A reconfigurable neural circuit includes a two dimensional array including a plurality of processing nodes, wherein each processing node includes a neuron circuit, a synapse circuit, a spike timing dependent plasticity (STDP) circuit, a weight memory for storing synaptic weights, the weight memory coupled to the synapse circuit, an interconnect fabric for interconnections to and from and between the neuron circuit, the synapse circuit, the STDP circuit, the weight memory, and between a respective node in the array and other processing nodes in the array, and a connectivity memory for storing interconnect routing controls coupled to the interconnect fabric.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A reconfigurable neural circuit comprising: a two dimensional array comprising a plurality of processing nodes; wherein each processing node comprises: a neuron circuit; a synapse circuit; a spike timing dependent plasticity (STDP) circuit; a weight memory for storing synaptic weights, the weight memory coupled to the synapse circuit; an interconnect fabric for interconnections to and from and between the neuron circuit, the synapse circuit, the STDP circuit, the weight memory, and between a respective node in the array and other processing nodes in the array; and a connectivity memory for storing interconnect routing controls coupled to the interconnect fabric. 2. The reconfigurable neural circuit of claim 1 wherein each processing node comprises: a time multiplexed synapse circuit. 3. The reconfigurable neural circuit of claim 1 wherein: an output of the synapse circuit is coupled to an input of the neuron circuit; an input to the synapse circuit is coupled to the STDP circuit; an output of the neuron circuit is coupled to the STDP circuit; an output of the STDP circuit is coupled to the weight memory. 4. The reconfigurable neural circuit of claim 1 wherein: the neuron circuit comprises an integrate and fire circuit. 5. The reconfigurable neural circuit of claim 1 wherein: the weight memory comprises a memristor memory, flip flops, or a static random access memory. 6. The reconfigurable neural circuit of claim 1 wherein: the connectivity memory comprises flip flops or a static random access memory. 7. The reconfigurable neural circuit of claim 1 wherein: the interconnect fabric comprises a plurality of switches for changing the interconnections to and from the neuron circuit, the synapse circuit, the STDP circuit, the weight memory, and other processing nodes in the array. 8. The reconfigurable neural circuit of claim 7 wherein: the plurality of switches comprise a plurality of uni-directional and bi-directional switches. 9. The reconfigurable neural circuit of claim 1 wherein: the weight memory stores N synaptic conductance values or weights for N virtual synapse circuits. 10. The reconfigurable neural circuit of claim 9 wherein: the connectivity memory stores interconnect routing controls for N time periods; wherein the interconnect fabric is reconfigurable for each of the N time periods. 11. The reconfigurable neural circuit of claim 10 wherein: one of the N synaptic conductance values or weights is read from the weight memory for each of the N time periods and coupled to the synapse circuit. 12. The reconfigurable neural circuit of claim 11 wherein: an output of the STDP circuit is coupled to the weight memory; and a synaptic conductance value or weight read from the weight memory during a respective time period of the N time periods is updated or changed in the weight memory by writing the weight memory in the respective time period according to the output of the STDP circuit. 13. The reconfigurable neural circuit of claim 1 wherein: the STDP element comprises a biologically inspired spike timing dependent plasticity (STDP) learning rule. 14. A method of providing a reconfigurable neural network comprising: forming a two dimensional array of plurality of processing nodes, wherein each processing node comprises: a synapse; a neuron coupled to the synapse; and a spike timing dependent plasticity (STDP) element; storing N synaptic weights for each processing node; accessing a synaptic weight for each processing node during each of N time periods and forming a virtual synapse within each processing node during each of the N time periods using the synapse and a respective accessed synaptic weight; and controlling connections to and from and between the neuron, the synapse, and the STDP element in a processing node, and connections between each respective processing node and other processing nodes in the array. 15. The method of claim 14 further comprising: time multiplexing the synapse to form N virtual synapses. 16. The method of claim 14 wherein within each processing node: an output of the synapse is coupled to an input of the neuron; an input to the synapse is coupled to the STDP element; an output of the neuron is coupled to the STDP element; an output of the STDP element is coupled to the weight memory. 17. The method of claim 14 wherein: the neuron comprises an integrate and fire circuit. 18. The method of claim 14 wherein controlling connections to and from and between the neuron, the synapse, and the STDP element in a processing node, and connections between each respective processing node and other processing nodes in the array comprises: controlling a plurality of switches. 19. The method of claim 14 further comprising: storing controls for N time periods for controlling connections to and from and between the neuron, the synapse, and the STDP element in a processing node, and connections between each respective processing node and other processing nodes in the array. 20. The method of claim 14 further comprising: updating or changing a synaptic weight read from the weight memory during a respective time period of the N time periods by writing the weight memory in the respective time period according to an output of the STDP element. 21. The method of claim 20 wherein: the STDP element updates or changes the synaptic weight according to a biologically inspired spike timing dependent plasticity (STDP) learning rule. 22. The method of claim 14 wherein storing N synaptic weights for each processing node comprises: storing the N synaptic weight in each processing node using a memristor memory, flip flops, or a static random access memory.