related to the mean activity of presynaptic neurons. We show here that extracting the variances of excitatory and inhibitory synaptic conductances can provide estimates of the mean temporal correlation-or level of synchrony-among thousands of neurons in the network. Thus, "probing the network" through intracellular V(m) activity is possible and constitutes a promising approach, but it will require a continuous effort combining theory, computational models and intracellular physiology.

Animals↗

PubMed2005

Precision constrained stochastic resonance in a feedforward neural network.

Stochastic resonance (SR) is a phenomenon in which the response of a nonlinear system to a subthreshold information-bearing signal is optimized by the presence of noise. By considering a nonlinear system (network of leaky integrate-and-fire (LIF) neurons) that captures the functional dynamics of neuronal firing, we demonstrate that sensory neurons could, in principle harness SR to optimize the detection and transmission of weak stimuli. We have previously characterized this effect by use of signal-to-noise ratio (SNR). Here in addition to SNR, we apply an entropy-based measure (Fisher information) and compare the two measures of quantifying SR. We also discuss the performance of these two SR measures in a full precision floating point model simulated in Java and in a precision limited integer model simulated on a field programmable gate array (FPGA). We report in this study that stochastic resonance which is mainly associated with floating point implementations is possible in both a single LIF neuron and a network of LIF neurons implemented on lower resolution integer based digital hardware. We also report that such a network can improve the SNR and Fisher information of the output over a single LIF neuron.

Action Potentials↗

PubMed2004

Design and characterization of cellular automata based associative memory for pattern recognition.

This paper reports a cellular automata (CA) based model of associative memory. The model has been evolved around a special class of CA referred to as generalized multiple attractor cellular automata (GMACA). The GMACA based associative memory is designed to address the problem of pattern recognition. Its storage capacity is found to be better than that of Hopfield network. The GMACA are configured with nonlinear CA rules that are evolved through genetic algorithm (GA). Successive generations of GA select the rules at the edge of chaos. The study confirms the potential of GMACA to perform complex computations like pattern recognition at the edge of chaos.

Algorithms↗

PubMed2003

The use of artificial neural networks for the selection of the most appropriate formulation and processing variables in order to predict the in vitro dissolution of sustained release minitablets.

The objective of this work was to apply artificial neural networks (ANNs) to examine the relative importance of various factors, both formulation and process, governing the in-vitro dissolution from enteric-coated sustained release (SR) minitablets. Input feature selection (IFS) algorithms were used in order to give an estimate of the relative importance of the various formulation and processing variables in determining minitablet dissolution rate. Both forward and backward stepwise algorithms were used as well as genetic algorithms. Networks were subsequently trained using the back propagation algorithm in order to check whether or not the IFS process had correctly located any unimportant inputs. IFS gave consistent rankings for the importance of the various formulation and processing variables in determining the release of drug from minitablets. Consistent ranking was achieved for both indices of the release process; ie, the time taken for release to commence through the enteric coat (T(lag)) and that for the drug to diffuse through the SR matrix of the minitablet into the dissolution medium (T9(0-10)). In the case of the T(lag) phase, the main coating parameters, along with the original batch blend size and the blend time with lubricant, were found to have most influence. By contrast, with the T(90-10 phase), the amounts of matrix forming polymer and direct compression filler were most important. In the subsequent training of the ANNs, removal of inputs regarded as less important led to improved network performance. ANNs were capable of ranking the relative importance of the various formulations and processing variables that influenced the release rate of the drug from minitablets. This could be done for all main stages of the release process. Subsequent training of the ANN verified that removal of less relevant inputs from the training process led to an improved performance from the ANN.

Artificial Intelligence↗

PubMed1998

Statistically efficient estimation using population coding.

Coarse codes are widely used throughout the brain to encode sensory and motor variables. Methods designed to interpret these codes, such as population vector analysis, are either inefficient (the variance of the estimate is much larger than the smallest possible variance) or biologically implausible, like maximum likelihood. Moreover, these methods attempt to compute a scalar or vector estimate of the encoded variable. Neurons are faced with a similar estimation problem. They must read out the responses of the presynaptic neurons, but, by contrast, they typically encode the variable with a further population code rather than as a scalar. We show how a nonlinear recurrent network can be used to perform estimation in a near-optimal way while keeping the estimate in a coarse code format. This work suggests that lateral connections in the cortex may be involved in cleaning up uncorrelated noise among neurons representing similar variables.

Computer Simulation↗

PubMed2003

Tools for intelligent control: fuzzy controllers, neural networks and genetic algorithms.

The goal of this expository paper is to bring forth the basic current elements of soft computing (fuzzy logic, neural networks, genetic algorithms and genetic programming) and the current applications in intelligent control. Fuzzy sets and fuzzy logic and their applications to control systems have been documented. Other elements of soft computing, such as neural networks and genetic algorithms, are also treated for the novice reader. Each topic will have a number of relevant references of as many key contributors as possible.

Algorithms↗

PubMed2006

Polychronization: computation with spikes.

We present a minimal spiking network that can polychronize, that is, exhibit reproducible time-locked but not synchronous firing patterns with millisecond precision, as in synfire braids. The network consists of cortical spiking neurons with axonal conduction delays and spike-timing-dependent plasticity (STDP); a ready-to-use MATLAB code is included. It exhibits sleeplike oscillations, gamma (40 Hz) rhythms, conversion of firing rates to spike timings, and other interesting regimes. Due to the interplay between the delays and STDP, the spiking neurons spontaneously self-organize into groups and generate patterns of stereotypical polychronous activity. To our surprise, the number of coexisting polychronous groups far exceeds the number of neurons in the network, resulting in an unprecedented memory capacity of the system. We speculate on the significance of polychrony to the theory of neuronal group selection (TNGS, neural Darwinism), cognitive neural computations, binding and gamma rhythm, mechanisms of attention, and consciousness as "attention to memories."

Attention↗