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At least 415 records · Page 23Linked to original sources
PubMed2003

Computer simulation of inhibition-dependent binding in a neural network.

Reverberating dynamics of neural network is modeled on PC in order to illustrate possible role of inhibition as binding controller in the network. The network is composed of binding neurons. In the binding neuron model [BioSystems 48 (1998) 263], the degree of temporal coherence between synaptic inputs is decisive for triggering, and slow inhibition is expressed in terms of the degree, which is necessary for triggering. Two learning mechanisms are implemented in the network, namely, adjusting synaptic strength and/or propagation delays. By means of forced playing of external pattern, the network is taught to support dynamics with disconnected and bound patterns of activity. By choosing either high, or low inhibition, one can switch between the disconnected and bound patterns, respectively. This is interpreted as inhibition-controlled binding in the network.

Computer Simulation↗
PubMed2001

Solving the binding problem of the brain with bi-directional functional connectivity.

We propose a neural network model which gives one solution to the binding problem on the basis of 'functional connectivity' and bidirectional connections. Here, 'functional connectivity' is dynamic neuronal connectivity peculiar to temporal spike coding neural networks with coincidence detector neurons. The model consists of a single primary map and two higher modules which extract two different features shown on the primary map. There exist three layers in each higher module and the layers are connected bi-directionally. An object in the outer world is represented by a 'global dynamical cell assembly' which is organized across the primary map and the two higher modules. Detailed, but spatially localized, information is coded in the primary map, whereas coarse, but spatially extracted information or globally integrated information is coded in the higher modules. Computer simulations of the proposed model show that multiple cell assemblies sharing the same neurons partially can co-exist. Furthermore, we introduce a three-dimensional J-PSTH (Joint-Peri Stimulus Time Histogram) which is capable of tracking such cell assemblies, altering its constituent neurons as in our proposed model.

Action Potentials↗
PubMed2002

The connections between the frustrated chaos and the intermittency chaos in small Hopfield networks.

In a previous paper we introduced the notion of frustrated chaos occurring in Hopfield networks [Neural Networks 11 (1998) 1017]. It is a dynamical regime which appears in a network when the global structure is such that local connectivity patterns responsible for stable oscillatory behaviors are intertwined, leading to mutually competing attractors and unpredictable itinerancy among brief appearance of these attractors. Frustration destabilizes the network and provokes an erratic 'wavering' among the orbits that characterize the same network when it is connected in a non-frustrated way. In this paper, through a detailed study of the bifurcation diagram given for some connection weights, we will show that this frustrated chaos belongs to the family of intermittency type of chaos, first described by Berge et al. [Order within chaos (1984)] and Pomeau and Manneville [Commun. Math. Phys. 74 (1980) 189]. Indeed, the transition to chaos is a critical one, and all along the bifurcation diagram, in any chaotic window, the duration of the intermittent cycles, between two chaotic bursts, grows as an invert ratio of the connection weight. Specific to this regime are the intermittent cycles easily identifiable as the non-frustrated regimes obtained by altering the values of these same connection weights. We will more specifically show that anywhere in the bifurcation diagram, a chaotic window always lies between two oscillatory regimes, and that the resulting chaos is a merging of, among others, the cycles at both ends. The strength (i.e. the duration of its oscillatory phase before the chaotic burst) of the first cycle decreases while the regime tends to stabilize into the second cycle (with the strength of this second cycle increasing) that will finally get the control. Since in our study, the bifurcation diagram concerns the same connection weights responsible for the learning mechanism of the Hopfield network, we will discuss the relations existing between bifurcation, learning and control of chaos. We will show that, in some cases, the addition of a slower Hebbian learning mechanism onto the Hopfield networks makes the resulting global dynamics to drive the network into a stable oscillatory regime, through a succession of intermittent and quasiperiodic regimes. Finally, we will present a series of possible logical steps to manually construct a frustrated network.

Computer Simulation↗
PubMed2001

Analogue VLSI for probabilistic networks and spike-time computation.

