Tutorial 8 — Modules: share and compose circuits

A module is what one person designs or evolves and another reuses as-is: a body plan, a genome, an interface (input and output regions), validation records, provenance and the neuron model it was validated with.

Save

from neurogenesis.modules import Module

m = Module("topographic map", plan, genome,
           inputs=["sensor"], outputs=["map"],
           description="sensor sheet -> topographic map; input: constant current 120 on sensor cells",
           validation={"seeds": [0, 1, 2], "metric": "topographic precision", "pass_fraction": 1.0},
           provenance={"authors": ["..."], "repository": "...", "commit": "..."},
           neuron_model="lif_int")
m.save("topographic_map.module.json")

Load and grow

m = Module.load("topographic_map.module.json")   # its plugins are imported, its genes kept
net = m.grow(seed=3)

Compose

from neurogenesis.modules import compose
eye = Module.load("eye.module.json")
circuit = Module.load("circuit.module.json")
system = compose("eye+circuit", {"eye": eye, "circuit": circuit},
                 links=[("eye.output", "circuit.input",
                         dict(B=np.eye(3), c=np.zeros(3), sigma=0.2, p0=0.5, w=200.0))])
net = system.grow(seed=0)

Region names get the module prefix (eye.output); each module keeps its own genes and plugin rules.
Links add projections with their own guidance genes, which can themselves be evolved.
Re-audit the composed system: a module validated alone may behave differently once connected.

Validation records

Write in validation how the module was tested: stimuli, readout, metric, the development seeds used, and the fraction of held-out seeds that pass. A declarative assay format, re-run by neurogenesis validate module.json, is planned (roadmap task E4).

PLACEHOLDER

A module registry (roadmap G1): neurogenesis install <module>, semantic versions, compatibility with toolbox versions, and a browsable gallery of published modules with their validation reports and screenshots.

Versions, registry and validation

Module has a semantic version and a toolbox range. python -m neurogenesis install module.json copies it into the local catalogue, and neurogenesis.modules.registry.get(name) loads the latest compatible version. Assays stored in validation["assays"] are rerun by python -m neurogenesis validate module.json. See Tutorial 12 — Fit to data, validate, share.