Tracking moving objects is a vital visual task for the survival of an animal. We describe oscillatory neural network models of visual attention with a central element that can track a moving target among a set of distracters on the screen. At the initial stage, the model forms the focus of attention on an arbitrary object that is considered as a target. Other objects are treated as distracters. We present here two models: 1) synchronisation based model designed as a network of phase oscillators and 2) spiking neural model which is based on the idea of resource-limited parallel visual pointers. Selective attention and the tracking process are represented by the partial synchronisation between the central unit and a subgroup of peripheral elements. Simulation results are in overall agreem...
..."Nehmen wir eine U-Boot-Simulation, in der bis hin zur letzten Schraube alles korrekt...
The main aim of this paper is to forecast gold and silver daily returns with advanced regression analysis using various linear and non-linear models. ARMA models are used as a linear benchmark for comparison purposes with established non-linear models such as Nearest Neighbours and MultiLayer Perceptron (MLP), and Higher Order Neural Networks (HONN) whose application to financial markets is quite new. All models are assessed using statistical criteria such as correct directional change as well as financial criteria such as risk adjusted return. The main aim is to find which of these models generate the best returns and if nonlinear models can be used for generating excess returns in the precious metals market. This is achieved by implementing a trading simulation where the forecast is t...
.... Repenning, N. P. (2002). A simulation-based approach to understanding the dynamics of in...
The free-energy-based reinforcement learning is a new approach to handling high-dimensional states and actions. We investigate its properties using a new experimental platform called the digit floor task. In this task, the high-dimensional pixel data of hand-written digits were directly used as sensory inputs to the reinforcement learning agent. The simulation results showed the robustness of the free-energy-based reinforcement learning method against noise applied in both the training and testing phases. In addition, reward-dependent sensory representations were found in the distributed activation patterns of hidden units. The representations coded in a distributed fashion persisted even when the number of hidden nodes were varied.
...Based on extensive simulation studies, these authors have proposed revised criti...
In this paper, threshold voltage modeling based on neural networks is presented. The database was obtained by performing DC analysis with possible combinations of MOSFETs terminal voltages and channel widths which directly effect threshold voltage values in submicron technology. The neural network was trained with the database including 0.25 µm and 0.40 µm TSMC process parameters. In order to prove the extrapolation ability, the test dataset is constituted with 0.18 µm TSMC process parameters, which were not applied to the neural network for training. The test results of neural network tool are compared with the data obtained by using the Cadence simulation tool. The excellent agreement between the experimental and the model results makes neural networks a powerful tool for estimation o...
This paper presents the results of experiments in applying a spiking neural network to control the locomotion of a simulated biped robot. The neural model used in simulations was developed to allow for an analytic solution to a neuron fire time, while maintaining a non-instant post-synaptic potential rise time. The synaptic weights and delays were tuned using an evolution strategy. Simulation experiments demonstrate that within about seven thousand generations the biped is able to acquire a dynamic walk which allows it to walk upright for several metres.
On the basis of analyzing the principles of the quantum, rotation gates and quantum controlled-NOT gates, an improved design for CNOT gated quantum neural networks model is proposed and a smart algorithm for it is derived in our paper, based on the gradient descent algorithm. In the improved model, the input information is expressed by the qubits, which, as the control qubits after being rotated by the rotation gate, control the qubits in the hidden layer to reverse. The qubits in the hidden layer, as the control qubits after being rotated by the rotation gate, control the qubits in the output layer to reverse. The networks output is described by the probability amplitude of state |1[right angle bracket] in the output layer. It has been shown in two application examples of pattern recog...
...Step 5. Save ... 5. Simulations. To examine the effectiveness of the proposed QGNN...
This article deals with a neural network based on Min/Max nodes and its utilisation for image recognition purposes. The general concepts of the Min/Max nodes and the single-layer neural networks are outlined. The developed software systems for simulation are briefly introduced and the results of simulations with the various settings of a neural net are presented. The subject of simulations was the recognition of human faces. Finally, the hardware design of the neural network in VHDL is shown. The design demonstrates the ease of systems realisation and the achieving of high performance.
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