Bernal S D_2010.pdf - University of Plymouth

2.1. OBJECT RECnCNmON
unit will be equivalent to the response of the afferent simple unit with the highest value. If any
of the simple units within the complex unit's spatial pmtling range is activated, then the complex
unit will also emit an equivalent response. This means complex units achieve a certain degree
of invariance to spatial translation and scale.
Selectivity is generated by a template-matching operation over a set of afferents tuned to differ­
ent features, implemented as a Radial Basis function network (Bishop 1995). First, a dictionary
of features or prototypes is learned. Each prototype represents a specific response configura­
tion of the afferent complex units from the level below, feeding into the simple unit in the level
above. Each simple unit is then luned to a specific feature of the dictionary, eliciting the max­
imum response when the input stimuli in the spatial region covered by the unit matches the
learned feature. The response is determined by a Gaussian tuning function which provides a
similarity measure between the input and the prototype. The malhematical formulation for both
the selectivity and invariance operations is described in Section 4.4.
With respect to the implementation of the tup level, in the first proposed model (Riesenhubcr and
Poggio 1999) this was described as a set of view-tuned units connected to the output of the C2
layer The weights were set so that the center of the Ciaussian ass(K;iated with each view-tuned
unit corresponded to a specific view of an input image. More recent versions have employed
C2 features as the input to a linear support vector machine (Serre el al. 2005b, 2007c). or have
implemented an additional unsupervised S,1/C3 level analogous to the intermediate level (Serre
et al. 2005a), In one particular implementation the model was extended to include an additional
supervised S4 level trained for a categorization task, possibly corresponding to categorization
finiis in prefrontal cortex (Serre el al. 2007b,a), A further extension, consisting of two extra
sublevels S2b and C2b, has enabled some of the models to account for bypass routes, such as
direct projections from V2 to IT which bypass V4 (Serre et al. 2005a. 2007b.a).
Learning in the model lakes place at the top level in a supervised way, while at the intermediate
levels the feature prototypes are learned in an unsupervised manner. The model implements
developmenlal-like learning, such that units store the synaptic weights of the current pattern of
activity from its afferent inputs, in response to the part of image that falls within its receptive
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