API Reference
Canonical import:
This page is the complete index of the public API (jaxfne.__all__, 204 names),
grouped by module. Per-module pages carry detailed signatures and examples.
Current release jaxfne==0.4.4 (tag v0.4.4). The root-level export
helpers introduced in the v0.3.37/v0.3.38 line remain formal __all__ members.
Scope & truth gates
All field/EEG/MEG/EMM outputs are computational proxies
(claim_level = "computational_scaffold", field_solver_status = "linear_solver",
field_claim_level = "proxy_readout", physical_amplitude_calibrated = False).
See Limitations and future plans for the
scope statement.
Module pages
| Page |
Covers |
Public names |
| Core |
Configuration, Model, Simulation, Signals, readouts, receipts, suites |
69 |
| Emitters |
Izhikevich emitter, receptors, synapses, EIG networks, edge lists |
19 |
| Fields |
Source→laminar projection, FieldOutput, proxy diagnostics |
12 |
| Probes |
EEG/MEG/EMM proxy transforms (within Fields) |
(in Fields) |
| Objectives |
Objective, ObjectiveReport, rate targets |
(in Core) |
| Runtime |
RuntimeConfig, enable_x64, runtime_report |
(in Core) |
| Validation |
config/field validators, operator_status, is_valid_signal |
2 |
| (no page yet) |
Optimizers (optim, 15) · IO/receipts (10) · Export & figures (6) · Bridges (7) · Paradigms (6) · Solvers (7) · Sanity-delta (7) · Plasticity (5) · Tutorial utils (4) · Sharding (4) · Connectivity (3) · PyNWB (2) · Experimental HPC (2) · JAX Spectral Analysis (6) · geometry/builders/streaming/stimulus (13) |
102 |
Several public names (optimizers, IO, export, bridges, paradigms, sharding,
solvers) do not yet have a dedicated module page — they are listed with full
signatures in the complete symbol index below. Counts sum to 204
(len(jaxfne.__all__)). See the docs audit
(internal_docs/docs_audit_v0330.md) for the page-migration plan.
Minimal workflow (verified)
The pipeline is one linear chain: setup → config → construct → simulate →
visualize → tune/objective → optimize → export.
import jaxfne as jtfne
jtfne.enable_x64() # setup
cfg = jtfne.build_laminar_column(n=1000, ei_profile="canonical") # config (canonical prior)
cfg = (cfg.set_emitter("izhikevich", "cortical_eig")
.probes(["spikes", "V_m", "LFP", "CSD"], n_contacts=16)
.field(domain="laminar_column", conductivity="proxy", boundary="mean_zero_neumann"))
model = jtfne.construct(cfg) # construct -> Model
signals = jtfne.simulate(model, duration_ms=1000.0, dt_ms=0.5, seed=0) # simulate -> Signals
vm = signals.get("vm") # membrane voltage [T, N]
spk = signals.get("spk") # spikes [T, N]
e_idx = model.select(cell_type="E") # excitatory neuron indices
vm_e = signals.get("vm", cell_type="E") # equivalent to vm[:, e_idx]
assert vm_e.shape[-1] == len(e_idx)
Column builders & the canonical prior
build_laminar_column and build_multi_area_columns build a Configuration
with every knob defaulted, so the canonical cortex is reproducible from the call
site. Both are partial builders — chain .set_emitter().probes().field() before
construct.
