- navis.connectivity_similarity(adjacency, metric='vertex_normalized', threshold=None, n_cores=1, **kwargs)¶
Calculate connectivity similarity.
This functions offers a selection of metrics to compare connectivity:
Cosine similarity (see here)
Normalized difference in rank of synaptic partners.
Number of shared partners divided by total number of partners.
Number of shared synapses (i.e. number of connections from/onto the same partners) divided by total number of synapses. Attention: this metric is tricky when there is a disparity of total number of connections between neuron A and B. For example, consider 100/200 and 1/50 shared/total synapse: 101/250 results in a fairly high matching index of 0.404.
Similar to matching_index_synapses but slightly less prone to above mentioned error as it uses the percentage of shared synapses:
Matching index that rewards shared and punishes non-shared partners. Based on Jarrell et al., 2012:
Final score is the sum of over all edges x, y between neurons A+B and their partners. C1 determines how negatively a case where one edge is much stronger than another is punished. C2 determines the point where the similarity switches from negative to positive. C1 and C2 default to 0.5 and 1, respectively, but can be changed by passing them in a dictionary as
This is vertex similarity normalized by the lowest (hypothetical total dissimilarity) and highest (all edge weights the same) achievable score.
adjacency (pandas DataFrame | numpy array) – (N, M) observation vector with M observations for N neurons - e.g. an adjacency matrix. Will calculate similarity for all rows using the columns as observations.
metric ('cosine' | 'rank_index'| 'matching_index' | 'matching_index_synapses' | 'matching_index_weighted_synapses' | 'vertex' | 'vertex_normalized') – Metric used to compare connectivity. See notes for detailed explanation.
threshold (int, optional) – Connections weaker than this will be set to zero.
n_cores (int) – Number of parallel processes to use. Defaults to half the available cores.
Pandas DataFrame with similarity scores. Neurons without any connectivity will show up with
- Return type: