Bernal S D_2010.pdf - University of Plymouth

6.4. FUTURE WORK
to temporal context and bisiability (Mamassian and Clouicher 2005)-
• Include the lateral geniculale nucleus (LGN) as the bottom layer of the Bayesian network.
This would allow the conieothalamic feedback loop to be included within the same per­
ceptual inference framework and compare model results with the detailed experimental
data (Simioeial. 2006).
• lncrea.se the size of the input image to allow the simulation of multiple object detection,
spatial attention and aulomaiic aiteni ion-shifting (e.g. occluder vs. occluded object)
(Wallher and Koch 2007, Chikkerur el al. 2009). Additionally, natural images instead of
silhouettes can be used. Thanks to the parametrized model implementation, no additional
extension, apart from learning new weights, is required lo test input images of different
sizes and characteristics.
• Test the model using input images that change over lime (movies). The hiearchical struc­
ture of the generative model should naturally lead to a hierarchy of time-scales similar to
slow-feature analysis (Wiskoii and Sejnowski 2002. Kiebel et al. 2008).
• Extend the model to include the where path containing spatial and motion information.
This could be modelled as a parallel Bayesian network wilh cross-inleraciions with the
what path at different levels.
• Formulate the model in a more generic way that can then be applied to diffcrenl visual
.scenarios or oilier domains, such as auditory perception. The generic formulation should
specify certain principles and constraints, describing how Bayesian networks and belief
propagation can be applied to perceptual inference processes where selectivity and invari-
ance are desired properties. The specific structure and parameters can then be panially
learned using Bayesian learning methods.
• Real-time hardware implemenlationof the model using large parallel distributed .systems,
such as SpiNNaker (Jin et al. 2010).
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