The history and some of the methods of analogue neural VLSI are described. The strengths of analogue techniques are described, along with residual problems to be solved. The nature of hardware-friendly and hardware-appropriate algorithms is reviewed and suggestions are offered as to where analogue neural VLSI's future lies.

Algorithms↗
PubMed1996

Computational study on the neural mechanism of sequential pattern memory.

The brain stores various kinds of temporal sequences as long-term memories, such as motor sequences, episodes, and melodies. The present study aims at clarifying the general principle underlying such memories. For this purpose, the memory mechanism of sequential patterns is examined from the viewpoint of computational theory and neural network modeling, and a neural network model of sequential pattern memory based on a simple and reasonable principle is presented. Specifically, spatio-temporal patterns varying gradually with time are stably stored in a network consisting of pairs of excitatory and inhibitory cells with recurrent connections; such a pair can achieve non-monotonic input-output characteristics which are essential for smooth sequential recall. Storage is performed using a simple learning algorithm which is based on the covariance rule and requires only that the sequence be input several times and retrieval is highly tolerant to noise. It is thought that a similar principle is used in cerebral memory systems, and the relevance of this model to the brain is discussed. Also, possible roles of hippocampus and basal ganglia in memorizing sequences are suggested.

Algorithms↗
PubMedDate not supplied

A molecular approach to retinal neural networks.

Our understanding of the computations carried out by neural networks in the central nervous system is limited by our incomplete knowledge of the diversity of cell types and the multiplicity of their functions. In the retina, over fifty cell types encode the spatial, temporal, and chromatic parameters of the incoming light stimuli to generate the messages of action potentials that travel to the brain along the fibers of the optic nerve. We have combined molecular techniques with microscopy and electrophysiology to study a rare cell type in the retina, the dopaminergic amacrine (interplexiform cells). These neurons were labeled in transgenic mice with human placental alkaline phosphatase so that they could be identified in vitro for patch-clamping and analysis of gene expression by single-cell RT-PCR and cDNA array profiling. We have thus demonstrated that the retinal dopaminergic neurons spontaneously generate action potentials in a rhythmic fashion, release dopamine over their entire surface, establish GABAergic synapses and contain proteins whose existence went undetected with more conventional techniques.

Amacrine Cells↗
PubMed2001

Neural representation of alpha-oriented moving light bars in the cortex: a neural network study.

A neural computational model is suggested in this paper for investigating the stimulus dependence of spiking patterns and the neural representation of alpha-oriented moving light bars in the cortex. In this model, a stimulus-directed cortical developing algorithm is introduced for training the neural network. Three classes of computer simulations concerned with the orientation of the stimulus are carried out. The simulation results show that the fine temporal structure of spiking patterns of single units depends on the alpha orientation of the two moving light bars, and the fine temporal structure of their combinatorial spiking patterns are also context dependent. They also show that the neural representation of an alpha-oriented moving light bar is determined not only by the stimulus itself but also the architecture of the matured network. In the end, we propose a possible neural coding mechanism underlying the temporal cell subassemblies in the cortex, which could be spontaneously and dynamically organized into a dynamical cell assembly by synchronized activity of these subassemblies.

Algorithms↗
PubMed1999

Dynamic stochastic synapses as computational units.

In most neural network models, synapses are treated as static weights that change only with the slow time scales of learning. It is well known, however, that synapses are highly dynamic and show use-dependent plasticity over a wide range of time scales. Moreover, synaptic transmission is an inherently stochastic process: a spike arriving at a presynaptic terminal triggers the release of a vesicle of neurotransmitter from a release site with a probability that can be much less than one. We consider a simple model for dynamic stochastic synapses that can easily be integrated into common models for networks of integrate-and-fire neurons (spiking neurons). The parameters of this model have direct interpretations in terms of synaptic physiology. We investigate the consequences of the model for computing with individual spikes and demonstrate through rigorous theoretical results that the computational power of the network is increased through the use of dynamic synapses.

Action Potentials↗
PubMed1998

Monitoring working memory load during computer-based tasks with EEG pattern recognition methods.

We assessed working memory load during computer use with neural network pattern recognition applied to EEG spectral features. Eight participants performed high-, moderate-, and low-load working memory tasks. Frontal theta EEG activity increased and alpha activity decreased with increasing load. These changes probably reflect task difficulty-related increases in mental effort and the proportion of cortical resources allocated to task performance. In network analyses, test data segments from high and low load levels were discriminated with better than 95% accuracy. More than 80% of test data segments associated with a moderate load could be discriminated from high- or low-load data segments. Statistically significant classification was also achieved when applying networks trained with data from one day to data from another day, when applying networks trained with data from one task to data from another task, and when applying networks trained with data from a group of participants to data from new participants. These results support the feasibility of using EEG-based methods for monitoring cognitive load during human-computer interaction.

Adult↗
PubMed1995

Neural network reconstruction of single-photon emission computed tomography images.

An artificial neural network (ANN) trained on high-quality medical tomograms or phantom images may be able to learn the planar data-to-tomographic image relationship with very high precision. As a result, a properly trained ANN can produce comparably accurate image reconstruction without the high computational cost inherent in some traditional reconstruction techniques. We have previously shown that a standard backpropagation neural network can be trained to reconstruct sections of single photon emission computed tomography (SPECT) images based on the planar image projections as inputs. In this study, we present a method of deriving activation functions for a backpropagation ANN that make it readily trainable for full SPECT image reconstruction. The activation functions used for this work are based on the estimated probability density functions (PDFs) of the ANN training set data. The statistically tailored ANN and the standard sigmoidal backpropagation ANN methods are compared both in terms of their trainability and generalization ability. The results presented show that a statistically tailored ANN can reconstruct novel tomographic images of a quality comparable with that of the images used to train the network. Ultimately, an adequately trained ANN should be able to properly compensate for physical photon transport effects, background noise, and artifacts while reconstructing the tomographic image.

Algorithms↗
PubMedDate not supplied

[Physical models of neural networks].

The state of art in computer modelling of neural networks with associative memory is reviewed. The available experimental data are considered on learning and memory of small neural systems, on isolated synapses and on molecular level. Computer simulations demonstrate that realistic models of neural ensembles exhibit properties which can be interpreted as image recognition, categorization, learning, prototype forming, etc. A bilayer model of associative neural network is proposed. One layer corresponds to the short-term memory, the other one to the long-term memory. Patterns are stored in terms of the synaptic strength matrix. We have studied the relaxational dynamics of neurons firing and suppression within the short-term memory layer under the influence of the long-term memory layer. The interaction among the layers has found to create a number of novel stable states which are not the learning patterns. These synthetic patterns may consist of elements belonging to different non-intersecting learning patterns. Within the framework of a hypothesis of selective and definite coding of images in brain one can interpret the observed effect as the "ideaś generating" process.

Animals↗
PubMed1996

Neural networks and fuzzy logic in clinical laboratory computing with application to integrated monitoring.

We present an analysis of the computational features of neural networks and fuzzy logic architectures which attempts to explain their recent popularity as well as their drawbacks. Based upon many reports in several fields, we identify the key computational requirements in the clinical laboratory setting, and review several classical tools. In particular we make the observation that all of these needs may be viewed as a search for an appropriate mathematical mapping. We suggest that the neural networks promise as a universal function approximant is the main source of its apparent attractivity. We then describe a customized neural network architecture as a non-linear, adaptive signal processor for integrated monitoring. This architecture is employed in the Adaptive Real-Time Anesthesiologist Associate (ARTAA) system, which has been developed as a joint project at the Department of Anesthesiology, Albert Einstein Medical Center and the Electrical and Computer Engineering Department, Drexel University in Philadelphia, USA. In this application the neural network realizes a non-linear scalar map from the set of physiological signals to a vital function status (VFS) indicator. The system is now under clinical testing.

Anesthesia↗
PubMed2001

A model with an intrinsic property of learning higher order correlations.

