Object-oriented modeling and simulation of neural networks

The article substantiates the necessity to develop training methods of computer simulation of neural networks in the spreadsheet environment. The systematic review of their application to simulating artificial neural networks is performed. The authors distinguish basic approaches to solving the problem of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools of neural network simulation, application of third-party add-ins to spreadsheets, development of macros using the embedded languages of spreadsheets; use of standard spreadsheet add-ins for non-linear optimization, creation of neural networks in the spreadsheet environment without add-ins and macros. After analyzing a collection of writings of 1890-1950, the research determines the role of the scientific journal “Bulletin of Mathematical Biophysics”, its founder Nicolas Rashevsky and the scientific community around the journal in creating and developing models and methods of computational neuroscience. There are identified psychophysical basics of creating neural networks, mathematical foundations of neural computing and methods of neuroengineering (image recognition, in particular). The role of Walter Pitts in combining the descriptive and quantitative theories of training is discussed. It is shown that to acquire neural simulation competences in the spreadsheet environment, one should master the models based on the historical and genetic approach. It is indicated that there are three groups of models, which are promising in terms of developing corresponding methods – the continuous two-factor model of Rashevsky, the discrete model of McCulloch and Pitts, and the discrete-continuous models of Householder and Landahl.

Within the past 10 years, computer simulations of neural networks have yielded new insights into the nature of information-processing neural systems. Since 1987, more and more network models relevant to psychiatry have been published, and such models have meanwhile been proposed for almost every psychopathological phenomenon. This paper provides an introduction to the theory of neural networks and gives examples of discoveries made via computer simulations. Network models of psychopathological phenomena are discussed with respect to formal thought disorder and delusions. The relevance of neural network models for psychopathological research and theory formulation is highlighted and it is argued that neural network modeling in psychopathology does not merely introduce a few mathematical gimmicks, but rather represents the beginning of a new basis for psychopathology.

Behavioral data show that echolocating bats can discriminate between different speeds of a moving object. A neural network system presented with echoes of simulated bat sounds recorded from targets moving at different speeds exhibits similar performance. In both cases, discriminations were successfully made between targets moving at 30 Hz and a slower variable speed (5, 10, and 20 Hz). However, in both cases, it is not clear what underlying concept was learned and used to perform the task. In other words, did the bat and the neural network perform the tasks based on a relative concept of faster (and thus learned to recognize `this is the faster speed') or did they perform the task based on an absolute concept of 30 Hz speed (and thus learned to recognize `this is the 30 Hz speed')? Both concepts would produce the observed performance of the bat and the neural networks. In this paper, we developed an approach for using neural networks to explore which concept was used to perform the task, and what factors may influence which type of concept is developed. First, we observed the behavior of the neural network when presented with a novel speed of 40 Hz. Its behavior on the novel 40 Hz speed suggested what underlying concept was implemented in the system (if it classified the novel 40 Hz as `30 Hz,' then it used a relative concept of faster; if it classified the novel 40 Hz speed as `not-30 Hz,' then it used an absolute concept, specifically quantifying the 30 Hz speed). Second, we examined what factors influenced the formation of the concept. Our computer simulations of neural networks that discriminated between 30 Hz and the slower variable speeds of 5, 10, and 20 Hz showed that the formation of a concept depends on the magnitude of the difference between the speeds used in training, i.e., the difficulty of the task. Specifically, when the difference between the speeds in the training set was large, the system formulated a relative concept of fast and slow. However, when the difference was small, the system formulated an absolute concept. Our neural network simulation of a system that learns to discriminate between a variety of speeds (i.e., 5, 10, and 20 Hz vs. 30 Hz speeds) demonstrated that the network developed the simplest concept that allowed it to perform the task correctly. Taken together, our results show that the development of concepts is influenced by the underlying computational demands of the task, and that neural networks can be used to explore the processes involved in concept formation.

We here describe SNNS, a neural network simulator for Unix workstations that we have been developing at the Universitat Stuttgart, Germany. Our network simulation environment is a tool to generate, train, test and visualize artificial neural networks. The simulator consists of three major components: a simulator kernel that operates on the internal representation ofthe neural networks, a graphical user interface based on X-Windows to interactively construct and change small neural nets,:ind a compiler to generate the internal representation of large neural networks from a high level network description language.Keywords: connectionism, neural networks, network simulators, network description language 1. INTRODUCTION AND MOTIVATION SNNS (Stuttgart Neural Network Simulator) is an efficient and portable neural network simulation environment for Unixworkstations that we have been developing at the Institute for Parallel and Distributed High Performance Systems, Universitht Stuttgart, Germany. It is an efficient software tool to generate, train, test and visualize artificial neural networks. The

