VisNEST — Interactive analysis of neural activity data

Abstract

Figure 1: 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). ABSTRACTThe aim of computational neuroscience is to gain insight into the dynamics and functionality of the nervous system by means of modeling and simulation. Current research leverages the power of High Performance Computing facilities to enable multi-scale simulations capturing both low-level neural activity and large-scale interactions between brain regions. In this paper, we describe an interactive analysis tool that enables neuroscientists to explore data from such simulations. One of the driving challenges behind this work is the integration of macroscopic data at the level of brain regions with microscopic simulation results, such as the activity of individual neurons. While researchers validate their findings mainly by visualizing these data in a non-interactive fashion, state-of-the-art visualizations, tailored to the scientific question yet sufficiently general to accommodate different types of models, enable such analyses to be performed more efficiently. This work describes several visualization designs, conceived in close collaboration with domain experts, for the analysis of network models. We primarily focus on the exploration of neural activity data, inspecting connectivity of brain regions and populations, and visualizing activity flux across regions. We demonstrate the effectiveness of our approach in a case study conducted with domain experts.

RemBrain: Exploring Dynamic Biospatial Networks with Mosaic Matrices and Mirror Glyphs

“…However, as the non-spatial data becomes more complex (activation levels, connectivity, clusters, dynamic characteristics, and other statistics), the linked-view paradigm 15–17,21 becomes the default choice. Nowke et al 14 use a hybrid approach that consists of both overlays and linked views. We follow a similar hybrid approach to support the exploration of dynamic biospatial networks.…”

“…In the visual analysis of neuroscience data, VisNEST 14 and NeuroLines 16 integrate data at macroscopic level with microscopic level. We similarly adopt a multiple views approach for different levels using both focus+context and details on demand.…”

Quo Vadis CAVE: Does Immersive Visualization Still Matter?

“…Here, we briefly discuss VisNEST, an application that integrated a variety of data modalities to help users form a comprehensive mental model of the underlying raw data. 10 The data resulted from a project aiming at biologically realistic simulation of large neural networks. For example, one simulation model consisted of 32 areas of the Macaque visual cortex.…”

Brain-Inspired Computing

“…Once more, all these views are semantically linked, helping the neuroscientists to reveal characteristic behaviors of network simulations. The overall tool and its functionality are described in more detail in Nowke et al (2013).…”