Described is a system for decoding spiking reservoirs even when the spiking reservoir has continuous synaptic plasticity. The system uses a set of training patterns to train a neural network having a spiking reservoir comprised of spiking neurons. A test pattern duration d is estimated for a set of test patterns P, and each test pattern is presented to the spiking reservoir for a duration of d/P seconds. Output spikes from the spiking reservoir are generated via readout neurons. The output spikes are measured and the measurements are used to compute firing rate codes, each firing rate code corresponding to a test pattern in the set of test patterns P. The firing rate codes are used to decode performance of the neural network by computing a discriminability index (DI) to discriminate between test patterns in the set of test patterns P.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for decoding output from spiking reservoirs, the system comprising: one or more processors and a non-transitory memory having instructions encoded thereon such that when the instructions are executed, the one or more processors perform operations of: training a neural network having a spiking reservoir comprised of spiking neurons by using a set of training patterns; presenting each test pattern in a set of test patterns to the spiking reservoir; generating output spikes from the spiking reservoir via a set of readout neurons; measuring the output spikes, resulting in a plurality of measurements, and using the plurality of measurements to compute firing rate codes, each firing rate code corresponding to a test pattern in the set of test patterns P; and decoding performance of the neural network, using the firing rate codes, by computing a discriminability index (DI) to discriminate between test patterns in the set of test patterns P. 2. The system as set forth in claim 1, wherein the neural network exhibits continuous plasticity. 3. The system as set forth in claim 1, wherein the one or more processors further perform operations of: computing, for each test pattern p, firing rates fip of a sink neuron i in the neural network as the total number of output spikes during a duration d; estimating a maximum firing rate fmaxp from the firing rates fip of all sink neurons in the neural network for the test pattern p; and computing a firing rate code for each test pattern p using fmaxp and fip. 4. The system as set forth in claim 3, wherein the DI is a product of a separability measure, ε, and a uniqueness measure, γ, wherein the separability measure is defined as a measure of a degree of separation of firing rate codes for the set of test patterns P, and wherein the uniqueness measure is defined as a number of unique firing rate codes produced by the neural network relative to a maximum possible number of unique firing rate codes. 5. The system as set forth in claim 4, wherein the separability measure is computed according to the following: ɛ = 1 - D intra D inter , where Dintra is defined an average pair-wise distance between firing rate codes computed from all possible unique pairs of firing rate codes generated by the neural network for the same test pattern, and Dinter is defined as an average pair-wise distance between firing rate codes computed from all possible unique pairs of firing rate codes generated by the neural network for the set of test patterns P. 6. The system as set forth in claim 5, wherein the uniqueness measure is computed according to the following: γ = #  S P , where #S represents the total number of unique firing rate codes for the set of test patterns P. 7. A computer-implemented method for decoding output from spiking reservoirs, comprising: an act of causing one or more processors to execute instructions stored on a non-transitory memory such that upon execution, the one or more processors perform operations of: training a neural network having a spiking reservoir comprised of spiking neurons by using a set of training patterns; presenting each test pattern in a set of test patterns to the spiking reservoir; generating output spikes from the spiking reservoir via a set of readout neurons; measuring the output spikes, resulting in a plurality of measurements, and using the plurality of measurements to compute firing rate codes, each firing rate code corresponding to a test pattern in the set of test patterns P; and decoding performance of the neural network, using the firing rate codes, by computing a discriminability index (DI) to discriminate between test patterns in the set of test patterns P. 8. The method as set forth in claim 7, wherein the neural network exhibits continuous plasticity. 9. The method as set forth in claim 7, wherein the one or more processors further perform operations of: computing, for each test pattern p, firing rates fip of a sink neuron i in the neural network as the total number of output spikes during a duration d; estimating a maximum firing rate fmaxp from the firing rates fip of all sink neurons in the neural network for the test pattern p; and computing a firing rate code for each test pattern p using fmaxp and fip. 10. The method as set forth in claim 9, wherein the DI is a product of a separability measure, ε, and a uniqueness measure, γ, wherein the separability measure is defined as a measure of a degree of separation of firing rate codes for the set of test patterns P, and wherein the uniqueness measure is defined as a number of unique firing rate codes produced by the neural network relative to a maximum possible number of unique firing rate codes. 