Category: Brain-Inspired Navigation
How to represent large areas by reusing cells in 2D continuous attractor network model?
The excerpt note is about how to represent large areas by reusing cells in 2D continuous attractor network model in RatSLAM from Michael et al., 2008, 2010, and Samsonovich et al., 1997, McNaughton et al., 2006.
Michael Milford, and Gordon …
How to represent location based on the place cells？
The excerpt note is about a model of spatial location based on the place cells from the Michael 2008.
Michael Milford. Robot Navigation from Nature: Simultaneous Localisation, Mapping, and Path Planning Based on Hippocampal Models. Springer-Verlag Berlin Heidelberg Press, …
What is the detail principle of Experience Map: Component, Transition, Creation, Maintenance in RatSLAM?
The excerpt note is about Experience Map: Component, Transition, Creation, Maintenance, from Michael 2008.
Michael Milford. Robot Navigation from Nature: Simultaneous Localisation, Mapping, and Path Planning Based on Hippocampal Models. Springer-Verlag Berlin Heidelberg Press, pp. 129-143, 149-150, 2008.
I. …
How to build cognitive map (experience map) for autonomous navigation in RatSLAM?
The excerpt note is about how to build Experience Map in RatSLAM from Michael et al 2008.
An experience map is a fine-grained topological map composed of many individual experiences, e, connected by transitions, t. Each experience 
is defined by …
Cognitive Navigation News (2018-003)
1. Can We Copy the Brain?
from the IEEE Spectrum Special Report about AI and Brain Inspired Computing.
From Macro to Micro: A Visual Guide to the Brain
In the Future, Machines Will Borrow …
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 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 …
How to perform robot place recognition with multi-scale, multi-sensor system inspired by place cells?
Adam Jacobson, Zetao Chen, Michael Milford. Leveraging variable sensor spatial acuity with a homogeneous, multi-scale place recognition framework. Biological Cybernetics, Jan 20, 2018, https://doi.org/10.1007/s00422-017-0745-7 .
This paper presented a biologically inspired multi-scale, multi-sensor place recognition system that incorporates the …
How Self-Motion Updates the Head Direction Cell Attractor?
Laurens J., et al., 2018 reviews head direction cells. They propose a quantitative framework whereby this drive represents a multisensory self-motion estimate computed through an internal model that uses sensory prediction errors of vestibular, visual, and somatosensory cues to improve …
How to implement internal dynamics of the head direction network in brain inspired 1D SLAM?
The excerpt note is from the Michael’s book. The example code is implemented by Fangwen, which can help us to understand the process and principle of the method.
Michael Milford. Robot Navigation from Nature: Simultaneous Localisation, Mapping, and Path Planning …
How to model the hippocampal place cell activity for spatial cognition and neuro-mimetic navigation?
This excerpt note is about place cell activity model from the Arleo A. et al. 2000 paper.
Arleo, Angelo, and Wulfram Gerstner. “Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity.” Biological cybernetics 83, no. …
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Brain Inspired Navigation Blog
New discovery worth spreading on brain-inspired navigation in neurorobotics and neuroscience
Recent Posts
- How hippocampal–neocortical communication reconfigure predetermined circuit motifs to flexibly encode experiences?
- How to build efficient robot navigation inspired by honeybee learning flights?
- How tuft dendrites in frontal motor cortex enable flexible learning?
- How human hippocampal ripples coordinate planning sequences and compositional representations in neocortex?
- How to implement planning in the Brain?
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