To understand the mechanism of information processing by a biological neural network, computer simulation of a large-scale spiking neural network is an important method. However, because of a high computation cost of the simulation of a large-scale spiking neural network, the simulation requires high performance computing implemented by a supercomputer or a computer cluster. Recently, hardware for parallel computing such as a multi-core CPU and a graphics card with a graphics processing unit (GPU) is built in a gaming computer and a workstation. Thus, parallel computing using this hardware is becoming widespread, allowing us to obtain powerful computing power for simulation of a large-scale spiking neural network. However, it is not clear how much increased performance the parallel computing method using a new GPU yields in the simulation of a large-scale spiking neural network. In this study, we compared computation time between the computing methods with CPUs and GPUs in a simulation of neuronal models. We developed computer programs of neuronal simulations for the computing systems that consist of a gaming graphics card with new architecture (the NVIDIA GTX 1080) and an accelerator board using a GPU (the NVIDIA Tesla K20C). Our results show that the computing systems can perform a simulation of a large number of neurons faster than CPU-based systems. Furthermore, we investigated the accuracy of a simulation using single precision floating point. We show that the simulation results of single precision were accurate enough compared with those of double precision, but chaotic neuronal response calculated by a GPU using single precision is prominently different from that calculated by a CPU using double precision. Furthermore, the difference in chaotic dynamics appeared even if we used double precision of a GPU. In conclusion, the GPU-based computing system exhibits a higher computing performance than the CPU-based system, even if the GPU system includes data transfer from a graphics card to host memory.

Neural networks: An overview of early research, current frameworks and new challenges

Neural networks provide the opportunity to estimate specific components of engineering problems. They are decomposed complex problems into different parts. Thus, it can be easy to compete with each of them through neural networks. In this paper, it was purposed to estimate the average speed of a 6-line road’s cross-section by observed traffic variables, such as numbers of vehicles and occupancy values, using radial basis function neural network (RBFNN), generalized regression neural network (GRNN) and the feed-forward back propagation neural network (FFBPNN) models. A comparison was fulfilled between different neural networks and checked against multivariate linear regression (MVLR), a conventional statistical model. After each simulation of neural networks, results show that different forecasts were obtained under the same conditions. The best forecasting is made by FFBPNN, GRNN, and RBFNN, respectively. When compared with multivariate linear regression (MVLR), FFBPNN performs better than MVLR, but GRNN and RBFNN perform lower than it.

Theory-oriented software engineering

This chapter contains sections titled: Introduction Voltage-Based Models Changing Paradigm – From Biological Networks of Neurons to Artificial Neural Networks Numerical Simulation of Neural Networks Population-Based Simulation of Large Spiking Networks Synaptic Plasticity and Developing Neural Networks References

Analysis of Japanese logistics development mode based on neural network and computer simulation is conducted in this research. The Japanese government provides strong financial support for green logistics and implements various green environmental protection tax preferential policies. For then referring this advanced feature, the listed aspects are analyzed. (1) During the training and learning process, the state of the neuron nodes in each layer only affects the signals of the neurons in the lower layer, which are gradually transmitted through multiple layers. (2) The simulation with the clustering model is analyzed. The simulation results are combined with efficient information analysis and mining. The experimental results compared with the other have proven the suggestions are reasonable.

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THE PYGMALION NEURAL NETWORK PROGRAMMING ENVIRONMENT

In recent years, the use and investigations about Grey Literature (GL) increased, in particular, in Software Engineering (SE) research. However, its understanding is still scarce and sometimes controversial, such as interpreting GL types and assessing their credibility. This study aimed to understand the credibility aspects that SE researchers consider in assessing GL and its types. To achieve this goal, we surveyed 53 SE researchers (who answered that they have used GL in our previous investigation), receiving a total of 34 valid responses. Our main findings show that: 1) GL source produced or cited by a renowned source is the main credibility criteria used to assess GL, 2) most of the GL types tend to have a Low to Moderate level of Control and Expertise, 3) there is a positive statistical correlation between the level of Control and Expertise for most GL types, and 4) the different respondent profiles shared similar opinions about the credibility criteria. Our investigation contributes to helping future SE researchers that intend to use GL with more credibility. Additionally, shows the need for future studies to better understand the GL types in SE research.

The skill of experienced log analysts is emulated by a new computer technology called Simulated Neural Networks (SNNs). Two SNNs are applied to problems in log interpretation. The first SNN is applied to a trivial and conceptually familiar interpretation problem: learning to compute total porosity (PHIT) and apparent grain density (RHOMA) from log data and lithology inputs. The second SNN learned to identify lithofacies patterns as a response to input log data. Interesting and human-like results were obtained when these SNNs interpreted log data in a well which has been full bore cored from top to bottom. Both SNNs performed as well as experienced humans and at least as well or better than conventional sequential computer program interpretations of the same two analysis problems.

An artificial neural network based dynamic controller for a robot in a multi-agent system