accepted by AAAI 2012 Robotics and Multimedia Fair
We combine both cognitive map and episodic memory functions together to enable advanced cognitive behaviors for robot. The proposed computational model focuses on the neocortical and hippocampus area that incorporates two major network components: EC and CA3 regions. The entorhinal-hippocampal system has been widely studied for its active involvement in the memory process , , . Some works on entorhinal-hippocampal system also focus on the spatial navigation , , , . Here, we focus on both properties and how they contribute to a high-level cognitive behavior. In the EC layer, a 3D CAN structure is used to model the cognitive map. The activities of the neurons in the CAN structure encode an estimation of the robot's location. Given that the size of CAN structure is limited; the size of the map space is limited by the number of cells in CAN. To address this problem, a torus shape connection method is used in CAN for the continuity of the neuron activities' shift in a large space environment . The most active neuron is then mapped to a scaled map with actual location information. The CA3 architecture and dynamics is inspired by the works of Jensen et al. , where the CA3 region is modeled as a recurrent network. The CA3 layer is composed of spiking neurons based on the spike response model (SRM). The synaptic modifications follow the Hebb-rule; simultaneous presynaptic and postsynaptic activity enhances synaptic efficacies. The direct perforant path input from superficial layer of EC to CA3 is quantitatively appropriate to provide cue for recall in CA3 , .
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