Reservoir computing neural networks based on synaptic connectivity graphs

A method performed by one or more data processing apparatus, the method comprising:

• processing a network input using a reservoir computing neural network to generate a network output, wherein the reservoir computing neural network comprises:
• (i) a brain emulation sub-network, and (ii) a prediction sub-network, wherein:
• the brain emulation sub-network is configured to process the network input in accordance with values of a plurality of brain emulation sub-network parameters to generate an alternative representation of the network input;
• the prediction sub-network is configured to process the alternative representation of the network input in accordance with values of a plurality of prediction sub-network parameters to generate the network output;
• the values of the brain emulation sub-network parameters are determined before the reservoir computing neural network is trained and are not adjusted during training of the reservoir computing neural network;
• the values of the prediction sub-network parameters are adjusted during training of the reservoir computing neural network;
• the brain emulation sub-network has a neural network architecture that is specified by a graph representing synaptic connectivity between neurons in a brain of a biological organism, wherein:
• 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.

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 implement:

• a reservoir computing neural network configured to receive a network input and to generate a network output from the network input, the reservoir computing neural network comprising:
• (i) a brain emulation sub-network, and (ii) a prediction sub-network, wherein:
• the brain emulation sub-network is configured to process the network input in accordance with values of a plurality of brain emulation sub-network parameters to generate an alternative representation of the network input;
• the prediction sub-network is configured to process the alternative representation of the network input in accordance with values of a plurality of prediction sub-network parameters to generate the network output;
• the values of the brain emulation sub-network parameters are determined before the reservoir computing neural network is trained and are not adjusted during training of the reservoir computing neural network;
• the values of the prediction sub-network parameters are adjusted during training of the reservoir computing neural network;
• the brain emulation sub-network has a neural network architecture that is specified by a graph representing synaptic connectivity between neurons in a brain of a biological organism, wherein:
• 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.