| || Models ||Description|
Parallelizing large networks in NEURON (Lytton et al. 2016)
||"Large multiscale neuronal network simulations and
innovative neurotechnologies are required for development of these models requires
development of new simulation technologies.
We describe here the current use of
the NEURON simulator with MPI (message passing interface) for simulation in
the domain of moderately large networks on commonly available High
Performance Computers (HPCs).
We discuss the
basic layout of such simulations, including the methods of simulation setup, the
run-time spike passing paradigm and post-simulation data storage and data
We also compare three types of
Reaction-diffusion in the NEURON simulator (McDougal et al 2013)
||"In order to support research on the role of cell biological principles (genomics, proteomics, signaling cascades and reaction dynamics) on the dynamics of neuronal response in health and disease, NEURON's Reaction-Diffusion (rxd) module in Python provides specification and simulation for these dynamics, coupled with the electrophysiological dynamics of the cell membrane. Arithmetic operations on species and parameters are overloaded, allowing arbitrary reaction formulas to be specified using Python syntax. These expressions are then transparently compiled into bytecode that uses NumPy for fast vectorized calculations. At each time step, rxd combines NEURON's integrators with SciPy's sparse linear algebra library."