A method for communicating a non-binary value in a spiking neural network includes encoding, with an encoder, a non-binary value as one or more spikes of at least one pre-synaptic neuron in a temporal frame. The method also includes computing a value with a decoder matched to the encoder. The value is computed by at least one post-synaptic neuron. The value is based on at least one synaptic weight and on the encoded spikes received from the pre-synaptic neuron.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for communicating a non-binary value in a spiking neural network, comprising: encoding, with an encoder, a non-binary value as one or more spikes of at least one pre-synaptic neuron in a temporal frame; and computing a value with a decoder matched to the encoder, the value computed by at least one post-synaptic neuron, the value based at least in part on at least one synaptic weight and on the encoded spikes received from the at least one pre-synaptic neuron. 2. The method of claim 1, in which the at least one synaptic weight is determined based at least in part on spike timing dependent plasticity (STDP). 3. The method of claim 1, in which the at least one synaptic weight is based at least in part on a perceptron learning rule. 4. The method of claim 1, in which encoding the non-binary value comprises expanding the non-binary value with a code. 5. The method of claim 4, in which the code is at least one of a logarithmic temporal code and a base expansive code. 6. The method of claim 1, further comprising computing a function based at least in part on the value computed at the post-synaptic neuron. 7. The method of claim 6, in which the function is a non-linear activation function. 8. The method of claim 1, further comprising decoding the value. 9. The method of claim 1, further comprising: encoding, with a second encoder, a second non-binary value as one or more spikes of a second pre-synaptic neuron in the temporal frame; and computing, by the post-synaptic neuron, a weighted sum of the value and the second non-binary value based at least in part on a summation of the received encoded spikes, as well as a second synaptic weight associated with a synapse between the second pre-synaptic neuron and the post-synaptic neuron. 10. The method of claim 9, further comprising computing a non-linear function based at least in part on the value computed at the post-synaptic neuron. 11. The method of claim 1, further comprising receiving a spike from an orchestrator neuron to define the temporal frame. 12. The method of claim 1, in which the spiking neural network implements an artificial neural network. 13. The method of claim 1, further comprising training the at least one synaptic weight using spike timing dependent plasticity. 14. The method of claim 1, further comprising training the at least one synaptic weight using a perceptron learning rule. 15. The method of claim 1, in which the non-binary value is at least a part of a non-linear function. 16. An apparatus for communicating a non-binary value in a spiking neural network, comprising: means for encoding a non-binary value as one or more spikes of at least one pre-synaptic neuron in a temporal frame; and means for computing a value, the value computed by at least one post-synaptic neuron, the value based at least in part on at least one synaptic weight and on the encoded spikes received from the at least one pre-synaptic neuron. 17. A computer program product for communicating a non-binary value in a spiking neural network, comprising: a non-transitory computer readable medium having encoded thereon program code, the program code comprising: program code to encode a non-binary value as one or more spikes of at least one pre-synaptic neuron in a temporal frame; and program code to compute a value, the value computed by at least one post-synaptic neuron, the value based at least in part on at least one synaptic weight and on the encoded spikes received from the at least one pre-synaptic neuron. 18. An apparatus for communicating a non-binary value in a spiking neural network, comprising: a memory; and at least one processor coupled to the memory, the at least one processor being configured: to encode a non-binary value as one or more spikes of at least one pre-synaptic neuron in a temporal frame; and to compute a value, the value computed by at least one post-synaptic neuron, the value based at least in part on at least one synaptic weight and on the encoded spikes received from the at least one pre-synaptic neuron. 19. The apparatus of claim 18, in which the at least one processor is further configured to expand the non-binary value with a code. 20. The apparatus of claim 19, in which the code is at least one of a logarithmic temporal code and a base expansive code. 21. The apparatus of claim 18, in which the at least one processor is further configured to compute a function based at least in part on the value computed at the post-synaptic neuron. 22. The apparatus of claim 21, in which the function is a non-linear activation function. 23. The apparatus of claim 18, in which the at least one processor is further configured to decode the value. 24. The apparatus of claim 18, in which the at least one processor is further configured: to encode a second non-binary value as one or more spikes of a second pre-synaptic neuron in the temporal frame; and to compute a sum product of the value and the second non-binary value based at least in part on a summation of the received encoded spikes, and a second synaptic weight associated with a synapse between the second pre-synaptic neuron and the post-synaptic neuron. 25. The apparatus of claim 24, in which the at least one processor is further configured to compute a non-linear function based at least in part on the value computed at the post-synaptic neuron. 26. The apparatus of claim 18, in which the at least one processor is further configured to receive a spike from an orchestrator neuron to define the temporal frame. 27. The apparatus of claim 18, in which the spiking neural network implements an artificial neural network. 28. The apparatus of claim 18, in which the at least one processor is further configured to train the at least one synaptic weight using spike timing dependent plasticity. 29. The apparatus of claim 18, in which the at least one processor is further configured to train the at least one synaptic weight using a perceptron learning rule. 30. The apparatus of claim 18, in which the non-binary value is at least a part of a non-linear function.