Tracking and data association
Evolutionary neurocontrollers for autonomous mobile robots
Neural Networks - Special issue on neural control and robotics: biology and technology
Memory dynamics in attractor networks with saliency weights
Neural Computation
Research frontier: deep machine learning--a new frontier in artificial intelligence research
IEEE Computational Intelligence Magazine
Autonomous Self-Reconfiguration of Modular Robots by Evolving a Hierarchical Mechanochemical Model
IEEE Computational Intelligence Magazine
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This paper presents a brain-inspired neural architecture with spatial cognition and navigation capability. It captures some navigation properties of rat brain in hidden goal hunting. The brain-inspired system consists of two main parts. One part is hippocampal circuitry and the other part is hierarchical vision architecture. The hippocampus is mainly responsible for the memory and spatial navigation in the brain. The vision system provides the key information about the environment. In the experiment, the cognitive model is implemented in a mobile robot which is placed in a spatial memory task. During the navigation, the neurons in CA1 area show a place dependent response. This place-dependent pattern of CA1 guides the motor neuronal area which then dictates the robot move to the goal location. The results of current study could contribute to the development of brain-inspired cognitive map which enables the mobile robot to perform a rodent-like behavior in the navigation task.