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Table 1 GHA-basedspike feature extraction

From: Stream-based Hebbian eigenfilter for real-time neuronal spike discrimination

Input:
Neuronal spikes, x (i) ;
Initial synaptic weight, W (1) ;
Learning rate, η ;
Output:
Principal components of neuronal spikes, W (N) ;
1. Initialize synaptic weight W (1) and learning rate η, j = 1
2. Calculate the mean vector of the aligned spikes
    μ = i = 1 n x (i)/n
3. Zero-mean transformation
    x (i) = x ( i ) - μ 1 i n
4. Perform Hebbian learning on zero-mean data
    y (j) = W (j) x (i)
    LT(j) = LT y (j) y T (j)
    dW(j) =η y (j) x T ( i ) - LT(j) W (j)
    W (j + 1) = W (j) + dW(j)
5. If j= N, the algorithm stops, otherwise j = j+ 1, go to step 4