TIME ENCODING USING INTEGRATE AND FIRE SAMPLER
Systems and methods of time encoding using an integrate and fire (IF) sampler are disclosed. In an example, a method includes receiving input signals for separate classes. The method also includes generating a pulse train based on the input signals. The method also includes binning the pulse train to generate a feature vector.
1 . A method of time encoding using an integrate and fire (IF) sampler, comprising:
receiving input signals for separate classes;
generating a pulse train based on the input signals; and
binning the pulse train to generate a feature vector.
2 . The method of claim 1 , wherein the separate classes include at least a first class and a second class.
3 . The method of claim 1 , further comprising applying IF encoding to the input signals.
4 . The method of claim 1 , further comprising determining higher order statistics using the feature vector for downstream processing.
5 . The method of claim 1 , applying discriminant analysis for class separation.
6 . The method of claim 5 , wherein discriminant analysis is linear.
7 . The method of claim 1 , further comprising applying at least one of: quadradic discriminant analysis, neural networks, support vector machines, K-NN (nearest neighbors), and other non-parametric statistics based classifiers for class separation.
8 . The method of claim 1 , further comprising determining class conditional probability densities based on the feature vector.
9 . The method of claim 8 , further comprising determining class assignment based on a likelihood ratio of the class conditional probability densities of input signal classes.
10 . The method of claim 1 , wherein binning comprises dividing the pulse train into equal size bins and counting a number of pulses in each bin.
11 . A system for time encoding using an integrate and fire (IF) sampler, comprising:
a pulse train generator to generate a pulse train based on the input signals for separate classes; and
a feature vector generator to bin the pulse train and generate a feature vector.
12 . The system of claim 11 , wherein the separate classes include at least a first class and a second class.
13 . The system of claim 11 , further comprising an IF encoder to apply IF encoding to the input signals.
14 . The system of claim 11 , further comprising a statistical analyzer to determine higher order statistics.
15 . The system of claim 11 , a discriminate analyzer to distinguish class separation of input signals.
16 . The system of claim 11 , further comprising a class assignment module to determine class assignment based on class conditional probability densities.
17 . A system having a computer readable medium and a processor, the processor executing program code for time encoding using an integrate and fire (IF) sampler by:
generating a pulse train based on input signals for separate classes; and
binning the pulse train to generate a feature vector.
18 . The system of claim 17 , wherein the program code applies IF encoding to the input signals.
19 . The system of claim 17 , wherein the program code applies discriminate analysis to distinguish class separation of input signals.
20 . The system of claim 17 , wherein the program code determines a class assignment based on class conditional probability densities.