Paul Verschure is Catalan Institute of Advanced Studies (ICREA) Research Professor, Director of the Center for Autonomous Systems and Neurorobotics at Universitat Pompeu Fabra and director of the neuro-engineering program at the Institute for Bioengineering of Catalunya, Barcelona Institute of Science and Technology. Paul directs the Synthetic Perceptive, Emotive and Cognitive Systems (SPECS) Laboratory (specs-lab.com). He is founder/CEO of Eodyne Systems S.L. (Eodyne.com), which is commercializing a novel science grounded neurorehabilitation technology. Paul is founder/Chairman of the Future Memory Foundation (futurememoryfoundation.org) which aims at supporting the development of new tools and paradigms for the conservation, presentation, and education of the history of the Holocaust and Nazi crimes. He received his MA and Ph.D. in Psychology, and Paul's scientific aim is to find a unified theory of mind and brain using synthetic methods and to apply it to the quality of life enhancing technologies. His theory of mind and brain, Distributed Adaptive Control, has been generalized to a range of brain structures and robotic systems and has laid the foundation for a novel neurorehabilitation approach called the Rehabilitation Gaming System (http://specs.upf.edu/research_in_neurorehabilitation). Paul explores new methods for the simulation, visualization, and exploration of complex data to support his DAC theory and advance clinical diagnostics and intervention in neuropathologies (brainx3.com). Complementary to his science, Paul has developed and deployed over 25 art installations (http://specs.upf.edu/installations). These include the biomimetic mixed reality space Ada experienced by over half a million visitors (2002) and more recently three virtual/augmented reality educational installations and applications for the Memorial Site Bergen-Belsen (2012 - ) which is now generalized to other sites across Europe.
The mechanisms of how the brain orchestrates multi-limb joint action have yet to be elucidated an... more The mechanisms of how the brain orchestrates multi-limb joint action have yet to be elucidated and few computational sensorimotor (SM) learning approaches have dealt with the problem of acquiring bimanual affordances. We propose a series of bidirectional (forward/inverse) SM maps and its associated learning processes that generalize from uni- to bimanual interaction (and affordances) naturally, reinforcing the motor equivalence property. The SM maps range from a SM nature to a solely sensory one: full body control, delta SM control (through small action changes), delta sensory co-variation (how body-related perceptual cues covariate with object-related ones). We make several contributions on how these SM maps are learned: (1) Context and Behavior-Based Babbling: generalizing goal babbling to the interleaving of absolute and local goals including guidance of reflexive behaviors; (2) Event-Based Learning: learning steps are driven by visual, haptic events; and (3) Affordance Gradients...
The visual systems of insects perform complex processing using remarkably compact neural circuits... more The visual systems of insects perform complex processing using remarkably compact neural circuits, yet these circuits are often studied using simplified stimuli which fail to reveal their behaviour in more complex visual environments. We address this issue by testing models of these circuits in real-world visual environments using a mobile robot. In this paper we focus on the lobula giant movement detector (LGMD) system of the locust which responds selectively to objects which approach the animal on a collision course and is thought to trigger escape behaviours. We show that a neural network model of the LGMD system shares the preference for approaching objects and detects obstacles over a range of speeds. Our results highlight aspects of the basic response properties of the biological system which have important implications for the behavioural role of the LGMD.
The mechanisms of how the brain orchestrates multi-limb joint action have yet to be elucidated an... more The mechanisms of how the brain orchestrates multi-limb joint action have yet to be elucidated and few computational sensorimotor (SM) learning approaches have dealt with the problem of acquiring bimanual affordances. We propose a series of bidirectional (forward/inverse) SM maps and its associated learning processes that generalize from uni- to bimanual interaction (and affordances) naturally, reinforcing the motor equivalence property. The SM maps range from a SM nature to a solely sensory one: full body control, delta SM control (through small action changes), delta sensory co-variation (how body-related perceptual cues covariate with object-related ones). We make several contributions on how these SM maps are learned: (1) Context and Behavior-Based Babbling: generalizing goal babbling to the interleaving of absolute and local goals including guidance of reflexive behaviors; (2) Event-Based Learning: learning steps are driven by visual, haptic events; and (3) Affordance Gradients...
The visual systems of insects perform complex processing using remarkably compact neural circuits... more The visual systems of insects perform complex processing using remarkably compact neural circuits, yet these circuits are often studied using simplified stimuli which fail to reveal their behaviour in more complex visual environments. We address this issue by testing models of these circuits in real-world visual environments using a mobile robot. In this paper we focus on the lobula giant movement detector (LGMD) system of the locust which responds selectively to objects which approach the animal on a collision course and is thought to trigger escape behaviours. We show that a neural network model of the LGMD system shares the preference for approaching objects and detects obstacles over a range of speeds. Our results highlight aspects of the basic response properties of the biological system which have important implications for the behavioural role of the LGMD.
BrainX 3 is a large-scale simulation of human brain activity, rendered in 3D in a virtual reality... more BrainX 3 is a large-scale simulation of human brain activity, rendered in 3D in a virtual reality environment. 2 This simulation is grounded in the structural connectivity obtained from diffusion spectrum imaging data of five healthy right-handed male volunteers. 4 BrainX 3 serves as a data mining platform for visualization, analysis, and feature extraction of neuroscience data. 1 As a proof of principle, we examine BrainX 3 's ability to map the aphasia connectome, and assess its ability to predict Transcranial Magnetic Stimulation (TMS)-induced language deficits. In neurosurgery, one of the primary goals is to maximize the resection of pathologic tissue while minimizing functional neurologic deficits. This goal becomes especially salient when operating around eloquent cortical areas, such as those involved in language function. Unfortunately, the prediction of cortical areas involved in language through classic anatomical topography is not sufficient due to interindividual variability of cortical organization. The main solutions to this problem utilize preoperative or intraoperative mapping of language function to ensure that eloquent cortex will be spared. The current gold-standard in language mapping for neurosurgical patients is direct cortical stimulation. However, due to its invasive nature, DCS cannot be used to map the healthy human brain. Furthermore, it is well known that in epilepsy or brain tumor patients, there is more likely to be an atypical language organization. 3,5 Given these considerations, we investigated the effect of TMS-induced " virtual lesions " in healthy participants undergoing an object-naming task. 6 We hypothesized that sites that were more likely to produce object-naming errors were more likely to have projections to language areas such as Broca's and Wernicke's areas.
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Papers by Paul Verschure