Extrasynaptic-GABA-mediated neuromodulation in a sensory cortical neural network

Simulating a neural network model of an early sensory cortical area, we investigated how gamma-aminobutyric acid (GABA) accumulated in extracellular space (ambient GABA), which depends on the synaptic activity of GABAergic interneurons, acts on the GABAa-receptors located on extrasynaptic membrane regions of principal cells (P), feedback inhibitory cells (F) and lateral inhibitory cells (L). The ambient GABA enhanced the selective responsiveness of P-cells to a target feature stimulus, if it acted on the extrasynaptic GABAa-receptors of P-cells. The ambient GABA led to depolarizing P-cells during ongoing (spontaneous) neuronal-activity periods, if it acted on the extrasynaptic GABAa-receptors of F or L cells. This membrane depolarization contributed to establishing an ongoing subthreshold neuronal state, by which the P-cells could respond quickly to the target stimulus. We suggest that the combinatorial inhibition of P, F, and L cells, meditated by extrasynaptic GABAa-receptors recognizing ambient GABA, is crucial for processing the information of relevant sensory features and for establishing an ongoing subthreshold cortical state that prepares as a ready state for subsequent sensory input. A failure in neuronal-activity-dependent regulation of ambient GABA, stemming largely from the depletion of GABA in extracellular space during senescence, may cause the degeneration of intracortical inhibition that leads to cognitive dysfunction in old animals.     

A novel blood-cell-two-compartment model for transferring a whole blood time activity curve to plasma in rodents

The term input function usually refers to the tracer plasma time activity curve (pTAC), which is necessary for quantitative positron emission tomography (PET) studies. The purpose of this study was to acquire the pTAC by independent component analysis (ICA) estimation from the whole blood time activity curve (wTAC) using a novel method, namely the FDG blood-cell-two-compartment model (BCM). This approach was compared to a number of published models, including linear haematocrit (HCT) correction, non-linear HCT correction and two-exponential correction. The results of this study show that the normalized root mean square error (NRMSE) and the error of the area under curve (EAUC) for the BCM estimate of the pTAC were the smallest. Compartmental and graphic analyses were used to estimate the metabolic rate of the FDG (MR(FDG)). The percentage error for the MR(FDG) (PE(MRFDG)) was estimated from the BCM corrected pTAC and this was also the smallest. It is concluded that the BCM is a better choice when transferring wTAC into pTAC for quantification.     

Rapid processing and unsupervised learning in a model of the cortical macrocolumn

We study a model of the cortical macrocolumn consisting of a collection of inhibitorily coupled minicolumns. The proposed system overcomes several severe deficits of systems based on single neurons as cerebral functional units, notably limited robustness to damage and unrealistically large computation time. Motivated by neuroanatomical and neurophysiological findings, the utilized dynamics is based on a simple model of a spiking neuron with refractory period, fixed random excitatory interconnection within minicolumns, and instantaneous inhibition within one macrocolumn. A stability analysis of the system's dynamical equations shows that minicolumns can act as monolithic functional units for purposes of critical, fast decisions and learning. Oscillating inhibition (in the gamma frequency range) leads to a phase-coupled population rate code and high sensitivity to small imbalances in minicolumn inputs. Minicolumns are shown to be able to organize their collective inputs without supervision by Hebbian plasticity into selective receptive field shapes, thereby becoming classifiers for input patterns. Using the bars test, we critically compare our system's performance with that of others and demonstrate its ability for distributed neural coding.     

A time-efficient convolutional neural network model in human activity recognition

Activity recognition represents the task of classifying data derived from different sensor types into one of predefined activity classes. The most popular and beneficial sensors in the area of action recognition are inertial sensors such as accelerometer and gyroscope. Convolutional neural network (CNN) as one of the best deep learning methods has recently attracted much attention to the problem of activity recognition, where 1D kernels capture local dependency over time in a series of observations measured at inertial sensors (3-axis accelerometers and gyroscopes) while in 2D kernels apart from time dependency, dependency between signals from different axes of same sensor and also over different sensors will be considered. Most convolutional neural networks used for recognition task are built using convolution and pooling layers followed by a few number of fully connected layers but large and deep neural networks have high computational costs. In this paper, we propose a new architecture that consists solely of convolutional layers and find that with removing the pooling layers and instead adding strides to convolution layers, the computational time will decrease notably while the model performance will not change or in some cases will even improve. Also both 1D and 2D convolutional neural networks with and without pooling layer will be investigated and their performance will be compared with each other and also with some other hand-crafted feature based methods. The third point that will be discussed in this paper is the impact of applying fast fourier transform (FFT) to inputs before training learning algorithm. It will be shown that this preprocessing will enhance the model performance. Experiments on benchmark datasets demonstrate the high performance of proposed 2D CNN model with no pooling layers.

