Abstract
Neural Field models (NFM) play an important role in the understanding of neural population dynamics on a mesoscopic spatial and temporal scale. Their numerical simulation is an essential element in the analysis of their spatio-temporal dynamics. The simulation tool described in this work considers scalar spatially homogeneous neural fields taking into account a finite axonal transmission speed and synaptic temporal derivatives of first and second order. A text-based interface offers complete control of field parameters and several approaches are used to accelerate simulations. A graphical output utilizes video hardware acceleration to display running output with reduced computational hindrance compared to simulators that are exclusively software-based. Diverse applications of the tool demonstrate breather oscillations, static and dynamic Turing patterns and activity spreading with finite propagation speed. The simulator is open source to allow tailoring of code and this is presented with an extension use case.
Highlights
The understanding of spatio-temporal electric activity in neural tissue is essential in the study of neurobiological phenomena
Since future research in neural fields will investigate spatio-temporal dynamics involving finite axonal transmission speed, we have developed an open-source simulation toolbox that allows to gain spatio-temporal solutions of Neural Field models (NFM) models in two spatial dimensions, visualize them and save them, if necessary, as movies
The simulator interacts with graphics hardware using system-specific drivers which can result in problems on some operating systems
Summary
The understanding of spatio-temporal electric activity in neural tissue is essential in the study of neurobiological phenomena. NFMs serve as a good description of the dynamic source of Local Field Potentials and encephalographic data (Nunez, 1974, 2000; Wright and Kydd, 1992; Wright and Liley, 1994; Jirsa et al, 2002; Nunez and Srinivasan, 2006; Coombes et al, 2014) They allow to consider diverse single neuron features that may tune neural population dynamics, such as somatic (Molaee-Ardekani et al, 2007) and synaptic adaptation (Coombes and Owen, 2005; Kilpatrick and Bressloff, 2010), extrasynaptic receptor dynamics (Hashemi et al, 2014; Hutt and Buhry, 2014) or finite axonal transmission speed (Jirsa and Haken, 1996; Pinto and Ermentrout, 2001; Hutt et al, 2003, 2008; Coombes, 2005; Faye and Faugeras, 2010; Veltz and Faugeras, 2011, 2013). It is open source, enabling modification of the simulator in any beneficial way
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