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 an output for the training input;
• processing the student neural network output using a discriminative neural network to generate a discriminative score for the student neural network output,
• wherein the discriminative neural network is trained to process a network input to generate a discriminative score that characterizes a prediction for whether the network input was generated using:
• (i) the student neural network, or (ii) a brain emulation neural network having a plurality of brain emulation neural network parameters, 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 depends on the discriminative score for the student neural 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 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 an output for the training input;
• processing the student neural network output using a discriminative neural network to generate a discriminative score for the student neural network output,
• wherein the discriminative neural network is trained to process a network input to generate a discriminative score that characterizes a prediction for whether the network input was generated using:
• (i) the student neural network, or (ii) a brain emulation neural network having a plurality of brain emulation neural network parameters, 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 depends on the discriminative score for the student neural network output.