Selecting neural network architectures based on community graphs

A system comprising:

• one or more computers;
• and
• one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising:
• obtaining data defining a connectivity graph that represents synaptic connectivity between a plurality of biological neuronal elements in a brain of a biological organism, wherein the connectivity graph comprises:
• (i) a plurality of nodes, and (ii) a plurality of edges that each connect a respective pair of nodes;
• determining a partition of the connectivity graph into a plurality of community sub-graphs by performing an optimization that encourages a higher measure of connectedness between nodes included within each community sub-graph relative to nodes included in different community sub-graphs;
• and
• selecting a neural network architecture for performing a machine learning task using the plurality of community sub-graphs determined by the optimization that encourages the higher measure of connectedness between nodes included within each community sub-graph relative to nodes included in different community sub-graphs, comprising:
• instantiating a plurality of candidate neural network architectures, wherein each candidate neural network architecture includes one or more brain emulation sub-networks that each have a respective architecture specified by a respective community sub-graph of the plurality of community sub-graphs;
• determining a respective performance measure of each of the plurality of candidate neural network architectures on the machine learning task;
• and
• selecting the neural network architecture for performing the machine learning task based on the performance measures of the plurality of candidate neural network architectures.

One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:

• obtaining data defining a connectivity graph that represents synaptic connectivity between a plurality of biological neuronal elements in a brain of a biological organism, wherein the connectivity graph comprises:
• (i) a plurality of nodes, and (ii) a plurality of edges that each connect a respective pair of nodes;
• determining a partition of the connectivity graph into a plurality of community sub-graphs by performing an optimization that encourages a higher measure of connectedness between nodes included within each community sub-graph relative to nodes included in different community sub-graphs;
• and
• selecting a neural network architecture for performing a machine learning task using the plurality of community sub-graphs determined by the optimization that encourages the higher measure of connectedness between nodes included within each community sub-graph relative to nodes included in different community sub-graphs, comprising:
• instantiating a plurality of candidate neural network architectures, wherein each candidate neural network architecture includes one or more brain emulation sub-networks that each have a respective architecture specified by a respective community sub-graph of the plurality of community sub-graphs;
• determining a respective performance measure of each of the plurality of candidate neural network architectures on the machine learning task;
• and
• selecting the neural network architecture for performing the machine learning task based on the performance measures of the plurality of candidate neural network architectures.