FANGWEN YU
Brain Inspired Navigation Blog
How to represent robot’s pose with a rate-coded neural network CAN in RatSLAM?
The excerpt note is about robot’s pose representation with a rate-coded neural network, continuous attractor network (CAN) in RatSLAM from Michael and Gordon 2010.
The CAN is a neural network that consists of an array of units with fixed weighted …
How to perform Path Integration in RatSLAM?
The excerpt note is about path integration in RatSLAM from Michael et al. 2010, 2008, 2004.
The RatSLAM system uses poses cells to concurrently represent the beliefs about the location and orientation of the robot. By representing 
in the same …
How does the velocity take effects on movement of activity of Pose Cells in RatSLAM?
The excerpt note is about how to move the activity of pose cells according to the translational and rotational velocity in RatSLAM from Michael et al. 2008, 2004.
The new path integration method shifts existing pose cell activity rather than …
How to update the activity of pose cells in RatSLAM?
The excerpt note is from Michael et al., 2008, which explains the detail process to update the activity of pose cells in RatSLAM.
Milford, Michael J., and Gordon F. Wyeth. “Mapping a suburb with a single camera using a …
How to build robust, visual-inertial state estimation for autonomous navigation?
Robust, Visual-Inertial State Estimation: from Frame-based to Event-based Cameras
This lecture was presented by Professor Davide Scaramuzza at Affiliation University of Zurich, ETH Zurich in September 25, 2017 on Series Microsoft Research Talks.
Professor Davide Scaramuzza presented main algorithms to …
Why drones can learn to navigate autonomously by imitating cars and bicycles?
Drones learn to navigate autonomously by imitating cars and bicycles powered by AI
January 23, 2018, by University of Zurich
Developed by UZH researchers, the algorithm DroNet allows drones to fly completely by themselves through the streets of a city …
How to enable robot cognitive mapping inspired by Grid Cells, Head Direction Cells and Speed Cells？
Taiping Zeng, and Bailu Si. “Cognitive Mapping Based on Conjunctive Representations of Space and Movement.” Frontiers in Neurorobotics 11 (2017).
In this work, the researchers developed a cognitive mapping model for mobile robots, taking advantage of the coding …
About
Brain Inspired Navigation Blog
New discovery worth spreading on brain-inspired navigation in neurorobotics and neuroscience
Recent Posts
- How synaptic plasticity underlies place-field formation and memory encoding during navigation?
- How human hippocampal CA3 uses specific functional connectivity rules for efficient associative memory?
- How are associative memories formed? Which cells represent a memory, and when are they engaged?
- How grid cell firing patterns rapidly adapt to novel or changing environmental features on a timescale relevant to behaviour?
- How the representations of space and time interact in the hippocampal memory system?
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