build_laminar_column(...) -> Configuration
| Parameter |
Default |
Meaning |
name |
"V1" |
Column name. |
n |
1000 |
Total neurons; per-layer count ∝ depth-band width. |
layers |
DEFAULT_LAYERS |
5-layer set ("L1","L2/3","L4","L5","L6"). Use CANONICAL_LAYERS_6L for the split-L2/L3 form. |
layer_fractions |
canonical/even bands |
(z0, z1) depth band per layer; width sets the count. |
cell_type_fractions |
FLAT_CELL_TYPE_FRACTIONS |
Global E/PV/SST/VIP used by ei_profile="flat". |
layer_cell_type_fractions |
None |
Explicit per-layer composition; overrides ei_profile. |
ei_profile (kw) |
"flat" |
"flat" = legacy depth-invariant; "canonical" = verified E:I gradient (E deep ≈90%, I superficial 50%, PV at L4, ≈77E:23I). |
geometry (kw) |
"auto" |
"auto" → laminar when a non-flat composition is requested, else uniform3d. uniform3d collapses layer identity. |
within_connectivity (kw) |
"all_to_all_uniform_random" |
Within-area rule. |
within_gain (kw) |
0.45 |
Within-area weight gain. |
radius_mm, height_mm (kw) |
0.25, 1.6 |
Column cylinder geometry. |
edge_seed (kw) |
None |
Connectivity edge seed. |
Returns: a Configuration (single column). Examples:
jtfne.build_laminar_column() # V1, n=1000, flat E:I, uniform3d (legacy)
jtfne.build_laminar_column(ei_profile="canonical") # ground-truth gradient, laminar placement
jtfne.build_laminar_column("M1", 500, layers=["L2/3", "L5"], within_gain=0.6)
build_multi_area_columns(...) -> Configuration
| Parameter |
Default |
Meaning |
areas |
("V1","V4","PFC") |
Area names, low→high in the hierarchy. |
n_per_area |
200 |
Neurons per area. |
layers |
DEFAULT_LAYERS |
Shared layer sequence. |
connectivity_mode |
"sparse" |
Inter-area mode ("sparse"/"all_to_all"). |
ei_profile (kw) |
"flat" |
Per-layer composition applied to every area (see above). |
cell_type_fractions (kw) |
FLAT_CELL_TYPE_FRACTIONS |
Global fractions for ei_profile="flat". |
within_connectivity, within_gain (kw) |
"all_to_all_uniform_random", 0.35 |
Within-area rule/gain. |
p_feedforward, p_feedback (kw) |
0.3, 0.2 |
Inter-area connection probabilities. |
Returns: a multi-area Configuration with declared feedforward/feedback edges.
Legacy compatibility: ei_profile="flat" reproduces the pre-sweep behavior
byte-for-byte (depth-invariant composition, uniform3d placement). Only the new
opt-in paths (ei_profile="canonical" or an explicit layer_cell_type_fractions)
change placement to laminar.
The strict-notebook export grammar introduced in jaxfne==0.3.37 is exposed as
root-level callables (jaxfne.<name>) and, as of v0.3.38, is registered in
jaxfne.__all__ as formal public API (see the Export & figures group in the
symbol index below). These are the canonical replacement for direct
matplotlib/json calls in release-facing notebooks. matplotlib is imported
lazily inside the plotting/save functions, so importing jaxfne does not pull
in a plotting backend.
| Symbol |
Kind |
Summary |
save_figure |
func |
Save a matplotlib figure to disk. |
save_figures |
func |
Save multiple figures to an output directory. |
export_report |
func |
Export a complete report with JSON artifacts and figures. |
export_tutorial_artifacts |
func |
Export tutorial artifacts (JSON only, no figures). |
plot_raster |
func |
Plot a spike raster. |
plot_spectrolaminar_suite |
func |
Plot spectrolaminar suite from a signals object. |
All export helpers honor the truth gates: JSON is written with allow_nan=False,
and figure/readout outputs remain proxy diagnostics
(physical_amplitude_calibrated = False).
Complete public symbol index
func/class/const/module as resolved from jaxfne.__all__ (204 names, grouped by defining module). Summaries are the first docstring line; _(undocumented)_ marks public callables with no docstring in the released wheel.
