Neural architecture search based on synaptic connectivity 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 synaptic connectivity graph representing synaptic connectivity between neurons in a brain of a biological organism, wherein:
• the synaptic connectivity graph comprises a plurality of nodes and edges, wherein each edge connects a pair of nodes, each node corresponds to a respective neuron in the brain of the biological organism, and each edge connecting a pair of nodes in the synaptic connectivity graph corresponds to a synaptic connection between a pair of neurons in the brain of the biological organism;
• generating data defining a plurality of candidate graphs based on the synaptic connectivity graph;
• determining, for each candidate graph, a performance measure characterizing a performance of a neural network having a neural network architecture that is specified by the candidate graph in accomplishing a machine learning task;
• and
• selecting a final neural network architecture for performing the machine learning task based on the performance measures.

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 synaptic connectivity graph representing synaptic connectivity between neurons in a brain of a biological organism, wherein:
• the synaptic connectivity graph comprises a plurality of nodes and edges, wherein each edge connects a pair of nodes, each node corresponds to a respective neuron in the brain of the biological organism, and each edge connecting a pair of nodes in the synaptic connectivity graph corresponds to a synaptic connection between a pair of neurons in the brain of the biological organism;
• generating data defining a plurality of candidate graphs based on the synaptic connectivity graph;
• determining, for each candidate graph, a performance measure characterizing a performance of a neural network having a neural network architecture that is specified by the candidate graph in accomplishing a machine learning task;
• and
• selecting a final neural network architecture for performing the machine learning task based on the performance measures.