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At least 1,063 records · Page 59Linked to original sources
PubMed2006

A new synaptic plasticity rule for networks of spiking neurons.

In this paper, we describe a new Synaptic Plasticity Activity Rule (SAPR) developed for use in networks of spiking neurons. Such networks can be used for simulations of physiological experiments as well as for other computations like image analysis. Most synaptic plasticity rules use artificially defined functions to modify synaptic connection strengths. In contrast, our rule makes use of the existing postsynaptic potential values to compute the value of adjustment. The network of spiking neurons we consider consists of excitatory and inhibitory neurons. Each neuron is implemented as an integrate-and-fire model that accurately mimics the behavior of biological neurons. To test performance of our new plasticity rule we designed a model of a biologically-inspired signal processing system, and used it for object detection in eye images of diabetic retinopathy patients, and lung images of cystic fibrosis patients. The results show that the network detects the edges of objects within an image, essentially segmenting it. Our ultimate goal, however, is not the development of an image segmentation tool that would be more efficient than nonbiological algorithms, but developing a physiologically correct neural network model that could be applied to a wide range of neurological experiments. We decided to validate the SAPR by using it in a network of spiking neurons for image segmentation because it is easy to visually assess the results. An important thing is that image segmentation is done in an entirely unsupervised way.

Algorithms↗
PubMed1998

Large neural network simulations on multiple hardware platforms.

To efficiently simulate very large networks of interconnected neurons, particular consideration has to be given to the computer architecture being used. This article presents techniques for implementing simulators for large neural networks on a number of different computer architectures. The neuronal simulation task and the computer architectures of interest are first characterized, and the potential bottlenecks are highlighted. Then we describe the experience gained from adapting an existing simulator, SWIM, to two very different architectures-vector computers and multiprocessor workstations. This work lead to the implementation of a new simulation library, SPLIT, designed to allow efficient simulation of large networks on several architectures. Different computer architectures put different demands on the organization of both data structures and computations. Strict separation of such architecture considerations from the neuronal models and other simulation aspects makes it possible to construct both portable and extendible code.

Algorithms↗
PubMed2006

Prediction of continuous B-cell epitopes in an antigen using recurrent neural network.

B-cell epitopes play a vital role in the development of peptide vaccines, in diagnosis of diseases, and also for allergy research. Experimental methods used for characterizing epitopes are time consuming and demand large resources. The availability of epitope prediction method(s) can rapidly aid experimenters in simplifying this problem. The standard feed-forward (FNN) and recurrent neural network (RNN) have been used in this study for predicting B-cell epitopes in an antigenic sequence. The networks have been trained and tested on a clean data set, which consists of 700 non-redundant B-cell epitopes obtained from Bcipep database and equal number of non-epitopes obtained randomly from Swiss-Prot database. The networks have been trained and tested at different input window length and hidden units. Maximum accuracy has been obtained using recurrent neural network (Jordan network) with a single hidden layer of 35 hidden units for window length of 16. The final network yields an overall prediction accuracy of 65.93% when tested by fivefold cross-validation. The corresponding sensitivity, specificity, and positive prediction values are 67.14, 64.71, and 65.61%, respectively. It has been observed that RNN (JE) was more successful than FNN in the prediction of B-cell epitopes. The length of the peptide is also important in the prediction of B-cell epitopes from antigenic sequences. The webserver ABCpred is freely available at www.imtech.res.in/raghava/abcpred/.

Amino Acid Sequence↗
PubMed2000

Artificial neural networks in liquid chromatography: efficient and improved quantitative structure-retention relationship models.

The application of the principal neural network architecture, namely the multilayer perceptron (MLP), has been developed for obtaining sufficient quantitative structure-retention relationships (QSRR) with high accuracy. The present study is an extension to the excellent study of Cserhati et al. [LC-GC Int., 11 (1998) 240] for the retention behavior of solutes based on their structure. To this end, a dataset of 25 substances as solutes to two different stationary phases (polyethylene-silica and polyethylene-alumina) were analyzed to their structural descriptors and related to their retention behavior as expressed by the logarithms of their capacity factors (log k'). The results were compared to those of Cserhati et al. who studied the same problem using as many as ten different equations based on multiple regression analysis. In the present study a series of new and improved algorithms other than the 'old-fashioned' and problematic steepest descent were examined for training the MLP networks. The proposed methods led to substantial gain in both the prediction ability and the computation speed of the resulting models. For the development and evaluation of the artificial neural network (ANN) systems the same (eight) descriptors proposed by Cserhati were used also in this study. Furthermore, the results were compared to those produced from classical linear multivariate regression such as partial least squares regression (PLS). Some of the proposed ANN models diminished the number of outliers, during their implementation to unseen data (solutes), to zero.

Algorithms↗
PubMed1998

A software sensor using neural networks for detection of patient workload.

