Training artificial neural networks 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 for training a student neural network having a plurality of student neural network parameters, comprising repeatedly performing operations including:
• processing a training input using the student neural network to generate a student neural network output comprising a respective score for each of a plurality of classes;
• processing the training input using a brain emulation neural network having a plurality of brain emulation neural network parameters to generate a brain emulation neural network output comprising a respective score for each of the plurality of classes, wherein:
• the brain emulation neural network has a neural network architecture that is specified by a graph representing synaptic connectivity between neurons in a brain of a biological organism;
• and
• the 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 graph corresponds to a synaptic connection between a pair of neurons in the brain of the biological organism;
• and
• adjusting current values of the student neural network parameters using gradients of an objective function that characterizes a similarity between:
• (i) the student neural network output for the training input, and (ii) the brain emulation neural network output for the training input.

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 for training a student neural network having a plurality of student neural network parameters, comprising repeatedly performing operations including:

• processing a training input using the student neural network to generate a student neural network output comprising a respective score for each of a plurality of classes;
• processing the training input using a brain emulation neural network having a plurality of brain emulation neural network parameters to generate a brain emulation neural network output comprising a respective score for each of the plurality of classes, wherein:
• the brain emulation neural network has a neural network architecture that is specified by a graph representing synaptic connectivity between neurons in a brain of a biological organism;
• and
• the 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 graph corresponds to a synaptic connection between a pair of neurons in the brain of the biological organism;
• and
• adjusting current values of the student neural network parameters using gradients of an objective function that characterizes a similarity between:
• (i) the student neural network output for the training input, and (ii) the brain emulation neural network output for the training input.