International Supercomputing Conference 2013

Presentation "Visualization Neural Activity of the Macaque Visual Cortex" at the booth of JARA-HP

Visualization Neural Activity of the Macaque Visual Cortex

Presentation "Visualization Neural Activity of the Macaque Visual Cortex" at the booth of JARA-HP

Interactive visualization and steering of structural plasticity in NEST

Neuronal models and neural networks, contain many degrees of freedom which determine their dynamics. Characterization of the parameter and solution spaces is fundamental to generate relevant conclusions about the system. Rigorous parameter space exploration is rarely performed. In this work we discuss the development and application of a software tool which allows interactive visualization and steering of parameters in neural networks in NEST. Examples of its usage, conclusions and future work are discussed

Using structural plasticity to study the relationship between structure and function in the brain

This poster presents some of the work which has been carried out around Structural Plasticity in the context of the HBP

Integrating Visualizations into Modeling NEST Simulations

Modeling large-scale spiking neural networks showing realistic biological behavior in their dynamics is a complex and tedious task. Since these networks consist of millions of interconnected neurons, their simulation produces an immense amount of data. In recent years it has become possible to simulate even larger networks. However, solutions to assist researchers in understanding the simulation's complex emergent behavior by means of visualization are still lacking. While developing tools to partially fill this gap, we encountered the challenge to integrate these tools easily into the neuroscientists' daily workflow. To understand what makes this so challenging, we looked into the workflows of our collaborators and analyzed how they use the visualizations to solve their daily problems. We identified two major issues: first, the analysis process can rapidly change focus which requires to switch the visualization tool that assists in the current problem domain. Second, because of the heterogeneous data that results from simulations, researchers want to relate data to investigate these effectively. Since a monolithic application model, processing and visualizing all data modalities and reflecting all combinations of possible workflows in a holistic way, is most likely impossible to develop and to maintain, a software architecture that offers specialized visualization tools that run simultaneously and can be linked together to reflect the current workflow, is a more feasible approach. To this end, we have developed a software architecture that allows neuroscientists to integrate visualization tools more closely into the modeling tasks. In addition, it forms the basis for semantic linking of different visualizations to reflect the current workflow. In this paper, we present this architecture and substantiate the usefulness of our approach by common use cases we encountered in our collaborative work

Multiscale approach to explore the relationships between connectivity and function in whole brain simulations

To better understand the relationship between connectivity and function in the brain at different scales, in this work we show the results of using point-neuron network simulations to complement connectivity information from whole brain simulations based on a dynamic neuron mass model. In our multiscale approach, we simulate a whole brain parcellated into 68 regions where each region is modeled as a dynamic neuron mass, and in parallel, we also model each region as small 200 point-neuron populations in NEST. Structural plasticity in NEST is then used to calculate inner connectivity of each region with the aid of an interactive tool designed for visualizing and steering the algorithm. Using this approach, the fitting and parameter space exploration times are reduced and a new way to explore the impact of connectivity in function at different scales is presented

Multiscale approach to explore the relationships between connectivity and function in whole brain simulations

In order to better understand the relationship of connectivity and function in the brain at different scales, in this work we show the results of using point neuron network simulations to complement connectivity information of whole brain simulations based on a dynamic neuron mass model. In our multiscale approach, we simulate a whole brain parcellated into 68 regions using a similar setup as described in Deco et al. 2014. Each region is modeled as a dynamic neuron mass and, in parallel, we also model each region as small point neuron populations in NEST. Structural plasticity in NEST is then used to calculate inner inhibitory connectivity required to match experimentally observed firing rate behavior. An interactive tool was developed in order to steer the structural plasticity algorithm and take all the regions, which are also highly interconnected, to their ideal firing activity. An inner inhibition fitting was first proposed in the work by Deco 2014, using an iterative tunning method. In our work, we allow the point neuron network to self generate the connectivity using simple homeostatic rules and then we feed this information to the dynamic mass model simulation. With the resulting connectivity data from the NEST simulations and experimentally obtained DTI inter region connectivity, simulations of the whole brain producing results comparable to experimental fMRI data are possible. Using this approach, the fitting and parameter space exploration times are reduced and a new way to explore the impact of connectivity in function at different scales is presented