Rats can orient themselves in an environment; they easily learn to navigate in an environment in order to approach a goal (food or escape platform). How is this possible? In the hippocampal formation of the rat, cells have been found that code for the current position of the rat (place cells) and for its orientation (head direction cells). These cells are thought to be a substrate for the representation of the environment and serve as a basis for spatial learning and navigation.
Autonomous agents must perform similar non-trivial navigation tasks in unpredictable environments, such as obstacle avoidance, homing, search for goals, in ways that remind the observer of animal behaviour. There are two opposite views of how such navigation tasks can be solved computationally. One approach splits the navigation task in two subtasks, representation and action. According to this approach, taking actions will be rather simple if a reliable model of the environment is available to the agent. The main problem consists in designing such an internal representation and making available to the agent in order to achieve a predesigned goal. The other approach makes no use of an explicit representation of the environment. Instead, a combination of simple rules and reflexes provides the agent with a set of intelligent behaviours. Here, the major challenge is that of devising an efficient methodology for automatic development of appropriate connections and priorities among several simple low-level processes.
Our approach is inspired by the hippocampus of the rat and can be situated somewhere in between these two extremes. The rat hippocampus is, to a certain degree a spatial representation. In contrast to representations in robotics, it is highly redundant, it has broad overlapping place fields, and and it has no topological organization.
Starting from a model of the rat hippocampus we ask the following questions:
- How to build such a spatial representations from visual and proprioceptive input while the agent interacts with its own environment
- How to use a coarse spatial representation for goal-oriented navigation
- How to construct learning rules for navigation that combine biological realism with abstract principles of reinforcement learning
To answer these and other questions we use neural modeling together with implementations on the Khepera Robot. Neural Network approches are applied in order to generate learning and adaptive behavior. Specifically, during the initial exlporation of the environment, the robot develops ‘place fields’, viz., units which are activated only if the robot is at a specific location in the environment. Given the place fields, simple learning rules like asymmetric Hebbian learning or Reinforcement learning can be applied to solve, e.g., homing tasks.
This project finished in 2008
Previous collaborators:
- Gediminas Luksys
- Denis Sheynikhovich
- Eleni Vasilaki
- Ricardo Chavarriaga
- Angelo Arleo, Paris
- Thomas Stroesslin
Some publications:
Sheynikhovich, Denis ; Chavarriaga, Ricardo ; Strösslin, Thomas ; Arleo, Angelo ; Gerstner, Wulfram (2005)
Is there a geometric module for spatial orientation? Insights from a rodent navigation model
Psychological Review, vol. 116, num. 3, 2009, p. 540-566
T. Strösslin, D. Sheynikhovich, R. Chavarriaga, and W. Gerstner (2005)
Robust self-localisation and navigation based on hippocampal place cells
NEURAL NETWORKS 18 (9): 1125-1140
T. Strösslin, R. Chavarriaga, D. Sheynikhovich, and W. Gerstner (2005)
Modelling Path Integrator Recalibration Using Hippocampal Place Cells
ICANN, Warsaw, Poland
Ricardo Chavarriaga, Thomas Strösslin, Denis Sheynikhovich and Wulfram Gerstner (2005)
A Computational Model of Parallel Navigation Systems in Rodents
Neuroinformatics, 3:223-241
Ricardo Chavarriaga, Thomas Strösslin, Denis Sheynikhovich and Wulfram Gerstner (2005)
Competition between cue response and place response: A model of rat navigation behaviour
Connection Science. 17:167-183
A. Arleo and W. Gerstner (2000)
Spatial Cognition and Neuro-Mimetic Navigation: A Model of Hippocampal Place Cell Activity
Biological Cybernetics, 83:287-299.
For additional references, consult the list of Publications of the lab