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, …

Be the First to comment. Read More

What is the future of grand challenges for robot navigation and exploration in extreme environments?

The excerpt note is about the grand challenges of robot navigation and exploration in extreme environments in the next 5 to 10 years according to the latest paper published in Science Robotics (Yang et al., Sci Robotics 2018).

Yang, G.Z., …

Be the First to comment. Read More

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. …

Be the First to comment. Read More

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 …

Be the First to comment. Read More

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

Why We Should Copy the Brain

In the Future, Machines Will Borrow …

Be the First to comment. Read More

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 …

Be the First to comment. Read More

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 …

Be the First to comment. Read More
About

CogNav Blog

New discovery worth spreading on cognitive navigation in neurorobotics and neuroscience

Links