System, network and method for selective activation of a computing network involving super-imposable stochastic graphs

A method of operating a stochastic neural network, comprising:

• providing a stochastic neural network comprising a plurality of nodes, a plurality of synapses and a plurality of processing elements, wherein each of the plurality of synapses comprises a connection between a respective pair of the plurality of nodes, and wherein each of the plurality of processing elements is embedded in a respective synapse of the plurality of synapses, wherein each of the plurality of processing elements is adapted to receive an input and generate an output based on the input, wherein the output of a first synapse of the one or more of the plurality of synapses feeds into a first subset of the plurality of nodes and comprises a first synaptic value computed according to first output of a first node, wherein the output of a second synapse of the one or more of the plurality of synapses comprises a second synaptic value computed according to a second output of the first node, wherein the second output of the first node is distinct from the first output of the first node, wherein the first subset of the plurality of nodes comprises a first subset of activated nodes, wherein, upon receipt of a first problem input, at least a portion of the stochastic neural network is selectively activated by one or more of the plurality of processing elements embedded in one or more of the plurality of synapses;
• and
• training the stochastic neural network.

A computing system, comprising:

• a stochastic neural network comprising:
• a plurality of nodes;
• a plurality of synapses, wherein each synapse of the plurality of synapses connects a respective pair of the plurality of nodes, wherein each synapse of the plurality of synapses comprises an activation weight, wherein each of the plurality of synapses is operable to control activation of at least one of the plurality of nodes, and wherein a subset of the plurality of synapses shares a first activation weight, wherein the output of a first synapse of the plurality of synapses feeds into a first subset of the plurality of nodes and comprises a first synaptic value computed according to a-first output of a first node, wherein the output of a second synapse of the plurality of synapses comprises a second synaptic value computed according to a second output of the first node, and wherein the second output of the first node is distinct from the first output of the first node;
• and
• a plurality of processing elements, wherein each of the plurality of processing elements is embedded in a respective synapse of the plurality of synapses, wherein, upon receipt of a first problem input, at least a first portion of the stochastic neural network is selectively activated by one or more of the plurality of processing elements embedded in one or more of the plurality of synapses.