The morphology of intracardiac electrograms (IEGMs) was used for pacemaker patient workload estimation. The body posture also was studied as another characteristic. The IEGMs were obtained and recorded via temporary transcutaneous leads connected to the implanted pacemaker. IEGMs were recorded during exercise and at rest. Recordings at rest were performed in different body positions. The morphology was analyzed visually in order to observe changes due to workload and posture. The recordings were digitized and processed by a computer-simulated neural network. The network was used as an automatic IEGM classifier based on the morphology. Our results show that the morphology of the IEGM may be used as an indicator of patient workload and body posture. The necessary information is found mainly in the ST segment. We conclude that neural networks seem to be useful in an active cardiac device.

Aged↗
PubMed1999

[Do neural networks revolutionize our predictive abilities?].

This paper claims for a rational treatment of patient data. It investigates data analysis with the aid of artificial neural networks. Successful example applications show that human diagnosis abilities are significantly worse than those of neural diagnosis systems. For the example of a newer architecture using RBF nets the basic functionality is explained and it is shown how human and neural expertise can be coupled. Finally, the applications and problems of this kind of systems are discussed.

Artificial Intelligence↗
PubMed2006

Bump formation in a binary attractor neural network.

The conditions for the formation of local bumps in the activity of binary attractor neural networks with spatially dependent connectivity are investigated. We show that these formations are observed when asymmetry between the activity during the retrieval and learning is imposed. An analytical approximation for the order parameters is derived. The corresponding phase diagram shows a relatively large and stable region where this effect is observed, although critical storage and information capacities drastically decrease inside that region. We demonstrate that the stability of the network, when starting from the bump formation, is larger than the stability when starting even from the whole pattern. Finally, we show a very good agreement between the analytical results and the simulations performed for different topologies of the network.

Algorithms↗
PubMed2006

Effects of 5-HT on memory and the hippocampus: model and data.

5-Hydroxytryptamine (5-HT) transmission has been implicated in memory and in depression. Both 5-HT depletion and specific 5-HT agonists lower memory performance, while depression is also associated with memory deficits. The precise neuropharmacology and neural mechanisms underlying these effects are unknown. We used neural network simulations to elucidate the neuropharmacology and network mechanisms underlying 5-HT effects on memory. The model predicts that these effects are largely dependent on transmission over the 5-HT1A and 5-HT3 receptors, which regulate the selectivity of retrieval. It also predicts differential memory deficit profiles for 5-HT depletion and overactivation. The latter predictions were confirmed in studies with healthy and depressed participants undergoing acute tryptophan depletion or ipsipirone challenge. The results suggest that the memory impairments in depressed subjects may be related to 5-HT undertransmission, and support the notion that 5-HT1A agonists ameliorate memory deficits in depression.

Animals↗
PubMed1996

Neural networks simulating the frequency discrimination of hearing for non-stationary short tone stimuli.

This paper addresses the question of frequency discrimination of hearing for non-stationary (short) tone stimuli (duration < or = 125 ms). Shortening of the stimulus duration leads to widening of the frequency spectrum of the tone. It can be shown that for hearing no acoustical uncertainty relation holds and thus some nonlinear elements must be present in hearing physiology. We present neurophysiological and psychoacoustical findings supporting the hypothesis that frequency discrimination of non-stationary short tone stimuli is performed in neural networks of the auditory system. Neural network architectures that could process the temporal and place excitation patterns originating in the cochlea are suggested. We show how these networks (temporal coincidence network processing the temporal code and lateral inhibition network processing the place code) can be combined to show performance consistent with auditory physiology. They might explain the frequency discrimination of hearing for non-stationary short tone stimuli. We show the fitting of psychophysical relations based on these networks with the experimentally determined data.

Acoustic Stimulation↗
PubMedDate not supplied

Framewise phoneme classification with bidirectional LSTM and other neural network architectures.

In this paper, we present bidirectional Long Short Term Memory (LSTM) networks, and a modified, full gradient version of the LSTM learning algorithm. We evaluate Bidirectional LSTM (BLSTM) and several other network architectures on the benchmark task of framewise phoneme classification, using the TIMIT database. Our main findings are that bidirectional networks outperform unidirectional ones, and Long Short Term Memory (LSTM) is much faster and also more accurate than both standard Recurrent Neural Nets (RNNs) and time-windowed Multilayer Perceptrons (MLPs). Our results support the view that contextual information is crucial to speech processing, and suggest that BLSTM is an effective architecture with which to exploit it.

Algorithms↗
PubMed2004

Cascade control and defense in complex networks.

Complex networks with a heterogeneous distribution of loads may undergo a global cascade of overload failures when highly loaded nodes or edges are removed due to attacks or failures. Since a small attack or failure has the potential to trigger a global cascade, a fundamental question regards the possible strategies of defense to prevent the cascade from propagating through the entire network. Here we introduce and investigate a costless strategy of defense based on a selective further removal of nodes and edges, right after the initial attack or failure. This intentional removal of network elements is shown to drastically reduce the size of the cascade.

Biophysics↗
PubMed2002

Genetic programming of polynomial harmonic networks using the discrete Fourier transform.

