Bernal S D_2010.pdf - University of Plymouth

3.4. EXISTING MODFXS
using two of the implementation methods described in this section {Lilvak and LHIman 2009,
Steimer et al. 2009), have already been implemented using the slate-depcndenl VLSI technol­
ogy (Emre Neftci, personal communication).
Although the technology is still ai a very early stage and the scalability of the V1.SI spiking neu­
ral networks is limited, it provides a slarting point for the development neuromorphic hardware
capable of reproducing graphical models with cortical functionality.
3.4.2 Functional models of visual processing
This subsection focuses on mtxJels based on generative modelling approaches, which employ
Bayesian networks/belief propagation or similar implementation methods. Specifically, we de­
scribe models which deal with visual perception (recognition, reconstruction, etc) and have
biologically grounded architectures. The literature in this area is very extensive so only mcxlels
most relevant to this thesis are included. To facililalc comparison between models, they have
been grouped according to the inference method employed (exact inference, sampling approx­
imation, or variational approximation), although the classitication is not .strict as some models
share characteristics of several methods. A summary and comparison of the models is included
al the end of this subsection.
3.4.2.1 Models based on exact inference mclhods
The model proposed by Epshiein et aJ. (2008) extends a well-known feedforward object recog­
nition model, namely Ultman's fragment-based hierarchical model described in Section 2.1.2.
A hierarchy of informative Iragments and its corresponding smaller sub-fragments are learned
for each class of objects. This information is stored using a factor graph where each variable
represents an object fragment, which can take A' different values/.slates indicating the po.sition
of that fragment within the image (a value of 0 indicates the fragment is not present). The
relation (conditional probability function) between a sub-fragment and its parent fragments de­
pends on the coordinate difference between the locations of child and parent fragments, and not
on their absolute position. This allows the model to perform recognition with certain position
invariance.
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