Bernal S D_2010.pdf - University of Plymouth

6. J. ANALYSIS OF RESULTS
However, both models fail lo capture any details at the neuronal level of description, such as
the complex balance between exciialory and inhibitory connections or spike decoding including
learning and adaptation mechanisms such as spike-timing dependent plasticity. Nevertheless,
detailed biological implementations have been proposed boih for ihc HMAX (Kouh and Poggio
20()8, Yu et al. 2002. Knoblich et al, 2007) and the belief propagation operations (George and
Hawkins 2009, Litvak and Ullnian 2009. Sieimer el al. 2(K)9). which could theoretically allow
the model to be implemented using spiking neurons. Importantly, given the large scale of the
model, which spans three different cortical regions and has over two hundred thousand nodes, it
seems reasonable to limit the level of detail until the principles tested have been shown lo work.
Implementations of belief propagation, in general, assume each node corresponds to the com­
putations perfomied by the microcircuits within a cortical column. Another interesting possi­
bility is that single neurons act as mxjes and approximate a simpler version of the algorithm.
as proposed by Rao (2004) and Dcncve (2008a). This approach has yielded some interesting
results relating generative models to spike-time dependent plasticity (Nesslerel al. 2009). Neu­
ral implementations of message-passing algorithms in graphical models are the current focus of
research for several prestigious research centres, such as the Gaisby Institute in London and the
Institute of Ncuroinformatics in Zurich.
Importantly, the model might not be suitable for neural implementation in the present state
due lo the high redundancy in the information represented by the likelihood, belief and prior
functions. A reformulation of the equations towards predictive coding approaches, wherein
feedforward messages convey the prediction errors, could lead to more eflicient implementa­
tions, in consonance with experimental evidence (Friston et al, 2006). Critically, predictive
ccxling can be derived from belief propagation, which speaks for formal similarities between
both approaches (Friston and Kiebel 2009. Kschischang et al, 2001. Yedidia et al. 2003).
The Bayesian network was d&signed based on the HMAX model, as this was a well-established
mode! of the ventral path at the appropriate level of description. However, the HMAX struc­
ture might not be the ideal one for modelling visual perception using Bayesian networks, as
it was designed exclusively for feedforward processing. For example, the Bayesian network
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