This paper presents a genetic programming system that evolves polynomial harmonic networks. These are multilayer feed-forward neural networks with polynomial activation functions. The novel hybrids assume that harmonics with non-multiple frequencies may enter as inputs the activation polynomials. The harmonics with non-multiple, irregular frequencies are derived analytically using the discrete Fourier transform. The polynomial harmonic networks have tree-structured topology which makes them especially suitable for evolutionary structural search. Empirical results show that this hybrid genetic programming system outperforms an evolutionary system manipulating polynomials, the traditional Koza-style genetic programming, and the harmonic GMDH network algorithm on processing time series.

Artificial Intelligence↗
PubMed2002

Using noise to compute error surfaces in connectionist networks: a novel means of reducing catastrophic forgetting.

In error-driven distributed feedforward networks, new information typically interferes, sometimes severely, with previously learned information. We show how noise can be used to approximate the error surface of previously learned information. By combining this approximated error surface with the error surface associated with the new information to be learned, the network's retention of previously learned items can be improved and catastrophic interference significantly reduced. Further, we show that the noise-generated error surface is produced using only first-derivative information and without recourse to any explicit error information.

Animals↗
PubMed2001

Dual information representation with stable firing rates and chaotic spatiotemporal spike patterns in a neural network model.

Although various means of information representation in the cortex have been considered, the fundamental mechanism for such representation is not well understood. The relation between neural network dynamics and properties of information representation needs to be examined. We examined spatial pattern properties of mean firing rates and spatiotemporal spikes in an interconnected spiking neural network model. We found that whereas the spatiotemporal spike patterns are chaotic and unstable, the spatial patterns of mean firing rates (SPMFR) are steady and stable, reflecting the internal structure of synaptic weights. Interestingly, the chaotic instability contributes to fast stabilization of the SPMFR. Findings suggest that there are two types of network dynamics behind neuronal spiking: internally-driven dynamics and externally driven dynamics. When the internally driven dynamics dominate, spikes are relatively more chaotic and independent of external inputs; the SPMFR are steady and stable. When the externally driven dynamics dominate, the spiking patterns are relatively more dependent on the spatiotemporal structure of external inputs. These emergent properties of information representation imply that the brain may adopt a dual coding system. Recent experimental data suggest that internally driven and externally driven dynamics coexist and work together in the cortex.

Action Potentials↗
PubMed1995

A neural network model of phantom limbs.

This paper presents a detailed clinical description of phantom limbs and a neuronal network model that provides a comprehensive and parsimonious explanation of otherwise inexplicable or at least unrelated phenomena. Simulations of self-organizing feature maps (Kohonen networks) that had been trained to recognize input patterns were deprived of parts of their input in order to simulate partial deafferentation. This leads to reorganization processes that are shown to be driven by input noise. In patients with an amputated limb, this noise is generated by dorsal root ganglion sensory neurons which are known to fire irregularly upon laceration. According to this model, the long-standing debate concerning non-cortical vs. cortical contributions to the generation of the phenomenon of phantom limbs can be resolved in that it is the peripherally generated noise that causes cortical reorganization. The model can be tested and may have therapeutic implications.

Cerebral Cortex↗
PubMed2025

Detecting Interspecific Positive Selection Using Convolutional Neural Networks.

Traditional statistical methods using maximum likelihood and Bayesian inference can detect positive selection from an interspecific phylogeny and a codon sequence alignment based on model assumptions, but they are prone to false positives due to alignment errors and can lack power. These problems are particularly pronounced when faced with high levels of indels and divergence. To address these issues, we trained and tested convolutional neural network models on simulated data and achieved higher accuracy in detecting selection across a specific range of phylogenetic scenarios and evolutionary modes. This advantage is particularly evident when performing inference on noisy data prone to misalignments. Our method shows some ability to account for these errors, where most statistical frameworks fail to do so in a tractable manner. We explore the generalizability of our convolutional neural network models to unseen evolutionary scenarios and identify future avenues to achieve broader utility. Once trained, our convolutional neural network model is faster at test time, making it a scalable alternative to traditional statistical methods for large-scale, multigene analyses. In addition to binary classification (inference of the presence or absence of positive selection during the evolution of the sequences), we use saliency maps to understand what the model learns and observe how this could be leveraged for sitewise inference of positive selection.

Neural Networks, Computer↗
PubMed1995

Artificial neural networks for decision support in clinical medicine.

Connectionist models such as neural networks are alternatives to linear, parametric statistical methods. Neural networks are computer-based pattern recognition methods with loose similarities with the nervous system. Individual variables of the network, usually called 'neurones', can receive inhibitory and excitatory inputs from other neurones. The networks can define relationships among input data that are not apparent when using other approaches, and they can use these relationships to improve accuracy. Thus, neural nets have substantial power to recognize patterns even in complex datasets. Neural network methodology has outperformed classical statistical methods in cases where the input variables are interrelated. Because clinical measurements usually derive from multiple interrelated systems it is evident that neural networks might be more accurate than classical methods in multivariate analysis of clinical data. This paper reviews the use of neural networks in medical decision support. A short introduction to the basics of neural networks is given, and some practical issues in applying the networks are highlighted. The current use of neural networks in image analysis, signal processing and laboratory medicine is reviewed. It is concluded that neural networks have an important role in image analysis and in signal processing. However, further studies are needed to determine the value of neural networks in the analysis of laboratory data.

Clinical Medicine↗