Virtual Reality in Computational Neuroscience

2017

In this work, we present a computational toolkit to perform neuroscience experiments with people inside virtual immersive environment like a CAVE (Computer Assisted Virtual Environment). Several experiments can be designed, performed and measured for customized virtual scenes. Each participant is exposed to experiences in which they search and collect objects within a three-dimensional space, where they have to use spatial strategies to solve the task. The purpose is to study cognitive functions such as perception, attention, cognition and short term memory. These tools can reduce the time of experiments formulations and helps to discover mechanisms of attention and spatial and episodic memory. All information about each participant trajectory, the virtual scene configuration, the location of objects in these scenes, and answers to particular questions regarding the experience is collected and presented in.

2014

Young students struggle with concepts that involve the parallel activity of large numbers of similar entities, precisely the kind of concepts that abound in neuroscience. While a direct experience to laboratory work cannot be replaced, such activities include a steep learning curve and may be impractical in certain course settings. This article describes a set of computer simulations of a number of neural processes using NetLogo (Wilensky, 1999), a software environment for the design and implementation of multi-agent simulations that has an intuitive graphical interface and minimal learning curve. NeuroLab is a group of graphical simulations that portray ions, molecules, synapses or cells as individual recognizable agents with particular behaviors, depending on the level at which the particular process is simulated. On a typical assignment, students run the simulation a few times manipulating specific variables by means of buttons, switches and sliders and observe the results of the...

Introduction: A brain connectome models the clusters of neurons as inter-connected nodes and can be obtained from different imaging techniques, including diffusion and functional MRI, for structural and functional connectivity, respectively. It offers us the opportunity to gain further insights regarding neural circuitry to better understand the mechanisms of brain functions and diseases. However, visualization, spatial understanding, and analysis of the network's topology are difficult due to the neuroanatomy's complex configuration of brain parcellation and 3D nature. Virtual reality (VR) off ers more intuitive visualization andinteraction for 3D data than traditional 2D displays and is a great fi t to tackle the challenges in visualizing andanalyzing brain connectomes. We introduce a novel immersive VR platform for connectomic data exploration, VRNConnect, with a user-friendly interface for quantitative network analysis and exploration. We demonstrated the functionalitie...

Frontiers in Neuroinformatics, 2016

2017

This paper introduces NeuroCave, an open-source immersive analytics software tool for exploring and making sense of both structural and functional connectomes describing the interconnectivity between brain regions. The software supports visual analysis tasks for clinical neuroscientists, facilitating explorations of connectome datasets used in comparative group studies. Researchers can enter and leave virtual reality as desired in order to visualize and reason about connectome datasets from a range of different perspectives.

Biosystems & Biorobotics, 2015

Studying and modeling the brain as a whole is a real challenge. For such systemic models (in contrast to models of one brain area or aspect), there is a real need for new tools designed to perform complex numerical experiments, beyond usual tools distributed in the computer science and neuroscience communities. Here, we describe an effective solution, freely available on line and already in use, to validate such models of the brain functions. We explain why this is the best choice, as a complement to robotic setup, and what are the general requirements for such a benchmarking platform. In this experimental setup, the brainy-bot implementing the model to study is embedded in a simplified but realistic controlled environment. From visual, tactile and olfactory input, to body, arm and eye motor command, in addition to vital interoceptive cues, complex survival behaviors can be experimented. We also discuss here algorithmic high-level cognitive modules, making the job of building biologically plausible bots easier. The key point is to possibly alternate the use of symbolic representation and of complementary and usual neural coding. As a consequence, algorithmic principles have to be considered at higher abstract level, beyond a given data representation, which is an interesting challenge.

2013 IEEE Symposium on Biological Data Visualization (BioVis), 2013

: VisNEST's control view allows users to inspect neural activity linked to a geometric representation of a macaque brain. Textual annotations show anatomical area designators (right). By selecting a brain area, users may access the corresponding raster plot (top left) and connectivity of populations (bottom left).

Virtual Reality, 1998

While many virtual reality (VR) applications have emerged in the areas of entertainment, education, military training, physical rehabilitation, and medicine, only recently have some reseamh projects begun to test the possibility of using virtual environments (VEs) for research in neuroscience, neurosurgery and for the study and rehabilitation of human cognitive and functional activities. Virtual reality technology could have a strong impact on neuroscience. The key characteristic of VEs is the high level of control of the interaction with the tool without the constraints usually found in computer systems. VEs are highly flexible and programmable. They enable the therapist to present a wide variety of controlled stimuli and to measure and monitor a wide variety of responses made by the user. However, at this stage, a number of obstacles exist which have impeded the development of active research. These obstacles include problems with acquiring funding for an almost untested new treatment modality, the lack of reference standards, the non-interoperability of the VR systems and, last but not least, the relative lack of familiarity with the technology on the part of researchers in these fields.

Large multiscale neuronal network simulations are of increasing value as more big data are gathered about brain wiring and organization under the auspices of a current major research initiative, such as Brain Research through Advancing Innovative Neurotechnologies. The development of these models requires new simulation technologies. We describe here the current use of the NEURON simulator with message passing interface (MPI) for simulation in the domain of moderately large networks on commonly available high-performance computers (HPCs). We discuss the basic layout of such simulations, including the methods of simulation setup, the run-time spike-passing paradigm, and postsimulation data storage and data management approaches. Using the Neuroscience Gateway, a portal for computational neuroscience that provides access to large HPCs, we benchmark simulations of neuronal networks of different sizes (500–100,000 cells), and using different numbers of nodes (1–256). We compare three types of networks, composed of either Izhike-vich integrate-and-fire neurons (I&F), single-compartment Hodgkin-Huxley (HH) cells, or a hybrid network with half of each. Results show simulation run time increased approximately linearly with network size and decreased almost linearly with the number of nodes. Networks with I&F neurons were faster than HH networks, although differences were small since all tested cells were point neurons with a single compartment.

Companion Proceedings of the 2020 Conference on Interactive Surfaces and Spaces, 2020

Figure 1: a) Traditional approach for the analysis of brain spatial and temporal datasets with two different tools. b) Our approach for understanding spatiotemporal phenomena in a brain with the situated context information.