Core (69)
| Symbol |
Kind |
Summary |
AxisSpec |
class |
Typed descriptor for one tensor axis in the TFNE scaffold. |
BasisSpec |
class |
Typed descriptor for the computation basis of a TFNE run. |
Config |
class |
Declarative TFNE model configuration. |
config_to_configuration |
func |
Map the network/emitter/field/probes sections to a Configuration. |
config_to_geometry |
func |
Map the geometry section to a LaminarSourceGeometry, or None. |
config_to_simulation |
func |
Map the run section of a JaxFNEConfig to a Simulation. |
config_to_trial_batch |
func |
Map the trials section and conditions to a TrialBatch. |
config_truth_boundary |
func |
Return a JSON-safe copy of the truth boundary section. |
Configuration |
class |
Declarative TFNE model configuration. |
configuration |
func |
— (undocumented) |
ConfigValidationResult |
class |
Report container for configuration validation. |
construct |
func |
— (undocumented) |
dataset_spec |
func |
Return a DatasetSpec schema declaration. |
DatasetSpec |
class |
Manifest-safe dataset/comparison declaration for observed data. |
default_basis_spec |
func |
Return the default BasisSpec matching the current laminar-proxy scaffold. |
enable_x64 |
func |
Enable JAX float64 mode before constructing arrays and report status. |
get_signal |
func |
Thin free-function accessor that delegates to Signals.get. |
JaxFNEConfig |
class |
JSON-safe container for a complete .jcfg.json TFNE specification. |
laminar_source_geometry |
func |
Build a LaminarSourceGeometry from an ordered population sequence. |
LaminarPopulation |
class |
Metadata descriptor for one named laminar cell population. |
LaminarSourceGeometry |
class |
Metadata descriptor for the full laminar source geometry. |
load_config |
func |
Load a .jcfg.json file and return a JaxFNEConfig. |
matrix_parameter |
func |
Create a matrix parameter specification for tuning weight matrices. |
MatrixParameterSpec |
class |
Declarative specification for a tunable weight matrix parameter. |
migrate_schema |
func |
Upgrade a legacy truth/metadata dict to the canonical truth-gate schema. |
Model |
class |
— (dataclass; fields in signature) |
Net |
class |
— (dataclass; fields in signature) |
Objective |
class |
Declarative objective specification: losses, regularizers, and diagnostic gates. |
objective |
func |
— (undocumented) |
ObjectiveReport |
class |
Structured, immutable result of evaluating an Objective against Signals. |
operator_status |
func |
Return the current operator status registry for all declared operators. |
Probe |
class |
— (dataclass; fields in signature) |
provenance_receipt |
func |
Capture release provenance atomically. |
rate_targets |
func |
Create a multi-group firing-rate objective. |
readout_spec |
func |
Build a ReadoutSpec for declarative feature extraction. |
ReadoutResult |
class |
Result of applying a ReadoutSpec to Signals. |
ReadoutSpec |
class |
Declarative specification for extracting a scalar feature from Signals. |
run_receipt |
func |
Build a RunReceipt for a completed simulation run. |
run_trials |
func |
Execute a batch of trials using the model. |
RunReceipt |
class |
Complete, JSON-safe record of a single simulation run. |
runtime |
func |
— (undocumented) |
runtime_report |
func |
— (undocumented) |
RuntimeConfig |
class |
JAX runtime and dtype policy. |
Signal |
class |
Simulation output container holding multiple arrays. |
Signals |
class |
Simulation output container holding multiple arrays. |
simulate |
func |
Run a simulation with the given model. |
Simulation |
class |
— (dataclass; fields in signature) |
simulation |
func |
— (undocumented) |
