Bernal S D_2010.pdf - University of Plymouth

Chapter 6
Discussion and conclusions
6.1 Analysis of results
6.1.1 Feedforward processing
6.1.1.1 Layer by layer response
The filtered image constitutes the input lo the Bayesian network and is coded as the A messages
of a set of dummy nodes at all localions and scales, as shown in l^'igure 5.\. Bach SI node
receives an input message from one of the dummy ntxies and obtains a normalised probability
dislribulion, A(51), over the four states (orientations). Ciabor filters have been widely used to
model the response properties of V1 simple cells, including the preprocessing that occurs at the
retina and lateral geniculate nucleus.
As illustrated in Figure 5.2, the SI response is equivalent to that of the dummy nodes except
that, due to normalization, blank input regions now present an equiprobable distribution such
that each orientation has a value of 0,25. This can be understotxl as the background activity
observed in non-active neural populations (Deneve 2008a). Furthermore, lateral inhibitory con­
nections have been suggested to provide a normalizaiion-like operation within pools of func­
tionally similar neurons (Cirossberg 2003, Kouh and foggio 2008). Normalization ha.s been
associated with homeostalic functions crucial for stability and to maintain activity within an
appropriate working regime (Grossberg 2003).
The CI model response (Figure 5,3) shows a tjualilalively similar pattern to the HMAX CI re­
sponse (Figure 5.4). The model response provides a lower resolution version of the input image,
mimicking the max operation implemented in HMAX. The multiplicative combination of evi-
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