Bernal S D_2010.pdf - University of Plymouth

6.1. ANALYSIS OF RF.SULTS
the image resolution decreases to ii point where the square ligure is unrecogniziiblc. which
jusiilies focusing on only the simulations on the lowest scale baud. The original HMAX model
was designed lo process large natural images where the lower resolution of the higher bands
might play a more important role.
6.1.2.3 Feedback from C2 to S2
The square reconstructions from feedback originating at layers CI and S2 show a relatively
good lit to the ideal square representation, even when iisinn feedback weights equivalent to the
feedforward weights. However, the loss of infonnation between the S2 and C2 layer is much
higher as it is mapping over 2000 nixies into 9 nodes. This is a general problem of modelling
feedback connections in mtxJcls that implement an invariance operation, such as the max func­
tion, which cannot be mapped backwards. For this reason, and because the feedforward weights
proved lo be inappropriale, u more systematic study was performed to elucidate what the key
factors to obtain meaningful feedback from the C2 layer are.
Figures 5.23 and 5.24 show the results of testing three factors. The first one is the number of
non-zero elements, or the inverse of sparseness, of the S2-C2 feedback weight matrix, which
shows an almost linear, positive correlation with the ability of feedback to reconslnicl an ideal
CI square representation. The second factor tested was the sampling parameters Nc2 and '^cz.
which, within the limited range of values tested due to the high computational cost, showed a
very clear positive correlation with feedback's reconstniction capacity. The last factor studied
was the number of non-zero elements in the S2-C2 feedforward weight matrix used to generate
the C2 square representation, from where feedback originated. Although only two different
values were tested, comparison between Figures 5.23 and 5.24 suggests that C2 representations
generated using more non-zero elements in the feedforward weight matrices (less sparse) are
better for feedback reconstruction.
It is important lo note that the pixel-wise mean absolute error is not a perfect measure of the
goodness of fit between the C1 feedback reconstruction and the idea! CI square. For example.
CI reconstructions using a less sparse matrices tend to show a higher level of background noise
or overall activity, which might lead to a lower error as they cover greater area of the ideal
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