Neural Networks In Human Brain Research Articles

Process structure-based fully connected neural network for the modelling of chemical processes: A comparison between global and modular configurations

BackgroundChemical process modeling is essential for the optimization, precise control and prediction of processes performance. As the modeling based on the first principles is time-consuming and labor-intensive, utilizing neural networks for the process modeling was proposed. However, the poor interpretability of the neural network models is may a problem in the modelling of chemical processes where the accuracy and reliability of the model are highly required. MethodInspired by the functional partitioning of neural networks in human brain, in this paper, global fully connected neural network (GFNN) and modular fully connected neural network (MFNN) models are constructed for the production process of ethylbenzene and that of isopropanol with cycle process, respectively. For the chemical processes with complex cycle processes, the quality of the product can be easily affected by small feed changes due to the highly nonlinear model which is called snowball effect. To avoid snowball effect, the cycle-avoidance MFNN is constructed, and it shows better modelling performance than GFNN. Significant findingsMFNN model has more advantages than GFNN model in the modelling of large-scale chemical processes without cycle processes. In addition, it is clear that MFNN model has a partial interpretability which means the local physical meaning for different parts of the whole process.

Rearrangement of the Activity of Neural Networks in the Human Brain on Reaching the Recognition Threshold for Fragmented Images

Common patterns of rearrangements of neural networks in the whole human brain on reaching the threshold of recognition of images in conditions of uncertainly were identifi ed. This result was obtained using a combination of digital synthesis of dynamic images and a brain activity mapping method using fMRI BOLD (blood oxygen level-dependent) signals. Reaching the threshold of recognition of images of objects was found to be associated with increases in the BOLD signal in some parts of the brain and decreases in the activity of neural networks in other areas. Brain areas whose activity was maximal at the time corresponding to the recognition threshold and task solution were described, and this activity was shown to be lesser in subthreshold and even suprathreshold conditions for observation of dynamic images. Areas in which this activity was minimal were identified. The data were interpreted in the framework of multichannel and multilevel model of systems taking decisions relating to visual signals.

Automatic Plant Recognition : A Review

Plant recognition and classification is required for the protection of biodiversity and for utilising them for human purposes. Not all people are having enough knowledge to recognize a plant while looking on to it. The developments in computer vision enhanced the use of computerized frameworks for recognition and classification of objects in surroundings. Plants can be classified based on their leaf pattern and other noval properties. Image recognition is closely related with neural networks which are developed to imitate that biological neural networks in human brain. Artificial neural networks are used to train with a large data set to recognize a new input data. The area of Artificial Intelligence is undergoing a drastic development and its use in computer vision made a huge impact. In this paper the history of plant recognition and features used for classification are analysed. Also various strategies for plant recognition using neural networks such as K-Nearest Neighbour(KNN), Support Vector Machine(SVM), Convolutional Neural Networks(CNN) are discussed.

Location Decision of Logistics Distribution Centers Based on Artificial Neural Network

With the never-ending changes and improvements of science and technology and the rapid development of economy, modern logistics plays an increasingly important role in social and economic development. As an important link between enterprises and consumers, logistics distribution is critical in the smooth development of logistics activities, and the reasonable location of logistics distribution centers is directly related to the distribution efficiency, logistics cost and consumer satisfaction of enterprises. Therefore, this paper studies the location decision of logistics distribution centers, and with the aid of artificial neural network, which can simulate the neural network of human brain and reproduce the process of brain thinking decision, establishes the BP neural network evaluation model, and evaluates the merits and demerits of several location plans with examples. The results show that the method based on artificial neural network is effective and practical, and provides a new idea for the location decision of logistics distribution centers.

Prediction of Excavation and Settlement of Shallow-buried Tunnel Based on Radial Basis Function Neural Network of Human Brain

Artificial neural network technology is to simulate the neural network structure of the human brain so as to solve the nonlinear engineering problem information processing system by establishing artificial neurons and sensors. The ground surface settlement caused by the excavation of shallow-buried tunnels is a research hotspot in the field of tunnels and underground engineering. By analyzing the influence factors that cause ground surface settlement, this study selects seven factors such as the cohesion and internal friction angle of surrounding rock as input and takes the measured value as the output to establish a ground surface settlement prediction model based on radial basis function neural network (RBFNN). A genetic algorithm is introduced to eliminate the slow convergence speed and local optimum of RBFNN. RBFNN prediction model can predict the ground surface settlement caused by the excavation of shallow-buried tunnels. The relative error of prediction is controlled within ±9%, and the prediction results meet the actual needs of the project. This study can provide reference for the expansion and application of RBFNN of human brain cortex in the engineering field.

Application of connected iterative scan in biological neural network

The current study applied connected iterative scan (CIS), a graph-theoretical-based approach that was previously developed to detect clusters among social networks, in detecting the functional clusters among the biological neural network of human brain extracted from resting-state functional MRI data. Unlike traditional clustering or modularity methods, CIS allows for overlapping among the detected clusters. CIS was tested on a simulation dataset as well as a biological dataset. CIS was able to detect the overlaps designed in the simulation dataset; CIS was also able to detect the designed overlap in the simulation dataset, and it also detected the overlap between the two networks in the biological dataset, which provided new knowledge about the architecture of the biological neural network. In conclusion, CIS is shown to be useful for revealing the delicate structures among the functional clusters in the biological neural network.

Large-scale directional connections among multi resting-state neural networks in human brain: A functional MRI and Bayesian network modeling study

This study examined the large-scale connectivity among multiple resting-state networks (RSNs) in the human brain. Independent component analysis was first applied to the resting-state functional MRI (fMRI) data acquired from 12 healthy young subjects for the separation of RSNs. Four sensory (lateral and medial visual, auditory, and sensory-motor) RSNs and four cognitive (default-mode, self-referential, dorsal and ventral attention) RSNs were identified. Gaussian Bayesian network (BN) learning approach was then used for the examination of the conditional dependencies among these RSNs and the construction of the network-to-network directional connectivity patterns. The BN based results demonstrated that sensory networks and cognitive networks were hierarchically organized. Specially, we found the sensory networks were highly intra-dependent and the cognitive networks were strongly intra-influenced. In addition, the results depicted dominant bottom-up connectivity from sensory networks to cognitive networks in which the self-referential and the default-mode networks might play respectively important roles in the process of resting-state information transfer and integration. The present study characterized the global connectivity relations among RSNs and delineated more characteristics of spontaneous activity dynamics.