Method and apparatus for constructing a neuroscience-inspired artificial neural network with visualization of neural pathways

Apparatus for detecting a change in an input stream via sensing events to a first spiking neural network for one of a control, detection and classification application, the apparatus comprising a special purpose processor and an associated memory, the special purpose processor for executing a special purpose stored program for the one of the control, detection and classification applications, the apparatus further comprising:

• the first spiking neural network constructed in one of the memory and a dynamic artificial neural network array, the first spiking neural network comprising at least one input neuron and at least one output neuron, each neuron having a threshold parameter;
• at least one synapse connecting a pair of neurons of the first spiking neural network, the synapse having a parameter that specifies a propagation delay through the synapse, the synapse permitting at least two discrete events to propagate together through the synapse connecting one neuron to another neuron,
• the first spiking neural network being connected to at least one affective system, the affective system altering the behavior of the first spiking neural network;
• the special purpose processor executing the special purpose stored program for simulating the first spiking neural network and connected at least one affective system to produce at least one output event at the at least one output neuron in response to the input stream of sensed events;
• and
• the special purpose processor for executing evolutionary optimization of the first spiking neural network comprising a crossover operation, the crossover operation comprising a selection of first and second parent neural networks, selection of two neurons from one of the first and the second selected parent neural networks, choosing a plane using locations of the two selected neurons and using a signed distance from the chose plane to a third neuron from the at least one selected parent network to construct a new spiking neural network having a different structure of neurons than the first spiking neural network.

A method of designing a best performing network of a population of neuromorphic networks, performance measured for each neuromorphic network of the population of neuromorphic networks on a process control application using a performance metric of the process control application,

• each neuromorphic network comprising at least a neuron and a synapse,
• each neuromorphic network coupled to at least one affective system, wherein the at least one affective system is designed to alter the behavior of a spiking neural network of the population of neuromorphic networks,
• each neuron having a parameter and able to produce events by firing,
• each synapse having a programmable delay or distance parameter value,
• the method of designing the best performing neuromorphic network comprising:
• using performance measured for each neuromorphic network of the population of neuromorphic networks to select first and second neuromorphic networks from the population of neuromorphic networks according to a performance metric of the process control application,
• choosing two neurons of the first network,
• applying crossover using the chosen two neurons and the selected first and second neuromorphic networks to produce two new neuromorphic networks,
• choosing one of a neuron and a synapse in one of the two new networks and modifying a parameter of the chosen one of a neuron and a synapse, and
• repeating these steps to create a new population of neuromorphic networks, measuring performance of the new population of neuromorphic networks according to the performance metric of the process control application,
• the best performing network of the new population of neuromorphic networks being coupled to at least one affective system and having an improved performance of the process control application according to its performance metric value.