A Spiking Neuron Model of Head-Direction Cells for Robot Orientation

International audienc

A Spiking Neuron Model of Head-Direction Cells for Robot Orientation

International audienc

Actor-critic models of reinforcement learning in the basal ganglia: from natural to artificial rats

Since 1995, numerous ActorCritic architectures for reinforcement learning have been proposed as models of dopaminelike reinforcement learning mechanisms in the rat’s basal ganglia. However, these models were usually tested in different tasks, and it is then difficult to compare their efficiency for an autonomous animat. We present here the comparison of four architectures in an animat as it performs the same rewardseeking task. This will illustrate the consequences of different hypotheses about the management of different Actor submodules and Critic units, and their more or less autonomously determined coordination. We show that the classical method of coordinations of modules by mixture of experts, depending on each module's performance, did not allow solving our task. Then we address the question of which principle should be applied to efficiently combine these units. Improvements for Critic modeling and accuracy of Actorcritic models for a natural task are finally discussed in the perspective of our Psikharpax project – an artificial rat having to survive autonomously in unpredictable environments

Auto-Organized Visual Perception Using Distributed Camera Network

International audienceCamera networks are complex vision systems difficult to control if the number of sensors is getting higher. With classic approaches, each camera has to be calibrated and synchronized individually. These tasks are often troublesome because of spatial constraints, and mostly due to the amount of information that need to be processed. Cameras generally observe overlapping areas, leading to redundant information that are then acquired, transmitted, stored and then processed. We propose in this paper a method to segment, cluster and codify images acquired by cameras of a network. The images are decomposed sequentially into layers where redundant information are discarded. Without need of any calibration operation, each sensor contributes to build a global representation of the entire network environment. The information sent by the network is then represented by a reduced and compact amount of data using a codification process. This framework allows structures to be retrieved and also the topology of the network. It can also provide the localization and trajectories of mobile objects. Experiments will present practical results in the case of a network containing 20 cameras observing a common scene

State of the artificial rat Psikharpax

This paper describes the current state of advancement of the Psikharpax project, which aims at producing an artificial rat equipped with control architectures and mechanisms that reproduce as nearly as possible those that have been widely studied in the natural rat. The article first describes the navigation system of Psikharpax, which is inspired from the anatomy and physiology of dedicated structures in the rat’s brain, like the hippocampus and the postsubiculum. Then, it defines the animat's action-selection system, which aims at replicating other structures, the basal ganglia. It also explains how navigation and action-selection capacities have been combined thanks to the interconnection of two different loops in the basal ganglia: a ventral loop that selects the direction of motion, and a dorsal loop that selects other behaviors, like feeding or drinking. Finally, preliminary results on the implementation of learning mechanisms in these structures are also presented. 1