git clone https://github.com/llandsmeer/spatial_snns/
pip install 'jax[cuda]' tqdm matplotlib h5py numpy optax pandas
For efficient execution you will need a GPU with a large amount of memory. In experiments, RTX PRO 6000 or H100 for larger networks, Quadro 6000 for nhidden < 150, and A2000 for small networks. CPU might work for very small networks.
The SHD dataset must be downloaded from https://zenkelab.org/datasets/
To run the training script, please run python3 train.py.
Some examples:
-
Train unconstrained network:
python3 train.py --net inf --dt 0.5 --nhidden 100 --batch_size 32 -
Interactive visualization:
mkdir -p /tmp/figs/python3 train.py --net 2 --circle --dt 0.5 --skip --nhidden 100 --lr 1e-3 --vplot
To run many experiments, ie on a cluster, generate the submit script via multirun.py. It provides from functions for creating grid searches over parameters and seeds, and outputs either a SLURM script of a list of command lines to be invoked sequentially on a single-GPU machine.
Checkout to the yy_spatial branch.
To run the training script for the YY dataset, please run python3 train_yy.py with the following options:
`python3 train_yy.py --ndim 3 --nhidden 30 --dt 0.01 --lr 0.00015 --batch_size 150 --layer --force`
You can change the ndim and nhidden parameters to have networks with different dimensions and hidden neurons respectively.
usage: train.py [-h] [--net NET] [--nhidden NHIDDEN] [--batch_size BATCH_SIZE]
[--load_limit LOAD_LIMIT] [--load_limit_test LOAD_LIMIT_TEST] [--lr LR]
[--ifactor IFACTOR] [--rfactor RFACTOR] [--force] [--skip] [--debug]
[--dt DT] [--tmp] [--reload] [--seed SEED] [--nepochs NEPOCHS]
[--delaygradscale DELAYGRADSCALE] [--delaymu DELAYMU]
[--delaysigma DELAYSIGMA] [--possigma POSSIGMA] [--tgtfreq TGTFREQ]
[--population_freq] [--tag TAG] [--line] [--circle] [--wstat] [--istat]
[--sparse SPARSE] [--sparse_iter] [--adex] [--adex_a ADEX_A]
[--adex_b ADEX_B] [--adex_tau ADEX_TAU] [--adex_dt ADEX_DT] [--vplot]
[--pos] [--iadapt0 IADAPT0] [--shard]
options:
-h, --help show this help message and exit
--net NET Dimension (inf or int or <int>e<float> or <int>g<float>)
--nhidden NHIDDEN Number of hidden units
--batch_size BATCH_SIZE
Batch size
--load_limit LOAD_LIMIT
Load limit no samples
--load_limit_test LOAD_LIMIT_TEST
Load limit no test samples
--lr LR Learning rate
--ifactor IFACTOR Extra ifactor multiplier
--rfactor RFACTOR Extra ifactor multiplier
--force Overwrite
--skip Skip metadata loading
--debug Start pdb on error
--dt DT Time step (bigger=faster, smaller=more accurate)
--tmp Store in /tmp/saved
--reload Reload previous
--seed SEED Seed for network generation
--nepochs NEPOCHS Epochs to trian for
--delaygradscale DELAYGRADSCALE
Scale delay gradients (or 0 for none)
--delaymu DELAYMU Mu
--delaysigma DELAYSIGMA
Sigma
--possigma POSSIGMA Sigma
--tgtfreq TGTFREQ Target frequency Hz
--population_freq Target freq after mean
--tag TAG Experiment ids
--line Initialize inputs on a line
--circle Initialize inputs on a circle
--wstat Initialize all weights at same value
--istat Dont move inputs
--sparse SPARSE Sparsify after each epoch
--sparse_iter Iteratively increase
--adex Use AdEx model instead of LIF
--adex_a ADEX_A AdEx a
--adex_b ADEX_B AdEx b
--adex_tau ADEX_TAU AdEx w tau
--adex_dt ADEX_DT AdEx DeltaT
--vplot AdEx model
--pos Force pos
--iadapt0 IADAPT0 Init iadapt
--shard Target all devices
Please cite as
Landsmeer, L. P. L., Movahedin, A., Negrello, M., Hamdioui, S., & Strydis, C. (2025). Spatial Spiking Neural Networks Enable Efficient and Robust Temporal Computation. arXiv preprint arXiv:2512.10011.
This work is partially supported by the European-Union Horizon Europe R&I program through projects SEPTON (no. 101094901) and SECURED (no. 101095717) and through the NWO - Gravitation Programme DBI2 (no. 024.005.022). This work used the Dutch national e-infrastructure with the support of the SURF Cooperative using grant no. EINF-10677, EINF-15815, and EINF-16791. The RTX PRO 6000 and Quadro Pro 6000 GPUs used for this research were donated by the NVIDIA Corporation.