Above are papers related to the following paper: [Paper_ID] Paper #1110563. 	[Title] CONVIS: Action Oriented Control and Visualization of Neural Networks Introduction and Technical Description [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39904. 	[Title] Performance of the GCel-512 and PowerXPlorer for parallel neural network simulations [Abstract] This report presents new results from work performed in the framework of the IC 3 A pro-gramme. Using the GCel-512 and the PowerXPlorer made available by the UvA, a performance prediction model for several neural network simulations could be validated quantitatively both for a larger processor grid and for a different target parallel processor configuration. The performance prediction model and its application on a popular neural network model | backpropagation | decomposed via network decomposition will be discussed here. Using the model, the suitability of the GCel-512 and PowerXPlorer are discussed in terms of performance, speedup, efficiency and scalability. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1108841. 	[Title] Segmentation and Classification of Combined Optical and Radar Imagery [Abstract] The classification performance of a neural network for combined six-band Landsat-TM and one-band ERS-1/SAR PRI imagery from the same scene is carried out. Different combinations of the data | either raw, segmented or filtered |, using the available ground truth polygons, training and test sets are created. The training sets are used for learning while the test sets are used for verification of the neural network. The different combinations are evaluated here. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #179180. 	[Title] Rochester Connectionist Simulator [Abstract] Specifying, constructing and simulating structured connectionist networks requires significant programming effort. System tools can greatly reduce the effort required, and by providing a conceptual structure within which to work, make large and complex network simulations possible. The Rochester Connectionist Simulator is a system tool designed to aid specification, construction and simulation of connectionist networks. This report describes this tool in detail: the facilities provided and how to use them, as well as details of the implementation. Through this we hope not only to make designing and verifying connectionist networks easier, but also to encourage the development and refinement of connectionist research tools themselves. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #22835. 	[Title] A Scalable Performance Prediction Method for Parallel Neural Network Simulations [Abstract] A performance prediction method is presented for indicating the performance range of MIMD parallel processor systems for neural network simulations. The total execution time of a parallel application is modeled as the sum of its calculation and communication times. The method is scalable because based on the times measured on one processor and one communication link, the performance, speedup, and efficiency can be predicted for a larger processor system. It is validated quantitatively by applying it to two popular neural networks, backpropagation and the Kohonen self-organizing feature map, decomposed on a GCel-512, a 512 transputer system. Agreement of the model with the measurements is within 9%. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1365. 	[Title] Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex. [Abstract] Selective suppression of transmission at feedback synapses during learning is proposed as a mechanism for combining associative feedback with self-organization of feedforward synapses. Experimental data demonstrates cholinergic suppression of synaptic transmission in layer I (feedback synapses), and a lack of suppression in layer IV (feed-forward synapses). A network with this feature uses local rules to learn mappings which are not linearly separable. During learning, sensory stimuli and desired response are simultaneously presented as input. Feedforward connections form self-organized representations of input, while suppressed feedback connections learn the transpose of feedfor-ward connectivity. During recall, suppression is removed, sensory input activates the self-organized representation, and activity generates the learned response. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1126037. 	[Title] Parallel Environments for Implementing Neural Networks [Abstract] As artificial neural networks (ANNs) gain popularity in a variety of application domains, it is critical that these models run fast and generate results in real time. Although a number of implementations of neural networks are available on sequential machines, most of these implementations require an inordinate amount of time to train or run ANNs, especially when the ANN models are large. One approach for speeding up the implementation of ANNs is to implement them on parallel machines. This paper surveys the area of parallel environments for the implementations of ANNs, and prescribes desired characteristics to look for in such implementations. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #139865. 	[Title] PREENS, a Parallel Research Execution Environment for Neural Systems [Abstract] PREENS a Parallel Research Execution Environment for Neural Systems is a distributed neurosimulator, targeted on networks of workstations and transputer systems. As current applications of neural networks often contain large amounts of data and as the neural networks involved in tasks such as vision are very large, high requirements on memory and computational resources are imposed on the target execution platforms. PREENS can be executed in a distributed environment, i.e. tools and neural network simulation programs can be running on any machine connectable via TCP/IP. Using this approach, larger tasks and more data can be examined using an efficient coarse grained parallelism. Furthermore, the design of PREENS allows for neural networks to be running on any high performance MIMD machine such as a trans-puter system. In this paper, the different features and design concepts of PREENS are discussed. These can also be used for other applications, like image processing. Please summarise all the given information.

Paper #1110563 has the title `CONVIS: Action Oriented Control and Visualization of Neural Networks Introduction and Technical Description  
` and the abstract `nan`.
Question: Which category should Paper #1110563 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1110563 has the title `CONVIS: Action Oriented Control and Visualization of Neural Networks Introduction and Technical Description  
` and the abstract `nan`.
Question: Which category should Paper #1110563 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks