Bernal S D_2010.pdf - University of Plymouth

3.2. EVIDENCE FROM THE BRAIN
hierarchical connectionist network provides a tractable implementation to computing the expo­
nential number of possible causes underlying each pattern, unlike other approaches such as the
Expectation-Maximization algorithm, which runs into prohibitive computational costs. The key
insight is to rely on using an explicit recognition model with its own parameters instead of using
the generative model parameters to perform recognition in an iterative process.
In recent years, the Bayesian brain hypothesis has become increasingly popular, and several
authors (Friston 2005, Dean 2006. Lee and Mumford 2003. Rao 2006. Deneve 2005, I.ilvak
and Uilman 2009. Steimer et al. 2009. Hinion et al. 2006) have elaborated and extended this
theory. Many of their contributions arc described in this chapter. One of the main reasons for
the rising recognition of the Baycsian brain hypothesis is its ability to accommodale disparale
experimental results and existing models within a common framework, as will be illustrated in
the following sections.
3.2 Evidence from the brain
The Bayesian brain irnxlel maps well onto anatomical, physiological and psychophysical as­
pects of the brain. Visual cortices are organized hierarchically {Felleman and Van lissen 1991)
in recurrent architectures using distinct forward and backward connections with functional
asymmetries. While feedforward connections are mainly driving, feedback connections arc
mostly modulatory in their effects (Angelucci and Bullicr 2003, Hupe et al. 2001). Evidence
shows that feedback originating in higher level areas such as V4, IT or MT. with bigger and
more complex receptive fields, can modify and shape VI responses, accounting for contextual
or extra-classical receptive lield effects (Guo et al. 2007, Harrison et al. 2007, Huang et al.
2001. Sillito et al. 2006). Chapter 2 describes these aspects in more detail. As we will see in
this section, hierarchical generative models are reminisceiii of the described cortical architec­
ture, sharing many structural and connectivity properties.
In terms of the neural mechanisms involved, although il is not yet practical to test the proposed
framework in detail, there are some relevant findings from functional magnetic resonance imag­
ing (fMRI) and electrophysiological recordings, Murray ei al. (2004) showed ihal when local
information is perceptually organized inlo whole objects, activity in VI decreases while acliv-
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