Bernal S D_2010.pdf - University of Plymouth

6.1. ANALYSIS OF RESULTS
operations. Specific functions can then lie implemented by defining the appropriate structure
and weights. Ii has been argued thai this and other properties map well onto cortical connectiv­
ity and account for experimenla! evidence as described in Sections 3.1 and 3.2. Additionally,
the mtKlel is well-suited for large-scale parallel implemenlation using asynchronous message-
passing, such as that offered by multicore computers or hardware implementation (Jin et al,
2010, Neftcictal. 2010).
The model is, nonetheless, still a Bayesian network and thus cannot be considered biologically
realistic. The model can only be argued lo be realistic al a network or systems level of abstrac­
tion, which is closer lo cognitive functionality than lo biology. At this level of abstraction the
network reproduces the same properties as ihe HMAX model, such as the hierarchical cortical
.structure and the tuning and invariance profiles of neurons at VI, V4 and inferoiemporal (IT)
cortex. This, of course, is slill a strong simplification of the visual system. For example, direct
reciprocal connections can be found between distant areas such as VI and higher-level object-
processing regions (Huang et al. 2007), which are not included in the model. Furthermore,
the Gabor fillers used to model VI neurons RF' and ihe distinction between simple and com­
plex cells are an oversimplilicalion of ihe wide spectrum of V1 neurons functionality (Ringach
2004). In addition, the response of neurons in higher cortical levels is still not well understood
and thus, any aiiempt lo mtKlel them is likely to be oversimplified and inaccurate (see Section
2.1.1 for further details).
Some of these effects could be accommodated by future versions of the model. For example,
direcl connections between the top and bottom layers of the model could be included by learning
the appropriate weights, similar lo one of ihe implemented version of HMAX (Serre et al.
2007 b).
Regarding the complexity of neural responses, the proposed model has an advantage over
HMAX in the sense thai responses are modulated over time by the interaction between feed­
forward and feedback connections. This accounts for extra-classical RF propenies of neurons
{Angelucci and Bullier 2003) and adds a large time-scale temporal dimension lo the model
responses (Kiebel ei al. 2008) opposed lo the static HMAX responses.
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