A neural network model that can learn higher order correlations within the input data without suffering from the combinatorial explosion problem is introduced. The number of parameters scales as M x N, where M is the number such that no higher order network with less than M higher order terms can implement the same input data set and N is the dimensionality of the input vectors. In order to have better generalization, the model was designed to realize a supervised learning such that after learning, output for any input vector is the same as the output of a higher order network that implements the same input data set using M number of higher order terms. Unlike the case in product units, the local minima problem does not pose itself as a severe problem in the model. Simulation results for some problems are presented and the results are compared with the results of a multilayer feedforward network. It is observed that the model can generalize better than the multilayer feedforward network.

Algorithms↗
PubMed2004

The role of networks and artificial intelligence in nanotechnology design and analysis.

Techniques with their origins in artificial intelligence have had a great impact on many areas of biomedicine. Expert-based systems have been used to develop computer-assisted decision aids. Neural networks have been used extensively in disease classification and more recently in many bioinformatics applications including genomics and drug design. Network theory in general has proved useful in modeling all aspects of biomedicine from healthcare organizational structure to biochemical pathways. These methods show promise in applications involving nanotechnology both in the design phase and in interpretation of system functioning.

Artificial Intelligence↗
PubMed2006

A thermodynamic view of networks.

Networks can be described by the frequency distribution of the number of links associated with each node (the degree of the node). Of particular interest are the power law distributions, which give rise to the so-called scale-free networks, and the distributions of the form of the simplified canonical law (SCL) introduced by Mandelbrot, which give what we shall call the Mandelbrot networks. Many dynamical methods have been obtained for the construction of scale-free networks, but no dynamical construction of Mandelbrot networks has been demonstrated. Here we develop a systematic technique to obtain networks with any given distribution of the degrees of the nodes. This is done using a thermodynamic approach in which we maximise the entropy associated with degree distribution of the nodes of the network subject to certain constraints. These constraints can be chosen systematically to produce the desired network architecture. For large networks we therefore replace a dynamical approach to the stationary state by a thermodynamical viewpoint. We use the method to generate scale-free and Mandelbrot networks with arbitrarily chosen parameters. We emphasise that this approach opens the possibility of insights into a thermodynamics of networks by suggesting thermodynamic relations between macroscopic variables for networks.

Computer Simulation↗
PubMed1992

Categorical versus coordinate spatial relations: computational analyses and computer simulations.

Results of 4 sets of neural network simulations support the distinction between categorical and coordinate spatial relations representations: (a) Networks that were split so that different hidden units contributed to each type of judgment performed better than unsplit networks; the reverse was observed when they made 2 coordinate judgments. (b) Both computations were more difficult when finer discriminations were required; this result mirrored findings with human Ss. (c) Networks with large, overlapping "receptive fields" performed the coordinate task better than did networks with small, less overlapping receptive fields, but vice versa for the categorical task; this suggests a possible basis for observed cerebral lateralization of the 2 kinds of processing. (d) The previously observed effect of stimulus contrast on this hemispheric asymmetry could reflect contributions of more neuronal input in high-contrast conditions.

Computer Simulation↗
PubMed2004

Local structural balance and functional interaction of excitatory and inhibitory synapses in hippocampal dendrites.

Theoretical and experimental studies on the computation of neural networks suggest that neural computation results from a dynamic interplay of excitatory and inhibitory (E/I) synaptic inputs. Precisely how E/I synapses are organized structurally and functionally to facilitate meaningful interaction remains elusive. Here we show that E/I synapses are regulated across dendritic trees to maintain a constant ratio of inputs in cultured rat hippocampal neurons. This structural arrangement is accompanied by an E/I functional balance maintained by a 'push-pull' feedback regulatory mechanism that is capable of adjusting E/I efficacies in a coordinated fashion. We also found that during activity, inhibitory synapses can determine the impact of adjacent excitatory synapses only if they are colocalized on the same dendritic branch and are activated simultaneously. These fundamental relationships among E/I synapses provide organizational principles relevant to deciphering the structural and functional basis for neural computation within dendritic branches.

Animals↗