Bernal S D_2010.pdf - University of Plymouth

6.3. COMPARISON WITH PRKVIOUS MODBLS
6.3 Comparison with previous models
The proposed model shares many structural and functional similarities with the Hierarchical
Temporal Memory (HTM) model proposed by George and Hawkins (2009). They both employ
the belief propagation equations to approximate selectivity and invariance in alternating hierar­
chical layers. The main difference is that the HTM nodes embody both the simple and complex
features, which are called coincidence patlems and groups (Markov chains), respectively. The
inclusion of a Markov chain within the node makes HTM tjualiiatively different from a Bayesian
network. Consequently, belief propagation also becomes a qualitatively diffcren! algnriihm that
can be applied exclusively to HTM nodes. By combining simple and complex features within
the same node, the authors avoid much of the complexity, and possibly benefits, inherent in a
rigorous implementation of belief propagation, such as lotjps and multiple parents.
The proposed model implements the same feature grouping mechanism present in HTMs (ex­
cept for the temporal correlation of Markov chains) by exploiting the weights of the CI*Ts be­
tween simple and complex layers. Figure A.l in the Appendix Section A provides a schematic
representation of an HTM network that implements the 3-level HMAX model (Serre et al,
2007c) used for this thesis. The HTM network is formulated using the original HTM nutation
(George and Hawkin.s 20()9) combined with the original HMAX parameter notation (Serre et al.
2007c). The resulling HTM network can be compared to the Bayesian network thai implements
the same 3-level HMAX mode! (Figure 4.4) in order to obtain a better understanding of the
differences between HTM and the proposed model.
The proposed mtxlel employs loopy IxMief propagation to perform approximate inference, sim­
ilar to the HTM model (George and Hawkins 2009). Other models implementing approximate
perceptual inference have employed message-passing algorithms derived from sampling meth­
ods (Hinton et al. 2006, Lee and Mumford 2003, Lewicki and ,Sejnowski 1997) or variiilional
methods (Murray and Kreutz-Delgado 2007, Rao and Ballard 1999. Friston and Kiebel 2009),
The nuKiel by Epshiein el al. (2008) implements exact inference using belief propagation. How­
ever, it is employed over simplified networks with no ItKips and is qiialilatively different from
the proposed model in that nodes correspond to features and .states to locations. The model
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