A New User-Adapted Search Haptic Algorithm to Navigate along Filiform Structures

One of the mayor research challenges of this century is the understanding of the human brain. Regarding this field line, simulation based research is gaining importance. A large amount of money is being spent in huge international projects such as The Human Brain Project [1] and The Blue Brain [2]. The behavior of the brain and, therefore, the behavior of brain simulations depend to a large extend on the neural topology. Neural elements are organized in a connected, dense, complex network of thread-like (i.e, filiform) structures. The analysis of a computer-based simulation using just the visual modality is a highly complex task due to the complexity of the neural topology and the large amounts of multi-variable and multi-modal data generated by computer simulations. In this paper, we propose the use of haptic devices to aid in the navigation along these neural structures, helping neurobiologists in the analysis of neural network topologies. However, haptic navigation constrained to complex filiform networks entails problems when these structures have high frequency features, noise and/or complex branching nodes. We address these issues by presenting a new user-adapted search haptic method that uses the forces exerted by the users to infer their intentions. In addition, we propose a specific calibration technique to adapt the haptic navigation to the user's skills and to the data. We validate this approach through a perceptual study. Finally, we show in this paper the application of our method to the analysis of dense and complex filiform structures in the neurobiology context. Additionally, our technique could be applied to other problems such as electronic circuits and graph exploration.