Abstract
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.
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References
Arel, I., Rose, D.C., Karnowski, T.P.: Deep Machine Learning–A New Frontier in Artificial Intelligence Research. IEEE Computational Intelligence Magazine 5(4), 13–18 (2010)
Meng, Y., Zhang, Y., Jin, Y.: Autonomous Self-Reconfiguration of Modular Robots by Evolving a Hierarchical Mechanochemical Model. IEEE Computational Intelligence Magazine 6(1), 43–54 (2011)
Dissanayake, M.W.M.G., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: A Solution to the Simultaneous Localization and Map Building (SLAM) Problem. IEEE Transactions on Robotics and Automation 17(3), 229–241 (2001)
Bar-Shalom, Y., Fortmann, T.E.: Tracking and Data Association. Academic Press, San Diego (1988)
Montemerlo, M., Thrun, S.: Simultaneous localization and mapping with unknown data association using FastSLAM. In: Proc. IEEE Int. Conference on Robotics and Automation (ICRA), pp. 1985–1991. IEEE Press, New York (2003)
Tang, H., Li, H., Yan, R.: Memory Dynamics in Attractor Networks with Saliency Weight. Neural Computation 22, 1899–1926 (2010)
Frank, L.M., Brown, E.N., Wilson, M.: Trajectory encoding in the hippocampus and entorhinal cortex. Neuron. 27, 169–178 (2000)
O’Keefe, J., Nadel, L.: The Hippocampus as a Cognitive Map. Oxford University Press, Oxford (1978)
Barrera, A., Weitzenfeld, A.: Biological Inspired Neural Controller for Robot Learning and Mapping. In: Proc. IEEE Int. Joint Conf. on Neural Networks, pp. 3664–3671. IEEE Press, New York (2006)
Wyeth, B.G., Milford, M.: Spatial Cognition for Robots. IEEE Robotics & Automation Magazine 16(3), 24–32 (2009)
Amaral, D.G., Ishizuka, N., Claiborne, B.: Neurons, numbers and the hippocampal network. Progress in Brain Research 83, 1–11 (1990)
Bernard, C., Wheal, H.V.: Model of Local Connectivity Patterns in CA3 and CA1 Areas of The Hippocampus. Hippocampus 4, 497–529 (1994)
Treves, A., Rolls, E.T.: Computational Analysis of The Role of The Hippocampus in Memory. Hippocampus 4, 374–391 (1994)
Ferbinteanu, J., Shapiro, M.L.: Prospective and Retrospective Memory Coding in The Hippocampus. Neuron 40(6), 1227–1239 (2003)
Fleischer, J.G., Krichmar, J.L.: Sensory Intergation and Remapping in a Model of the Medial Temporal Lobe during Maze Navigation by a Brain-based Device. Journal of Integrative Neuroscience 6(3), 403–431 (2007)
Ungerleider, L.G., Haxby, J.V.: “What” and “Where” in The Human Brain. Curr. Opin. Neurobiol. 4, 157–165 (1994)
Riesenhuber, M., Poggio, T.: Hierarchical Models of Object Recognition in Cortex. Nature Neuroscience 2(11), 1019–1025 (1999)
Deneve, S., Latham, P.E., Pouget, A.: Reading Population Codes: A Neural Implementation of Ideal Observers. Nature Neuroscience 8(2), 740–755 (1999)
Krichmar, J.L., Seth, A.K., Nitz, D.A., Fleischer, J.G., Edelman, G.M.: Spatial Navigation and Causal Analysis in a Brain-Based Device Modeling Cortical-Hippocampal Interactions. Neuroinformatics 3(3), 197–221 (2005)
Pfeifer, R., Scheier, C.: Sensory-motor coordination: The metaphor and beyond. Robot. Auton. Syst. 20, 157–178 (1997)
Floreano, D., Mondada, F.: Evolutionary neurocontrollers for autonomous mobile robots. Neural Netw. 11, 1461–1478 (1998)
Gaussier, P., Revel, A., Banquet, J.P., Babeau, V.: From view cells and place cells to cognitive map learning: processing stages of the hippocampal system. Biol. Cybern. 86, 15–28 (2002)
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Huang, W., Tang, H., Yu, J., Tan, C.H. (2011). A Neuro-cognitive Robot for Spatial Navigation. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_58
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DOI: https://doi.org/10.1007/978-3-642-24955-6_58
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