Options for high-precision connections

[stuarteberg]

Neuron-neuron connections in neuprint have a weight attribute and a weightHP attribute for "high precision" synapses. Functions in neuprint-python that use edge weights should include an option to use the latter attribute if the user wants to use only high-precision connections.

• fetch_simple_connections
• fetch_shortest_paths
• etc.

[unidesigner]

What is the difference between a weight and weightHP synapse?

[stuarteberg]

What is the difference between a weight and weightHP synapse?

Each synapse has an associated "confidence" indicating roughly how likely the synapse is real (vs. a false prediction). These can be inspected in the output of fetch_synapses() and fetch_synapse_connections().

But that's only helpful if you're inspecting individual synapse properties. The overall neuron-to-neuron connection strength is precomputed and stored in the :ConnectsTo edge relationship, as the weight attribute. It counts all synapses. If you're interested in a higher precision (but lower recall) version of the edge strength, you can use weightHP. It's also precomputed, but only counts synapses which exceed a minimum confidence score.

In general, the "high precision" threshold could be different from one dataset to the next. In the hemibrain, I believe it is 0.7.

[unidesigner]

Thanks for the explanation.