Connectome analysis¶
This package provides connectome analysis routines.
-
class
connectome.analysis.connectivity.
ConnectivityEstimator
(**kwargs)¶ Inputs: network, measures
Estimate the connectivity of a network.
Output Keys: p_ee, p_ii, p_ie, p_ei
-
class
connectome.analysis.inoutdegreecorrelation.
InOutDegreeCorrelation
(**kwargs)¶ Inputs: network, measures
Estimates the correlation of in and out degrees of the excitatory subpopulation.
Output keys: in_out_degree_correlation_exc
-
class
connectome.analysis.relativecycleanalysis.
RelativeCycleAnalysis
(**kwargs)¶ Inputs: length, network, measures
Compares relative to ER the expected number of cycles which is approximated here by \((p*n)^{length}\), where
p
is the connectivity,n
the number of excitatory nodes andlength
the cycle length.Note that this approximation ignores the non-independent nature of the Bernoulli product on the diagonal of the matrix power. This approximation fails for very small and very sparse networks but is precise for large and dense networks.
Configuration parameter: length, integer
Output keys: relative_cycles_<length>
-
class
connectome.analysis.reciprocity.
ReciprocityEstimator
(**kwargs)¶ Inputs: network, measures
- Estimate the network’s reciprocities; here “reciprocity_ei” means:
Given a connection from E -> I, what is the probability of the reciprocated connection to also exist?
Output keys: reciprocity_ee, reciprocity_ii, reciprocity_ei, reciprocity_ie
-
class
connectome.analysis.relativereciprocity.
RelativeReciprocityEstimator
(**kwargs)¶ Inputs: network, measures
Estimate the reciprocity relative to an ER network of the same connectivity. See also
ReciprocityEstimator
.Output keys: relative_reciprocity_ee, relative_reciprocity_ei, relative_reciprocity_ie, relative_reciprocity_ii