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Guidelines for developing NeuroJAX (OSL-JAX) components.
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Basierend auf der SOC-Berufsklassifikation
Validate BEM Forward Model Accuracy using the MNE CTF Phantom dataset.
Validate Inverse Solver Accuracy using the MNE Elekta Phantom dataset.
Quantify Inverse Solver Leakage and Resolution using a Synthetic Phantom.
Detect Loss of Consciousness (LOC) using SINDy-based bifurcation analysis on EEG.
Validate Artifact Subspace Reconstruction (ASR) using the SSVEP with Artifact Trials dataset (ds004745).
Guidelines for implementing biophysical neural mass models in NeuroJAX.
| name | neurojax_dev |
| description | Guidelines for developing NeuroJAX (OSL-JAX) components. |
NeuroJAX follows the "Kidger Stack" philosophy:
equinox.Module. State is explicit and immutable.lineax for linear solves and optimistix for non-linear optimization.numpy (except for I/O); use jax.numpy.jaxtyping to enforce shapes, e.g., Float[Array, "time sensors"].src/neurojax/glm.py: Mass-univariate statistics.src/neurojax/inverse/: Beamformers and source reconstruction.src/neurojax/models/: Biophysical and generative models (Diffrax, Equinox).src/neurojax/utils/: Bridges and helpers.When solving $Ax=b$, prefer lineax over jnp.linalg.solve:
operator = lx.MatrixLinearOperator(A)
solution = lx.linear_solve(operator, b, solver=lx.QR())
Passing key is mandatory for stochastic operations. Split keys early:
keys = jax.random.split(key, num=100)
jax.vmap(func)(keys)