entity_management.circuit.building.functional¶

Entities for s2f recipe generation.

Classes

`EstimateBoutonReduction`(bio_data[, sample])	Estimate an overall reduction factor based on an estimated mean bouton density over all mtypes.
`EstimateIndividualBoutonReduction`(bio_data)	Estimate a reduction factor for each individual mtype, where experimental data is available.
`EstimateSynsCon`(formula[, formula_ee, ...])	Estimate the functional mean number of synapses per connection from the structural number of appositions per connection.
`ExperimentalSynsCon`(bio_data)	Use the biological mean number of synapses per connection for a number of pathways where experimental data is available.
`GeneralizedCv`(cv)	Set cv_syns_connection value for all pathways.
`Recipe`([strategies])	Synapse pruning functionalizer recipe.
`Sample`([size, target, mask, ...])	Parameters for sampling bouton density.
`Strategy`()	Base strategy class.

class entity_management.circuit.building.functional.EstimateBoutonReduction(bio_data, sample=None)¶

Bases: Strategy

Estimate an overall reduction factor based on an estimated mean bouton density over all mtypes.

bio_data¶: Path to bouton-density dataset representing reference biological data (OR single float value)

sample¶: Parameters for sampling bouton density OR path to bouton-density dataset already sampled

class entity_management.circuit.building.functional.EstimateIndividualBoutonReduction(bio_data, sample=None)¶

Bases: EstimateBoutonReduction

Estimate a reduction factor for each individual mtype, where experimental data is available.

class entity_management.circuit.building.functional.EstimateSynsCon(formula, formula_ee=None, formula_ei=None, formula_ie=None, formula_ii=None, max_value=None, sample=None)¶

Bases: Strategy

Estimate the functional mean number of synapses per connection from the structural number of appositions per connection. For the prediction, an algebraic expression using ‘n’ (mean number of appositions) should be specified.

formula¶: Synapse number prediction formula.

formula_ee¶: Synapse number prediction formula for EXC->EXC pathways. If omitted, general formula would be used

formula_ei¶: Synapse number prediction formula for EXC->INH pathways. If omitted, general formula would be used

formula_ie¶: Synapse number prediction formula for INH->EXC pathways. If omitted, general formula would be used

formula_ii¶: Synapse number prediction formula for INH->INH pathways. If omitted, general formula would be used

max_value¶: Max value for predicted synapse number. If omitted, the predicted synapse number is not clipped above NB: predicted synapse value would be always min-clipped to 1.0 to avoid invalid synapse count values.

sample¶: Parameters for sampling bouton density OR path to bouton-density dataset already sampled

class entity_management.circuit.building.functional.ExperimentalSynsCon(bio_data)¶

Bases: Strategy

Use the biological mean number of synapses per connection for a number of pathways where experimental data is available.

bio_data¶: Path to nsyn-per-connection dataset representing reference biological data

class entity_management.circuit.building.functional.GeneralizedCv(cv)¶

Bases: Strategy

Set cv_syns_connection value for all pathways.

cv¶: cv_syns_connection value to use

class entity_management.circuit.building.functional.Recipe(strategies=[])¶

Bases: object

Synapse pruning functionalizer recipe.

asdict()¶: Recipe dictionary representation.

strategies¶

class entity_management.circuit.building.functional.Sample(size=100, target=None, mask=None, assume_nsyn_bouton=1.0, assume_syns_bouton=1.0)¶

Bases: object

Parameters for sampling bouton density.

assume_nsyn_bouton¶: FIMXE

assume_syns_bouton¶: Assumed synapse count per bouton

mask¶: Region of interest. If provided, only axonal segments within this region would be considered.

size¶: Sample size

target¶: Sample target

class entity_management.circuit.building.functional.Strategy¶

Bases: object

Base strategy class.