     

Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity

Analyzing the dependencies between spike trains is an important step in understanding how neurons work in concert to represent biological signals. Usually this is done for pairs of neurons at a time using correlation-based techniques. Chornoboy, Schramm, and Karr (1988) proposed maximum likelihood methods for the simultaneous analysis of multiple pair-wise interactions among an ensemble of neurons. One of these methods is an iterative, continuous-time estimation algorithm for a network likelihood model formulated in terms of multiplicative conditional intensity functions. We devised a discrete-time version of this algorithm that includes a new, efficient computational strategy, a principled method to compute starting values, and a principled stopping criterion. In an analysis of simulated neural spike trains from ensembles of interacting neurons, the algorithm recovered the correct connectivity matrices and interaction parameters. In the analysis of spike trains from an ensemble of rat hippocampal place cells, the algorithm identified a connectivity matrix and interaction parameters consistent with the pattern of conjoined firing predicted by the overlap of the neurons' spatial receptive fields. These results suggest that the network likelihood model can be an efficient tool for the analysis of ensemble spiking activity.     

A rational model of the surface swept by a curve*

This paper shows how to construct a rational Bezier model of a swept surface that interpolates N frames (i.e., N position/orientation pairs) of a fixed rational space curve c(s) and maintains the shape of the curve at all intermediate points of the sweep. Thus, the surface models an exact sweep of the curve, consistent with the given data. The primary novelty of the method is that this exact modeling of the sweep is achieved without sacrificing a rational representation for the surface. Through a simple extension, we also allow the sweeping curve to change its size through the sweep. The position, orientation, and size of the sweeping curve can change with arbitrary continuity (we use C² continuity in this paper). Our interpolation between frames has the classical properties of Bezier interpolation, such as the convex hull property and linear precision. This swept surface is a useful primitive for geometric design. It encompasses the surface of revolution and extruded surface, but extends them to arbitrary sweeps. It is a useful modeling primitive for robotics and CAD/CAM, using frames generated automatically by a moving robot or tool.     

Eye synthesis using the eye curve model

Eyes are a critical part in exhibiting facial expressions. Because of the appearance diversity of eyes due to motion, it is difficult to synthesize eye with a particular facial expression. Traditional methods have failed to adequately catch motion-related appearance changes. In order to generate a photorealistic expression eye, we propose a two-step method. First, we propose a curve-based model to represent eyes. The model uses one circle and four skewed elliptical arcs to represent the shape of eyes, and divides the entire eye region into six sub-regions that correspond to different anatomical components of eyes. Then we propose a structure-based-similarity (SBS) framework to synthesize expression eyes using the eye curve model. The main contributions of this paper are: first of all, the proposed eye curve model can represent the diversity of eyes due to motion and structure, which is better than some traditional models. Second, the SBS framework is flexible. In multiple samples and multiple targets situation, the framework can synthesize eyes which exhibit same expression with different personal style. In single sample and single target situation, the framework can clone this expression successful. Experimental results show that synthesized eyes are realistic and expressive.     

赋工序完工隶属函数的网络计划模型

文中为处理网络计划模型中工序作业时间的不确定性,提出了用工序完工隶属函数描述作业时间不确定性,并给出了求工程的完工隶属函数的方法,最后说明了利用工程的完工隶属函数进行关键路线分析。     

Neural Network Methods for NURBS Curve and Surface Interpolation

New algorithms based on artificial neural network models are presented for cubic NURBS curve and surface interpolation.When all th knot spans are identical,the NURBS curve interpolation procedure degenerates into that of uniform rational B-spline curves.If all the weights of data points are identical,then the NURBS curve interpolation procedure degenerates into the integral B-spline curve interpolation.     

脑皮层神经网络的线性时滞反馈控制

神经元的病态同步放电会破坏大脑的正常功能, 导致癫痫和帕金森等生理疾病. 本文采用神经元二维映射模型构建一个脑皮层神经网络, 当神经元之间的耦合强度超过某一阈值时, 网络中所有神经元同步放电. 通过施加线性时滞反馈控制, 可以有效的消除这种同步状态, 且不改变神经元本身的放电特性. 仿真结果表明线性时滞反馈 可以实现对脑皮层神经网络的去同步化控制, 且对刺激参数的变化具有鲁棒性.     

On the performance of a hybrid network traffic model

We study a hybrid network traffic model that combines a fluid-based analytical model using ordinary differential equations with the packet-oriented discrete-event simulation. The hybrid model is important to large-scale real-time network simulations, where the packet-level emulation traffic is handled by discrete events and the majority of the background traffic is described more efficiently as fluids. We present a simple performance analysis of this hybrid approach. We propose three techniques—namely, pointer caching, update dampening, and dynamic time stepping—in an implementation of the hybrid model. Experiments show that these techniques can significantly improve the performance of the fluid-based network simulation.     

Texture synthesis by a neural network model

In this paper we propose a neural network model to synthesise texture images. The model is based on a continuous Hopfield-like network where each pixel of the image is occupied by a neuron that is eight-connected to its neighbours. A state of the neuron denotes a certain grey level of the corresponding pixel. The firing of the neuron changes its state, and hence the grey level of the corresponding pixel. Different two-tone and grey-tone texture images can be synthesised by manipulating the connection weights and by varying the algorithm iteration number. For grey-tone texture synthesis, a Markov chain principle has been employed to decide on the multiple state transition of a neuron. The model can be employed for texture propagation with the advantage that it allows propagation without showing any blocky effect.     

VLSI implementation of a neural network model

The authors describe a complementary metal-oxide-semiconductor (CMOS) very-large-scale integrated (VLSI) circuit implementing a connectionist neural-network model. It consists of an array of 54 simple processors fully interconnected with a programmable connection matrix. This experimental design tests the behavior of a large network of processors integrated on a chip. The circuit can be operated in several different configurations by programming the interconnections between the processors. Tests made with the circuit working as an associative memory and as a pattern classifier were so encouraging that the chip has been interfaced to a minicomputer and is being used as a coprocessor in pattern-recognition experiments. This mode of operation is making it possible to test the chip's behavior in a real application and study how pattern-recognition algorithms can be mapped in such a network     

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.     

A neural network model of a communication network with information servers

This paper models information flow in a communication network. The network consists of nodes that communicate with each other, and information servers that have a predominantly one-way communication to their customers. A neural network is used as a model for the communication network. The existence of multiple equilibria in the communication network is established. The network operator observes only one equilibrium, but if he knows the other equilibria, he can influence the free parameters, for example by providing extra bandwidth, so that the network settles in another equilibrium that is more profitable for the operator. The influence of several network parameters on the dynamics is studied both by simulation and by theoretical methods.The author was with the Intelligent Systems Unit, BT Laboratories, Martlesham Heath, Ipswich IP5 7RE, UK.     

Adaptive driver model using a neural network

An adaptive driver model for longitudinal movements of a vehicle has been developed. It incorporates a conventional feedback brake controller, and both fixed and adaptive neural network controllers to produce the throttle demand. It has been interfaced with a vehicle model in a Simulink environment, and simulation studies indicate a high level of performance. Implementation of the adaptive driver model within a real-time environment has also been realized successfully. This work was presented in part at the 7th International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

Estimation and inference in the yield curve model with an instantaneous error term

Many variations exist of yield curve modeling based on the exponential components framework, but most do not consider the generating process of the error term. In this paper, we propose a method of yield curve estimation using an instantaneous error term generated with a standard Brownian motion. First, we add an instantaneous error term to Nelson and Siegel’s instantaneous forward rate model [C.R. Nelson, A.F. Siegel, Parsimonious modeling of yield curves, Journal of Business 60 (1987) 473–489]. Second, after differencing multiperiod spot rate models transformed using Nelson and Siegel’s instantaneous forward rate model [C.R. Nelson, A.F. Siegel, Parsimonious modeling of yield curves, Journal of Business 60 (1987) 473–489], we obtain a model with serially uncorrelated error terms because of independent increment properties of Brownian motion. As the error term in this model is heteroskedastic and not serially correlated, we can apply weighted least squares estimation techniques. That is, this specification of the error term does not lead to incorrect estimation methods. In an empirical analysis, we compare the instantaneous forward rate curves estimated by the proposed method and an existing method. We find that the shape from the proposed estimation equation differ from the latter method when fluctuations in the interest rate data used for the estimation are volatile.     

A recurrent neural network architecture to model physical activity energy expenditure in older people

Data Mining and Knowledge Discovery - Through the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing,...