MMSE-IC1 algorithm reduces significantly the complexity of computing the filter coefficients.
In order to compute the filtered value of a pixel, four passes are required.
Then, Eq. (12) computes the filter weight.
This suggests that the biophysical oscillatory behavior of neurons and synapses leads to selecting the corresponding frequency of the inputs and performing the same computation as for the Hebbian linear case of the previous section: computing the correlation of the (filtered) inputs.
To elaborate, instead of directly copying (D_{xy}) over network (Line 5 in Algorithm 7), we first compute the bloom filter of each (D_{xy}) for (p_{xy}), denoted as ({mathcal {F}}_{xy}), which is a (m_f) bits vector.
After computing the spatial filters and using it to filter the signal, the variance of the resultant signal was computed for every time segment of 70 samples (approximately 30 ms) over the whole 3 s trial.
Computing the feedback filter coefficients requires matrix inversion of size, which requires complexity.
Computing the feedback filter coefficients requires matrix inversion of size, which requires MA operations.
However, computing the activations of a convolutional filter (unit) is much more expensive than a traditional hidden unit in an DNN.
The former uses the output of a whitened match filter for computing the branch metrics of the Viterbi algorithm (VA) leading to a Markovian channel model, while the latter can work directly with the output of a filter matched to the received pulse.