standard_visual_omission |
func |
Construct a Paradigm with standard visual oddball/omission task conditions. |
stimulus_schedule |
func |
Build a StimulusSchedule from a sequence of events. |
StimulusSchedule |
class |
Explicit drive schedule for event-aligned stimulus injection. |
suite2_celltype_presets |
func |
Return compact E/PV/SST/VIP reduced-emitter preset metadata. |
suite2_four_celltype_config |
func |
Build the Suite No. 2 four-emitter E/PV/SST/VIP configuration. |
suite2_net1_config |
func |
Build net1: a uniformly sampled 3D E/PV/SST/VIP column. |
suite2_run_bundle |
func |
Run simulation, readouts, manifest, and receipt for Suite No. 2 notebooks. |
suite2_simulation |
func |
Create a Suite No. 2 simulation with deterministic runtime metadata. |
suite2_single_neuron_config |
func |
Build the Suite No. 2 one-emitter configuration. |
suite2_tune_noise_agsdr_adam |
func |
Tune Poisson-drive amplitude toward a target mean firing-rate range. |
suite2_v1_v4_config |
func |
Build the Suite No. 2 V1-V4 laminar scaffold with six layers per area. |
surrogate_config |
func |
Return a SurrogateConfig declaration for an Optax gradient path. |
SurrogateConfig |
class |
Declared surrogate-gradient metadata for discontinuous emitter paths. |
trial_batch |
func |
Create a TrialBatch by repeating conditions. |
TrialBatch |
class |
A collection of trial specifications to be run. |
TrialBatchResult |
class |
Results from a batch of trials. |
TrialResult |
class |
Result of a single simulation trial. |
TrialSpec |
class |
Specification for a single simulation trial. |
TuneResult |
class |
Result object returned by Model.tune() with multi-parameter optimization. |
validate_config |
func |
Validate a JaxFNEConfig and return a ConfigValidationResult. |
with_emitter_parameters |
func |
Functional wrapper for Model.with_emitter_parameters. |
Emitters (19)
| Symbol |
Kind |
Summary |
EdgeList |
class |
Sparse recurrent connectivity as a JAX pytree. |
EIGNetwork |
class |
Lightweight description of an E/PV/SST/VIP-like reduced network. |
Emitter |
class |
Base class for package-level emitter facades. |
GLIFEmitter |
class |
Base class for package-level emitter facades. |
izhikevich_params_from_labels |
func |
Create reduced Izhikevich parameters from explicit cell labels. |
IzhikevichEmitter |
class |
Reduced Izhikevich emitter facade with a JAX step function. |
IzhikevichParams |
class |
Parameter container for a reduced Izhikevich population. |
LIFEmitter |
class |
Base class for package-level emitter facades. |
make_edge_list_from_dense |
func |
Convert a dense recurrent weight matrix into a sparse EdgeList. |
make_eig_network |
func |
Build a minimal EIG network with laminar depth positions. |
ReceptorSpec |
class |
Metadata declaration for a synaptic receptor. Not a biological kernel. |
simulate_edge_recurrent_izhikevich |
func |
Simulate reduced Izhikevich emitters with sparse recurrent synapses. |
simulate_eig_izhikevich |
func |
Simulate a reduced EIG Izhikevich scaffold using jax.lax.scan. |
simulate_receptor_exponential_izhikevich |
func |
v0.0.11 receptor-indexed exponential recurrent kernel. |
standard_receptor_specs |
func |
Provide standard declarative receptor metadata. No biological claim. |
standard_receptor_tau_table |
func |
Return the receptor_index → tau_ms lookup table used by v0.0.11. |
SynapseLayer |
class |
Exponential synapse layer returning recurrent input currents. |
SynapseSpec |
class |
Metadata declaration for a synapse. Not a biological kernel. |
SynapseState |
class |
— (no docstring) |
Fields (12)
| Symbol |
Kind |
Summary |
compute_conservation_proxy_diagnostics |
func |
Compute conservation-inspired proxy diagnostics over existing source/field arrays. |
construct_source_tensor |
func |
— (undocumented) |
eeg_proxy_transform |
func |
Compute EEG-proxy readout via linear leadfield projection. |
emm_proxy_transform |
func |
Compute EMM-proxy (normalized activity/source/field cost) readout. |
FieldOutput |
class |
Container for laminar proxy field/readout arrays. |
LinearReadout |
class |
— (dataclass; fields in signature) |
meg_proxy_transform |
func |
Compute MEG-proxy readout via linear leadfield projection. |
probe_laminar_modes |
func |
— (undocumented) |
project_laminar_sources |
func |
Project source traces to laminar proxy contacts. |
project_sources_to_laminar_field |
func |
— (undocumented) |
validate_projection_invariants |
func |
— (undocumented) |
validate_source_field_status |
func |
Return truth-preserving status for source-field readouts. |
Optimizers — optim (15)
| Symbol |
Kind |
Summary |
AGSDR |
class |
Legacy AGSDR adapter retained for old notebooks and tests. |
agsdr |
func |
Return an optimizer spec for AGSDR. |
agsdr_transform |
func |
Return an Optax-compatible GradientTransformation for Adaptive GSDR. |
AGSDROptimizerSpec |
class |
Multi-parameter AGSDR optimizer specification with execution parameters. |
AGSDRState |
class |
Adaptive Genetic Stochastic Delta Rule optimizer state. |
gsdr |
func |
Return an OptimizerSpec for the GSDR (Genetic Stochastic Delta Rule) optimizer. |
gsdr_transform |
func |
Return an Optax-compatible GradientTransformation for Genetic SDR. |
GSDRState |
class |
Genetic Stochastic Delta Rule optimizer state. |
optax_adam |
func |
Return an OptimizerSpec for Optax Adam. |
optax_sgd |
func |
Return an OptimizerSpec for Optax SGD. |
OptimizerSpec |
class |
Declarative optimizer specification with differentiability metadata. |
random_search |
func |
Return an OptimizerSpec for random search. |
require_optax |
func |
Import Optax lazily with an informative error. |
sdr_transform |
func |
Return an Optax-compatible GradientTransformation for Stochastic Delta Rule. |
SDRState |
class |
Stochastic Delta Rule optimizer state. |
IO & receipts (10)
| Symbol |
Kind |
Summary |
asset_hashes |
func |
Create a SHA256 hash manifest for assets. |
config_hash |
func |
Return a compact SHA256 hash for a configuration-like object. |
json_safe |
func |
Convert common scientific Python/JAX objects into strict JSON values. |
manifest |
func |
Build a strict JSON-safe run manifest. |
probe_report |
func |
Create a probe operator report JSON bundle. |
save_json |
func |
Save strict JSON with allow_nan=False. |
save_receipt |
func |
Save a RunReceipt as strict JSON. |
sha256_file |
func |
Return SHA256 for a file. |
sha256_text |
func |
Return SHA256 for a text payload. |
validation_report |
func |
Create a validation report JSON bundle. |
| Symbol |
Kind |
Summary |
export_report |
func |
Export a complete report with JSON artifacts and figures. |
export_tutorial_artifacts |
func |
Export tutorial artifacts (JSON only, no figures). |
plot_raster |
func |
Plot a spike raster. |
plot_spectrolaminar_suite |
func |
Plot spectrolaminar suite from signals object. |
save_figure |
func |
Save a matplotlib figure to disk. |
save_figures |
func |
Save multiple figures to an output directory. |
Bridges — Jaxley (7)
| Symbol |
Kind |
Summary |
BridgeSpec |
class |
JSON-safe optional-backend bridge declaration. |
hh_numpy_reference_trace |
func |
Standalone tutorial/reference Hodgkin-Huxley single-compartment trace. |
jaxley_trace_to_signals |
func |
Convert Jaxley-style voltage trace array to jaxfne Signals. |
JaxleyBridge |
class |
Jaxley-focused biophysical emitter bridge. |
JaxleyEmitterBridge |
class |
Jaxley bridge contract for reserved compartment emitters. |
JaxleyTraceSpec |
class |
Metadata specification for Jaxley-style voltage trace arrays. |
require_jaxley |
func |
Import Jaxley lazily with an informative error. |
Paradigms (6)
| Symbol |
Kind |
Summary |
coop_omission_oddball_paradigm |
func |
Create a Continuous Omission Oddball Paradigm (COOP) stimulus sequence. |
omission_oddball_paradigm |
func |
Create an omission/oddball detection paradigm. |
Paradigm |
class |
— (dataclass; fields in signature) |
paradigm |
module |
(constant; see source) |
ParadigmCondition |
class |
A specific trial condition: sequence of stimuli and associated events. |
ParadigmEvent |
class |
Discrete event within a task trial: stimulus, behavioral code, or omission marker. |
Solvers (7)
| Symbol |
Kind |
Summary |
DiffraxSolver |
class |
Optional Runge-Kutta solver using diffrax (lazily imported). |
euler_scan |
func |
Forward Euler integration scan (backward compatibility). |
euler_step |
func |
Single forward Euler step (backward compatibility). |
EulerSolver |
class |
Forward Euler integrator using JAX and lax.scan. |
solve_ode |
func |
Public ODE solver entrypoint routing to appropriate solver backend. |
solve_volume_conductor_experimental |
func |
Experimental volume conductor solver skeleton. |
SolverConfig |
class |
Configuration class for ODE solvers. |
Sanity-delta runtime (7)
| Symbol |
Kind |
Summary |
BackupState |
class |
Resumable task state with ring buffer history. |
BehaviorGate |
class |
Fixation gate: monitors PFC superficial activity. |
HierarchicalOddballParadigm |
class |
Task paradigm: AAAB oddball sequence with timing and gating. |
Manifest |
class |
Output manifest: configuration, paradigm, backup, validation. |
SanityDeltaConfig |
class |
Hierarchical oddball configuration factory and validation. |
SanityDeltaModel |
class |
Wrapper around constructed hierarchical oddball model. |
TaskEpisode |
class |
Result of a task episode with probing, export, validation. |
Plasticity (5)
| Symbol |
Kind |
Summary |
plot_stdp_adaptation_suite |
func |
Generates and saves the standard STDP adaptation visualization figures. |
STDPPlasticityConfig |
class |
Configuration class for STDP activity-dependent plasticity. |
STDPState |
class |
Container for the state variables of the STDP synapse model. |
summarize_stdp_adaptation |
func |
Computes synapse-by-synapse adaptation statistics. |
update_stdp_weights_jax |
func |
JAX-optimized plasticity weight update kernel (STDP). |
Tutorial utils (4)
| Symbol |
Kind |
Summary |
build_tutorial_laminar_column |
func |
Build a laminar column scaffold model. |
kappa_synchrony |
func |
Compute spike synchrony measure (kappa statistic) across neurons. |
rate_synchrony_targets |
func |
Create an objective specification for AGSDR tuning toward rate and synchrony targets. |
select_neurons |
func |
Select neuron indices matching given criteria (area, layer, cell_type). |
Sharding (4)
| Symbol |
Kind |
Summary |
get_sharding_context |
func |
Return a dict with mesh, candidate, and replicated sharding specs. |
make_candidate_sharding |
func |
Return a jax.sharding.NamedSharding that slices the first |
make_population_mesh |
func |
Return a 1-D named jax.sharding.Mesh across all visible JAX devices. |
make_replicated_sharding |
func |
Return a jax.sharding.NamedSharding that fully replicates an array |
Connectivity (3)
| Symbol |
Kind |
Summary |
compile_connection_rules |
func |
Compile declared connection rules into sparse finite edge arrays. |
