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Stimulus Simplification and Object Representation: A Modeling Study

dc.date.accessioned2004-10-20T21:04:59Z
dc.date.accessioned2018-11-24T10:23:37Z
dc.date.available2004-10-20T21:04:59Z
dc.date.available2018-11-24T10:23:37Z
dc.date.issued2002-03-15en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/7269
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/7269
dc.description.abstractTsunoda et al. (2001) recently studied the nature of object representation in monkey inferotemporal cortex using a combination of optical imaging and extracellular recordings. In particular, they examined IT neuron responses to complex natural objects and "simplified" versions thereof. In that study, in 42% of the cases, optical imaging revealed a decrease in the number of activation patches in IT as stimuli were "simplified". However, in 58% of the cases, "simplification" of the stimuli actually led to the appearance of additional activation patches in IT. Based on these results, the authors propose a scheme in which an object is represented by combinations of active and inactive columns coding for individual features. We examine the patterns of activation caused by the same stimuli as used by Tsunoda et al. in our model of object recognition in cortex (Riesenhuber 99). We find that object-tuned units can show a pattern of appearance and disappearance of features identical to the experiment. Thus, the data of Tsunoda et al. appear to be in quantitative agreement with a simple object-based representation in which an object's identity is coded by its similarities to reference objects. Moreover, the agreement of simulations and experiment suggests that the simplification procedure used by Tsunoda (2001) is not necessarily an accurate method to determine neuronal tuning.en_US
dc.format.extent7 p.en_US
dc.format.extent1367647 bytes
dc.format.extent641994 bytes
dc.language.isoen_US
dc.subjectAIen_US
dc.subjectcomputational neuroscience object recognition representation simplificationen_US
dc.titleStimulus Simplification and Object Representation: A Modeling Studyen_US


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