Running grown networks in other simulators

The toolbox’s own kernels are deliberately simple and fast (integer LIF with bit-identical spikes across implementations). For detailed or large-scale simulation, grown networks are handed to established simulators through interchange formats (roadmap phase J).

SONATA (available)

python -m neurogenesis export network.json --format sonata --out circuit/    # prints an export report
python -m neurogenesis import circuit/ --format sonata --out back.json       # back.json == network.json

from neurogenesis.interop import export_network, import_network
report = export_network(net, "sonata", "circuit")    # needs: pip install "neurogenesis-sim[sonata]" (h5py)
print(report)                                         # exact / approximated / dropped, as JSON
net2 = import_network("sonata", "circuit")

The directory holds circuit_config.json, network/nodes.h5, network/edges.h5, the node and edge type tables and NEST iaf_psc_exp parameter files: the layout of the SONATA format (Dai et al. 2020), read by BMTK (which runs NEST and NEURON), libsonata, PyNN (pyNN.serialization.import_from_sonata) and Connectome-Manipulator. Every toolbox field is also stored raw, so reading the circuit back gives the same network bit for bit (tested), and the edge indices are checked with libsonata.

How the integer neuron is translated

All exporters share one translation (neurogenesis.interop.dynamics), into a current-based exponential LIF (NEST iaf_psc_exp, PyNN IF_curr_exp):

Toolbox	Exported	Exact?
`shift_v`, `shift_s`	`tau_m`, `tau_syn = −1 / ln(1 − 2^−shift)` ms	per-tick decay exact; rounding not
voltage units, `threshold`, reset −256	`V = E_L + s·v`: `V_th`, `V_reset` (default `E_L` −65 mV, `s` 15/1024 mV)	exact (a free scale)
weight `w`	`W = C_m·s·w·(1 − a)/(4a·τ_syn)` pA, `a = 2^−shift_s`	total charge matched; PSP shape approximate
delay `d` ticks	`d + 1` ms	the kernel’s extra tick of latency included
`refractory` ticks	`t_ref` ms	close
Poisson and burster drivers	nodes with their rates as attributes	their spontaneous spikes are not generated elsewhere (reported)
integer rounding, minimum decrement, ±2048 clamp	—	not represented

So spike times in another simulator are close to the toolbox’s, not identical. The export report says this every time.

Other targets

Simulator / toolchain	Route	Status
NEST (Gewaltig & Diesmann 2007)	SONATA through BMTK PointNet, or PyNN	SONATA available; PyNN planned (J4)
NEURON (Hines & Carnevale 1997)	SONATA through BMTK; NeuroML 2	NeuroML 2 planned (J5)
Brian 2 (Stimberg et al. 2019)	PyNN backend	planned (J4)
Arbor (Abi Akar et al. 2019)	NeuroML 2 / morphologies (phase N)	planned
SpiNNaker (sPyNNaker, Rhodes et al. 2018)	PyNN	planned (J4)
snnTorch, Norse, Lava-DL, SpiNNaker2	NIR (Pedersen et al. 2024): small modules only, no per-synapse delays	planned (J3)

PLACEHOLDER

Cross-simulator checks (J6): the same grown networks simulated in the toolbox and, through SONATA and PyNN, in NEST and Brian 2, with spike statistics compared against declared tolerances.