A Neural-Network Model of Cognitive Dysfunctions


A Neural-Network Model of Cognitive Dysfunctions – This paper investigates the possibility of integrating neural networks with the task of understanding cognitive processes. Although many tasks in neuroscience require the computation of multiple information streams, we develop a system that is able to automatically process information in many contexts. The goal of this work is to bridge the gap between cognitive processes and artificial neural networks by allowing them to be used on the same task using very different knowledge representations. In particular, a novel technique based on deep neural networks (DNNs) can be used to represent the spatial representation of the task-related signals. To our knowledge, this is the first time that a DNN representation on multiple dimensions of a task is used for the same task. The proposed approach is evaluated in multiple tasks and a DNN-based system is used. The results show the effectiveness of the proposed approach in terms of accuracy and speed.

We propose a new deep neural networks-based approach for classifying a target class using a sequence of training samples. Based on two variants of the CNN model, namely, convolution neural networks (CNN) and deep-network-based networks (DNNs), the CNN model is able to classify the samples based on their spatial ordering and temporal ordering. The CNN is a two-layer CNN, which takes its input data points as input and outputs the corresponding prediction results. DNN is a two-layer CNN which can be trained jointly with conventional CNNs. The CNN can predict the classification accuracy with the two-layer CNN, and both the CNN and the deep-network CNN have a representation of the target classes. A preliminary analysis conducted on the UCF101 dataset reported that the CNN model achieves an accuracy of 89.7% which is superior than the conventional CNN model with a baseline of 91.6% and a baseline of 97.8%.

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A Neural-Network Model of Cognitive Dysfunctions

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  • Pseudo Generative Adversarial Networks: Learning Conditional Gradient and Less-Predictive Parameter

    Multi-Dimensional Gaussian Process ClassificationWe propose a new deep neural networks-based approach for classifying a target class using a sequence of training samples. Based on two variants of the CNN model, namely, convolution neural networks (CNN) and deep-network-based networks (DNNs), the CNN model is able to classify the samples based on their spatial ordering and temporal ordering. The CNN is a two-layer CNN, which takes its input data points as input and outputs the corresponding prediction results. DNN is a two-layer CNN which can be trained jointly with conventional CNNs. The CNN can predict the classification accuracy with the two-layer CNN, and both the CNN and the deep-network CNN have a representation of the target classes. A preliminary analysis conducted on the UCF101 dataset reported that the CNN model achieves an accuracy of 89.7% which is superior than the conventional CNN model with a baseline of 91.6% and a baseline of 97.8%.


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