RatSLAM: a hippocampal model for simultaneous localization and mapping

Milford, Michael; Wyeth, Gordon; Prasser, David

doi:10.1109/robot.2004.1307183

Cited by 320 publications

(265 citation statements)

References 4 publications

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“…Importantly, no underlying functions were used to bias neuronal unit activity toward spatial tuning. Moreover, unlike robotic systems (34)(35)(36)(37)(38), in which abstract features of the hippocampus were used to drive spatially modulated discharge, Darwin X implements many elements of the macro-and microanatomy characteristic of hippocampal-hippocampal and hippocampalcortical connections.…”

Section: Discussionmentioning

confidence: 99%

Characterizing functional hippocampal pathways in a brain-based device as it solves a spatial memory task

Krichmar

Nitz

Gally

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

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Analyzing neural dynamics underlying complex behavior is a major challenge in systems neurobiology. To meet this challenge through computational neuroscience, we have constructed a brain-based device (Darwin X) that interacts with a real environment, and whose behavior is guided by a simulated nervous system incorporating detailed aspects of the anatomy and physiology of the hippocampus and its surrounding regions. Darwin X integrates cues from its environment to solve a spatial memory task. Placespecific units, similar to place cells in the rodent, emerged by integrating visual and self-movement cues during exploration without prior assumptions in the model about environmental inputs. Because synthetic neural modeling using brain-based devices allows recording from all elements of the simulated nervous system during behavior, we were able to identify different functional hippocampal pathways. We did this by tracing back from reference neuronal units in the CA1 region of the simulated hippocampus to all of the synaptically connected units that were coactive during a particular exploratory behavior. Our analysis identified a number of different functional pathways within the simulated hippocampus that incorporate either the perforant path or the trisynaptic loop. Place fields, which were activated by the trisynaptic circuit, tended to be more selective and informative. However, place units that were activated by the perforant path were prevalent in the model and were crucial for generating appropriate exploratory behavior. Thus, in the model, different functional pathways influence place field activity and, hence, behavior during navigation.A nalyzing the complexities of neural dynamics underlying behavior is a difficult task for systems neurobiologists. A number of factors contribute to this complexity: the variability of behavior, the multilevel nature and nonlinearity of neural interactions, and the large number of neurons in different functioning brain regions. These factors challenge the design of experimental approaches as well as the construction of computational models. For this reason, we have constructed brainbased devices (BBDs) whose behavior in a real world environment is guided by a simulated nervous system based on features of vertebrate neuroanatomy and neurophysiology. The power of this approach is that it allows simultaneous recording of the state and interactions of all components of the simulated nervous system at all levels during a behavioral task in the real world. Here, we describe a functional analysis of neural patterns in Darwin X, a BBD incorporating aspects of the detailed anatomy and physiology of the hippocampus and its surrounding regions (1). Over the last 12 years, we have successfully constructed BBDs to test theories of the nervous system having to do with perceptual categorization, primary and secondary conditioning, visual binding, and texture discrimination (2-6).Darwin X can integrate cues from its environment and provide flexible navigation solutions to spatial memory task...

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Section: Discussionmentioning

confidence: 99%

Characterizing functional hippocampal pathways in a brain-based device as it solves a spatial memory task

Krichmar

Nitz

Gally

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

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“…However, general topological map concepts do not offer a solution for an efficient map thinning in case of memory limitations. Another interesting solution to the global navigation problem is RatSLAM [10], which builds a topological map with metric information, by separating the topological and the metric layer. This approach requires a proper scaling of the map in advance, because it cannot be changed efficiently at runtime.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Landmark-Tree map: A biologically inspired topological map for long-distance robot navigation

Augustine

Ortmeier

Mair

et al. 2012

2012 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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Abstract-Metric maps provide a reliable basis for mobile robot navigation. However, such maps are in general quite resource expensive and do not scale very well. Aiming for a highly scalable map, we adopt theories of insect navigation to develop an algorithm which builds a topological map for global navigation. Similar to insect conduct, positions in space are memorized as snapshots, which are unique configurations of landmarks. Unlike conventional snapshot approaches, we do not simply store the landmarks as a set, but we build a landmark tree which enables us to easily free memory in case of a continuously growing map while still preserving the dominant information. The resulting navigation is not sensor specific and solely relies on the directions of arbitrary landmarks. The generated map enables a mobile robot to navigate between defined locations and let it retrace a previously pursued path. Finally, we verify the reliability of the Landmark-Tree Map (LT-Map) concept and its robustness on memory limitations.

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“…1) is very important for long-term autonomy. Glover et al [9] presented a combination of FAB-MAP [6] and the biologically inspired RatSLAM [10] approach, and showed that it is robust to illumination and structural changes in outdoor environments. Milford et al [11] proposed to match sequences of images instead of a single image and showed good precision in recognizing places across different seasons (summer-rain).…”

Section: Introductionmentioning

confidence: 99%

Toward mutual information based place recognition

Pandey

McBride

Savarese

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-This paper reports on a novel mutual information (MI) based algorithm for robust place recognition. The proposed method provides a principled framework for fusing the complementary information obtained from 3D lidar and camera imagery for recognizing places within an a priori map of a dynamic environment. The visual appearance of the locations in the map can be significantly different due to changing weather, lighting conditions and dynamical objects present in the environment. Various 3D/2D features are extracted from the textured point clouds (scans) and each scan is represented as a collection of these features. For two scans acquired from the same location, the high value of MI between the features present in the scans indicates that the scans are captured from the same location. We use a non-parametric entropy estimator to estimate the true MI from the sparse marginal and joint histograms of the features extracted from the scans. Experimental results using seasonal datasets collected over several years are used to validate the robustness of the proposed algorithm.

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RatSLAM: a hippocampal model for simultaneous localization and mapping

Cited by 320 publications

References 4 publications

Characterizing functional hippocampal pathways in a brain-based device as it solves a spatial memory task

Characterizing functional hippocampal pathways in a brain-based device as it solves a spatial memory task

Landmark-Tree map: A biologically inspired topological map for long-distance robot navigation

Toward mutual information based place recognition

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