Home > Papers from 2017 > Neurocomputational models of navigation

Neurocomputational models of navigation

There are well established aspects of insect navigation, such as the use of vectors memories, which are well established, but we currently don’t have good models of how these behaviours might be implemented in the insect brain. Goldschmidt et al. address this with a model of Path Integration, where they extend the model to include the learning of vectors.

Abstract: “The model consists of a path integration mechanism, reward-modulated global learning, random search, and action selection. The path integration mechanism integrates compass and odometric cues to compute a vectorial representation of the agent’s current location as neural activity patterns in circular arrays. A reward-modulated learning rule enables the acquisition of vector memories by associating the local food reward with the path integration state. A motor output is computed based on the combination of vector memories and random exploration. “
Goldschmidt, D., Manoonpong, P., & Dasgupta, S. (2017). A neurocomputational model of goal-directed navigation in insect-inspired artificial agents. Frontiers in Neurorobotics11, 20.
Categories: Papers from 2017
  1. No comments yet.
  1. No trackbacks yet.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s