A Computational Platform for Visual Fear Conditioning
Pavlou, A and Casey, M (2009) A Computational Platform for Visual Fear Conditioning In: International Joint Conference on Neural Networks, 2009-06-14 - 2009-06-19, Atlanta, GA.
Available under License : See the attached licence file.
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Official URL: http://dx.doi.org/ 10.1109/IJCNN.2009.5178718
Computational models of fear conditioning help us understand the sensory pathways and neural structures underlying fear elicitation in the brain. The majority of the existing models have focused on conditioning on auditory stimuli by simulating the processing of the amygdala, which is the main brain structure implicated in processing fearful stimuli. However, there is now a growing understanding of how fear is elicited from visual stimuli, but as yet we do not have sufficiently capable techniques that can be used to model visual fear conditioning. Masking experiments are a key psychophysics technique that can help us understand these pathways by observing the behavior of the amygdala when presented with visual input that is not consciously perceived (masked). The amygdala's response is indicative of whether it is influenced more by the proposed sub-cortical pathway, than by the cortical pathway. In this paper, we present a computational platform for visual fear conditioning. We use the platform to model the visual pathways leading to the amygdala and with them simulate masking experiments to explore the hypothesis that a sub-cortical pathway exists. The platform uses a modularized Hebbian learning architecture that can organize inputs topographically and condition on multiple stimuli representing visual inputs. We evaluate the properties and behavior of the platform and its capability in simulating masking experiments by comparing our simulation results with those observed for human behavior. Our results provide computational evidence for the influence the sub-cortical pathway has on the amygdala.
|Item Type:||Conference or Workshop Item (Paper)|
|Uncontrolled Keywords:||AMYGDALA, ORGANIZATION, ATTENTION, STIMULI, NETWORK, MODEL|
|Divisions:||Faculty of Engineering and Physical Sciences > Computing Science|
|Deposited By:||Symplectic Elements|
|Deposited On:||17 Jun 2011 16:29|
|Last Modified:||16 Feb 2013 15:11|
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