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Welcome to the documentation of hybridLFPy!

Notes on performance

The present version of hybridLFPy may facilitate on a trivial parallelism as the contribution of each single-cell LFP can be computed independently. However, this does not imply that the present implementation code is highly optimized for speed. In particular, initializing the multicompartment neuron populations do not as much benefit from increasing the MPI pool size, as exemplified by a benchmark based on the Brunel-network example scaled up to 50,000 neurons and with simplified neuron morphologies.

Scaling example with hybridLFPy based on a Brunel-like network with 50,000 neurons, running on the JURECA cluster at the Juelich Supercomputing Centre (JSC), Juelich Research Centre, Germany.

Scaling example with hybridLFPy based on a Brunel-like network with 50,000 neurons, running on the JURECA cluster at the Juelich Supercomputing Centre (JSC), Juelich Research Centre, Germany.

Module hybridLFPy

hybridLFPy

class CachedNetwork

hybridLFPy.CachedNetwork

class CachedNoiseNetwork

hybridLFPy.CachedNoiseNetwork

class CachedFixedSpikesNetwork

hybridLFPy.CachedFixedSpikesNetwork

class PopulationSuper

hybridLFPy.PopulationSuper

class Population

hybridLFPy.Population

class PostProcess

hybridLFPy.PostProcess

class GDF

hybridLFPy.GDF

submodule helpers

hybridLFPy.helpers

submodulue test

hybridLFPy.test

Indices and tables

  • genindex
  • modindex
  • search