compile_connection_rules_jax |
func |
Tensorized JAX connectivity compiler producing static-shape edge outputs. |
ConnectionCompileResult |
class |
Compiled sparse connectivity. |
Geometry (1)
| Symbol |
Kind |
Summary |
make_ei_cloud_network |
func |
Generates geometry and initial weights for a 100-neuron E-I cloud network. |
Builders (10)
| Symbol |
Kind |
Summary |
laminar_cortex_config |
func |
Generalized multi-area laminar cortical configuration builder. |
build_laminar_column |
func |
Single-column builder; defaults name="V1", n=1000; ei_profile/geometry select flat-legacy vs canonical laminar prior. |
build_multi_area_columns |
func |
Multi-area builder; defaults to the V1→V4→PFC hierarchy, 200/area, with inter-area feedforward/feedback. |
CANONICAL_LAYER_CELL_TYPE_FRACTIONS |
const |
Ground-truth per-layer E:I composition (6-layer); E peaks deep, I peaks superficial. |
CANONICAL_LAYER_CELL_TYPE_FRACTIONS_5L |
const |
5-layer (L2/3 merged) variant of the canonical composition. |
CANONICAL_Z_BANDS |
const |
Count-proportional depth bands for the canonical 6-layer column. |
CANONICAL_Z_BANDS_5L |
const |
Count-proportional depth bands for the 5-layer column. |
CANONICAL_LAYERS_6L |
const |
Canonical 6-layer name tuple ("L1".."L6"). |
FLAT_CELL_TYPE_FRACTIONS |
const |
Legacy depth-invariant E/PV/SST/VIP fractions. |
DEFAULT_LAYERS |
const |
Historical 5-layer default ("L1","L2/3","L4","L5","L6"). |
Streaming (1)
| Symbol |
Kind |
Summary |
run_stdp_stream |
func |
Runs simulation in a chunked, streaming fashion to avoid memory explosion. |
Stimulus (1)
| Symbol |
Kind |
Summary |
triangular_drive |
func |
Generates a triangular drive trace. |
JAX Spectral Analysis (6)
| Symbol |
Kind |
Summary |
spectrolaminar_psd_jax |
func |
Compute spectrolaminar PSD averaged across trials using JAX. |
bandpower_jax |
func |
Compute average power within a frequency band normalized by channel max. |
spectrolaminar_readout_kernel_jax |
func |
Batchable readout kernel computing relative power and normalized band profiles. |
spectrolaminar_similarity_kernel_jax |
func |
Compute the profile similarity score in JAX. |
spectrolaminar_similarity_candidates_jax |
func |
Batched vectorization path for similarity scoring. |
spectrolaminar_similarity_candidates_seeds_jax |
func |
Nested batched vectorization path for seeds and candidates. |
Validation registry (2)
| Symbol |
Kind |
Summary |
compilation_registry |
const |
Automated JAX tracing and compilation tracking registry. |
is_valid_signal |
func |
Check if signal arrays contain only finite values (no NaN/Inf). |
PyNWB compatibility (2)
| Symbol |
Kind |
Summary |
read_nwb |
func |
Placeholder for NWB read (reserved status). |
write_nwb |
func |
Placeholder for NWB write (reserved status). |
Experimental HPC (2)
| Symbol |
Kind |
Summary |
NodeIdentity |
class |
Stable node identity for selector-addressable circuits. |
SelectorSpec |
class |
Selector over area/layer/cell-type/id fields. |
Submodules (1)
| Symbol |
Kind |
Summary |
vis |
module |
Visualization package for jaxfne. |
Constants (4)
| Symbol |
Kind |
Summary |
_KNOWN_METRICS |
const |
⚠ private name leaking into __all__ — see docs audit (remove). |
CELL_TYPE_PRESETS |
const |
Mapping of cell-type label → preset Izhikevich parameters. |
DEFAULT_SPIKE_IMPULSE_GAIN |
const |
Default spike-impulse gain for the source proxy. |
RECEPTOR_KINETICS |
const |
Mapping of receptor name → kinetic time constants. |
See the docs audit & restructure notes (internal_docs/docs_audit_v0330.md)
for orphaned pages, duplicate cleanup, and the per-module table migration.