A framework for simulating and estimating the state and functional topology of complex dynamic geometric networks

TL;DR

This paper introduces a GPU-accelerated framework for simulating and estimating the state and functional topology of complex dynamic geometric networks, especially useful for biological cellular neural networks.

Contribution

It provides a novel, high-performance simulation and estimation framework tailored for dynamic geometric networks, with a focus on biological neural systems.

Findings

Simulation speed close to or faster than real time.

Framework effectively estimates functional connectivity from experimental data.

Standard test set enables performance comparison of mapping algorithms.

Abstract

We present a framework for simulating signal propagation in geometric networks (i.e. networks that can be mapped to geometric graphs in some space) and for developing algorithms that estimate (i.e. map) the state and functional topology of complex dynamic geometric net- works. Within the framework we define the key features typically present in such networks and of particular relevance to biological cellular neural networks: Dynamics, signaling, observation, and control. The framework is particularly well-suited for estimating functional connectivity in cellular neural networks from experimentally observable data, and has been implemented using graphics processing unit (GPU) high performance computing. Computationally, the framework can simulate cellular network signaling close to or faster than real time. We further propose a standard test set of networks to measure performance and compare different mapping algorithms.