Standards
These are focused on spiking models and defining ODE-level model descriptions:
- Open Source Brain: http://www.opensourcebrain.org
- NeuroML: https://www.neuroml.org
- PyNN: https://neuralensemble.org/PyNN/
- nest: https://www.nest-simulator.org -- based on PyNN
- BMTK Allen Institute: https://alleninstitute.github.io/bmtk/ -- includes SONATA data format
- ModCI: https://github.com/ModECI/MDF
These are standards for defining computational graphs among tensor-valued layers:
Dai, K., Hernando, J., Billeh, Y. N., Gratiy, S. L., Planas, J., Davison, A. P., Dura-Bernal, S., Gleeson, P., Devresse, A., Dichter, B. K., Gevaert, M., King, J. G., Geit, W. A. H. V., Povolotsky, A. V., Muller, E., Courcol, J.-D., & Arkhipov, A. (2020). The SONATA data format for efficient description of large-scale network models. PLOS Computational Biology, 16(2), e1007696. https://doi.org/10.1371/journal.pcbi.1007696
Each edge identifies the node_id of the source node and the node_id of the target, plus relevant attributes like location of synapse and weight. There may be multiple edges for a single source/target pair. Edge populations are similar to “projections” found in other languages like PyNN, but with a key difference that edge populations may contain a heterogeneous array of connection types, partitioned into separate edge groups. Edge populations are not nested.
wow, pretty verbose format! very general, but..
encoded into HDF5: https://portal.hdfgroup.org/display/HDF5/HDF5
big model took about 5 minutes to setup! just the network config file alone was 2.4GB. they are trying to say that is not massive!
It should be noted that the computational performance of BioNet and PointNet relies on the excellent performance and parallelization capabilities of NEURON [8] and NEST [9]. What these BMTK modules add is the convenience and interoperability. For example, although NEURON provides powerful parallelization environment, users typically need to write parallel code in that environment to run their simulations. Likewise, constructing sophis- ticated bio-realistic models in NEURON or NEST requires substantial amount of coding. BMTK streamlines the latter part through the uniform model building operations in BMTK Builder and obviates the former part for the users by dealing with NEURON or NEST paralleli- zation “under the hood”, so that users do not need to write any code at all.
test