Memory Based Decision Making: A Spiking Neural Circuit Model

Abstract

Conscious decision making is one of the important functions of human behavior. Episodic memory is the source of knowledge for conscious decision making. The mechanism of how episodic memory affects conscious decision-making is unclear. To investigate the brain mechanism of conscious decision making, we investigated a biologically-based network model of spiking neurons for competition between automatic response and conscious decision making. The proposed model integrates episodic memory modular and brain decision-making modular, and uses episodic memory output as the top-down input of decision making. In the decision making, the network realizes the competition between decision patterns through mutual inhibition, finally reaches the conscious decision making. The simulations show that the proposed model can well implement multimodal coherent decision making under sequential memory control. The proposed model can effectively explain the transmission mechanism of conscious decision information.

Water Areas Segmentation from Remote Sensing Images Using a Separable Residual SegNet Network

“…The proposed water area segmentation system is illustrated in Figure 7. First, the Lake and River dataset was preprocessed to generate more data for neural network training through data augmentation; this technique increases the complexity of data and effectively reduces the overfitting of training [39]. Second, the dataset was divided into training set and testing set, and the images from the training set were put into the model for training.…”