Abstract
In this paper, a model of visual place cells (PCs) based on precise neurobiological data is presented. The robustness of the model in real indoor and outdoor environments is tested. Results show that the interplay between neurobiological modelling and robotic experiments can promote the understanding of the neural structures and the achievement of robust robot navigation algorithms. Short Term Memory (STM), soft competition and sparse coding are important for both landmark identification and computation of PC activities. The extension of the paradigm to outdoor environments has confirmed the robustness of the vision-based model and pointed to improvements in order to further foster its performance.
Highlights
Ethological studies of animal navigation show that a wide variety of sensory modalities can be used by the animals to navigate and to self localize
The place recognition could be performed in the entorhinal cortex (EC), the main source of input to the hippocampus and the dentate gyrus (DG), a substructure of the hippocampal system
We will show that going back and forth between robotics and neurobiological modelling can help both to obtain a more robust and faster place recognition for robotics applications and explain why short term memory (STM) and soft competition mechanisms are so important for the brain functioning
Summary
Ethological studies of animal navigation show that a wide variety of sensory modalities can be used by the animals to navigate and to self localize. In a first model, proposed in 1994, we showed how the learning of a few sensory-motor associations around a goal location was sufficient for a robot-like agent to exhibit a robust homing behavior [Gaussier and Zrehen, 1994] when the environment is simple (i.e. open field navigation with no need to plan a detour). The place recognition could be performed in the entorhinal cortex (EC), the main source of input to the hippocampus and the dentate gyrus (DG), a substructure of the hippocampal system. In this view, hippocampus proper (CA1/CA3) could be devoted to the learning of transitions between places and more generally context learning. We will show that going back and forth between robotics and neurobiological modelling can help both to obtain a more robust and faster place recognition for robotics applications and explain why short term memory (STM) and soft competition mechanisms are so important for the brain functioning
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