Artificial neural network architectures 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 a synaptic resolution image of at least a portion of a brain of a biological organism;
• processing the image to identify:
• (i) a plurality of neurons in the brain, and (ii) a plurality of synaptic connections between pairs of neurons in the brain;
• generating data defining a graph representing synaptic connectivity between the neurons in the brain, wherein the graph comprises a plurality of nodes and edges, wherein each edge connects a pair of nodes, comprising:
• mapping each neuron in the brain to a respective node in the graph;
• and
• mapping each synaptic connection between a pair of neurons in the brain to an edge between a corresponding pair of nodes in the graph;
• determining an artificial neural network architecture corresponding to the graph representing the synaptic connectivity between the neurons in the brain;
• and
• processing a network input using an artificial neural network having the artificial neural network architecture to generate a network output.

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 a synaptic resolution image of at least a portion of a brain of a biological organism;
• processing the image to identify:
• (i) a plurality of neurons in the brain, and (ii) a plurality of synaptic connections between pairs of neurons in the brain;
• generating data defining a graph representing synaptic connectivity between the neurons in the brain, wherein the graph comprises a plurality of nodes and edges, wherein each edge connects a pair of nodes, comprising:
• mapping each neuron in the brain to a respective node in the graph;
• and
• mapping each synaptic connection between a pair of neurons in the brain to an edge between a corresponding pair of nodes in the graph;
• determining an artificial neural network architecture corresponding to the graph representing the synaptic connectivity between the neurons in the brain;
• and
• processing a network input using an artificial neural network having the artificial neural network architecture to generate a network output.