11. The method as set forth in claim 10, wherein the separability measure is computed according to the following: ɛ = 1 - D intra D inter , where Dintra is defined as an average pair-wise distance between firing rate codes computed from all possible unique pairs of firing rate codes generated by the neural network for the same test pattern, and Dinter is defined as an average pair-wise distance between firing rate codes computed from all possible unique pairs of firing rate codes generated by the neural network for the set of test patterns P. 12. The method as set forth in claim 11, wherein the uniqueness measure is computed according to the following: γ = #  S P , where #S represents the total number of unique firing rate codes for the set of test patterns P. 13. A computer program product for decoding output from spiking reservoirs, the computer program product comprising: computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having one or more processors for causing the processor to perform operations of: training a neural network having a spiking reservoir comprised of spiking neurons by using a set of training patterns; presenting each test pattern in a set of test patterns to the spiking reservoir; generating output spikes from the spiking reservoir via a set of readout neurons; measuring the output spikes, resulting in a plurality of measurements, and using the plurality of measurements to compute firing rate codes, each firing rate code corresponding to a test pattern in the set of test patterns P; and decoding performance of the neural network, using the firing rate codes, by computing a discriminability index (DI) to discriminate between test patterns in the set of test patterns P. 14. The computer program product as set forth in claim 13, wherein the neural network exhibits continuous plasticity. 15. The computer program product as set forth in claim 13, further comprising instructions for causing the one or more processors to perform operations of: computing, for each test pattern p, firing rates fip of a sink neuron i in the neural network as the total number of output spikes during a duration d; estimating a maximum firing rate fmaxp from the firing rates fip of all sink neurons in the neural network for the test pattern p; and computing a firing rate code for each test pattern p using fmaxp and fip. 16. The computer program product as set forth in claim 15, wherein the DI is a product of a separability measure, ε, and a uniqueness measure, γ, wherein the separability measure is defined as a measure of a degree of separation of firing rate codes for the set of test patterns P, and wherein the uniqueness measure is defined as a number of unique firing rate codes produced by the neural network relative to a maximum possible number of unique firing rate codes. 17. The computer program product as set forth in claim 16, wherein the separability measure is computed according to the following: ɛ = 1 - D intra D inter , where Dintra is defined as an average pair-wise distance between firing rate codes computed from all possible unique pairs of firing rate codes generated by the neural network for the same test pattern, and Dinter is defined as an average pair-wise distance between firing rate codes computed from all possible unique pairs of firing rate codes generated by the neural network for the set of test patterns P. 18. The computer program product as set forth in claim 17, wherein the uniqueness measure is computed according to the following: γ = #  S P , where #S represents the total number of unique firing rate codes for the set of test patterns P. 19. The system as set forth in claim 1, wherein the set of test patterns P are input patterns from images obtained around a vehicle, and wherein the set of test patterns P are used to assist the vehicle in autonomous driving. 20. A system for decoding output from spiking reservoirs, the system comprising: one or more processors and a non-transitory memory having instructions encoded thereon such that when the instructions are executed, the one or more processors perform operations of: providing an input signal to a neural network, the neural network having a spiking reservoir comprised of spiking neurons trained by: presenting each test pattern in a set of test patterns to the spiking reservoir; generating; output spikes from the spiking reservoir via a set of readout neurons; measuring the output spikes, resulting in a plurality of measurements, and using the plurality of measurements to compute firing rate codes, each firing rate code corresponding to a test pattern in a set of test patterns P; and determining performance of the neural network, using the firing rate codes, by computing a discriminability index (DI) to discriminate between test patterns in the set of test patterns P; obtaining a readout code from the neural network produced in response to the input signal; and identifying a component of the input signal based on the readout code.