GENESIS, The GEneral NEural SImulation System

Bower, James M.; Cornelis, Hugo; Beeman, David

doi:10.1007/978-1-4614-7320-6_255-1

Cited by 23 publications

(20 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Over the years, several simulators have been developed to integrate systems of coupled differential equations that model the spiking activity of neurons (Hines and Carnevale, 1997 ; Bower et al, 2003 ; Delorme and Thorpe, 2003 ; Brette et al, 2007 ; Vitay et al, 2015 ; Sherfey et al, 2018 ). A critical architectural choice in designing a simulator is how much “scaffolding” a user is provided with to construct a model; whether a model is specified by equations or by components, or by some combination of the two.…”

Section: Discussionmentioning

confidence: 99%

Xolotl: An Intuitive and Approachable Neuron and Network Simulator for Research and Teaching

Gorur-Shandilya

Hoyland

Marder

2018

Front. Neuroinform.

View full text Add to dashboard Cite

Conductance-based models of neurons are used extensively in computational neuroscience. Working with these models can be challenging due to their high dimensionality and large number of parameters. Here, we present a neuron and network simulator built on a novel automatic type system that binds object-oriented code written in C++ to objects in MATLAB. Our approach builds on the tradition of uniting the speed of languages like C++ with the ease-of-use and feature-set of scientific programming languages like MATLAB. Xolotl allows for the creation and manipulation of hierarchical models with components that are named and searchable, permitting intuitive high-level programmatic control over all parts of the model. The simulator's architecture allows for the interactive manipulation of any parameter in any model, and for visualizing the effects of changing that parameter immediately. Xolotl is fully featured with hundreds of ion channel models from the electrophysiological literature, and can be extended to include arbitrary conductances, synapses, and mechanisms. Several core features like bookmarking of parameters and automatic hashing of source code facilitate reproducible and auditable research. Its ease of use and rich visualization capabilities make it an attractive option in teaching environments. Finally, xolotl is written in a modular fashion, includes detailed tutorials and worked examples, and is freely available at https://github.com/sg-s/xolotl, enabling seamless integration into the workflows of other researchers.

show abstract

Section: Discussionmentioning

confidence: 99%

Xolotl: An Intuitive and Approachable Neuron and Network Simulator for Research and Teaching

Gorur-Shandilya

Hoyland

Marder

2018

Front. Neuroinform.

View full text Add to dashboard Cite

show abstract

“…In the early 1990s, the simulator “NEURON” was developed for empirically based simulations of single and networks of neurons with complex anatomical and biophysical properties, such as complex branching morphology, multiple channel types, inhomogeneous channel distribution, ionic diffusion and the effects of second messengers (Hines, 1989, 1993). During the same period, was released the GEneral NEural SImulation System (GENESIS), a simulation environment for constructing realistic models of neurobiological systems from subcellular processes and individual neurons to networks of neurons and neuronal systems (Wilson et al, 1989; Bower et al, 2013). In the following years, simulators such as MCell and STEPS were developed to simulate biochemical signaling pathways at the molecular scale (Stiles et al, 1996; Hepburn et al, 2012).…”

Section: Functional Mapping: From Cranial Bumps Towards Neural Mechanmentioning

confidence: 99%

A Brief History of Simulation Neuroscience

Fan

Markram

2019

Front. Neuroinform.

View full text Add to dashboard Cite

Our knowledge of the brain has evolved over millennia in philosophical, experimental and theoretical phases. We suggest that the next phase is simulation neuroscience. The main drivers of simulation neuroscience are big data generated at multiple levels of brain organization and the need to integrate these data to trace the causal chain of interactions within and across all these levels. Simulation neuroscience is currently the only methodology for systematically approaching the multiscale brain. In this review, we attempt to reconstruct the deep historical paths leading to simulation neuroscience, from the first observations of the nerve cell to modern efforts to digitally reconstruct and simulate the brain. Neuroscience began with the identification of the neuron as the fundamental unit of brain structure and function and has evolved towards understanding the role of each cell type in the brain, how brain cells are connected to each other, and how the seemingly infinite networks they form give rise to the vast diversity of brain functions. Neuronal mapping is evolving from subjective descriptions of cell types towards objective classes, subclasses and types. Connectivity mapping is evolving from loose topographic maps between brain regions towards dense anatomical and physiological maps of connections between individual genetically distinct neurons. Functional mapping is evolving from psychological and behavioral stereotypes towards a map of behaviors emerging from structural and functional connectomes. We show how industrialization of neuroscience and the resulting large disconnected datasets are generating demand for integrative neuroscience, how the scale of neuronal and connectivity maps is driving digital atlasing and digital reconstruction to piece together the multiple levels of brain organization, and how the complexity of the interactions between molecules, neurons, microcircuits and brain regions is driving brain simulation to understand the interactions in the multiscale brain.

show abstract

“…GENESIS (Bower et al 2013), MOOSE (Ray and Bhalla 2008), NEURON (Carnevale and Hines 2006), and STEPS (Hepburn et al 2012) allow coupling between reaction-diffusion system and electrophysiology models. Historically, general neural modeling software like GENESIS, MOOSE, and NEURON allowed 1D longitudinal diffusion between compartments, which could be extended to 2D diffusion by specifying concentric shells within a compartment and writing equations for diffusion between adjacent shells.…”

Section: Practicementioning

confidence: 99%