# Neurojax Biophysics

> Guidelines for implementing biophysical neural mass models in NeuroJAX. Use when this capability is needed.

- Skill: `tomevault-io/neurojax-biophysics` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/neurojax-biophysics`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/neurojax-biophysics/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/neurojax-biophysics

---


# NeuroJAX (OSL-JAX) Biophysics

## Goal
To implement differentiable biophysical models (Neural Masses) that can be fitted to data using `diffrax` and `optimistix`.

## Physics Kernels
All models should inherit from a common base and solve ODEs/SDEs.

### Wong-Wang (Reduced)
- **Use Case**: Whole-brain functional connectivity fitting.
- **Complexity**: Low (2 variables).
- **Implementation**: See `vbjax` for reference equations. Wraps in `equinox.Module`.

### Canonical Microcircuit (CMC)
- **Use Case**: Layer-specific inference (Laminar Dynamics).
- **Complexity**: High (4 populations: SS, SP, II, DP).
- **Origin**: SPM Dynamic Causal Modelling (DCM).
- **Implementation**: Needs `diffrax` ODE solver.

## Implementation Pattern
```python
class AbstractNeuralMass(eqx.Module):
    def vector_field(self, t, y, args):
        raise NotImplementedError

class WongWang(AbstractNeuralMass):
    coupling: float
    def vector_field(self, t, y, args):
        # dx/dt = ...
        return dS
```

---
> Converted and distributed by [TomeVault](https://tomevault.io/claim/m9h) — claim your Tome and manage your conversions.
<!-- tomevault:4.0:skill_md:2026-04-13 -->