Bernal S D_2010.pdf - University of Plymouth

6.1. ANALYSIS OF RESULTS
square. Reconstnictions using more sparse weight matrices may not cover as much area of the
square hut might be cleaner and more precise. Despile this, the mean absolute error provides an
objective indicator of the goodness of lit between the reconstructions and can be used to guide
the broad initial parameter search. This can be later refined for a smaller target parameter space
using a more accurate measure.
Although a more exhaustive parameter search is required, the preliminary results obtained
strongly suggest that asymmetric weight matrices are required: feedforward weights should
be relatively sparse, leading to more selective higher-level representations; while feedback con­
nection matrices and high-level representations require a higher densily in order io increase
the amouni of information available lo reconstruct the lower levels. This is consistent with
evidence from cortex showing that feedforward connections tend to have sparse axonat bifur­
cation whereas backward connections have abundant axonal bifurcation, [•unhermore, it agrees
with the theoretical perspective thai argues that a cell is likely to have few feedforward driving
connections and many modulalory conneelions {Friston 2003),
Anolher parameter ihat is also likely to influence the feedback reconslruclion is the number of
features per group in the complex layers. Given the current implemenlution, where feedback
to complex layers affects eijually all ihe I'eatures belonging to a group, increasing ihc number
of features per group will increase the overall amouni of, slill relatively diffuse, feedback. One
important extension for the model would be to achieve heterogeneous feedback modulation of
the features within a group, This can be done, for example, by allowing features to belong to
different groups, such as in the HTM model (George and Hawkins 2009). The learning method
in HTM automatically does this, whereas in the proposed model this could be achieved by
finding correlations between features in different groups and ihen combining them into a single
new group. A more comprehensive study of how [his factor can aid the feedback disambiguation
process is left as future work,
6.1.2.4 Feedback from C2 to SI
As discussed above, it is difficult to generate accurate feedback from the C2 square represen­
tation, so an alternative is to clamp the 7z{S2) to the ideal S2 square representalion as if the
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