Title
Hippocampal anatomy supports the use of context in object recognition: a computational model.
Abstract
The human hippocampus receives distinct signals via the lateral entorhinal cortex, typically associated with object features, and the medial entorhinal cortex, associated with spatial or contextual information. The existence of these distinct types of information calls for some means by which they can be managed in an appropriate way, by integrating them or keeping them separate as required to improve recognition. We hypothesize that several anatomical features of the hippocampus, including differentiation in connectivity between the superior/inferior blades of DG and the distal/proximal regions of CA3 and CA1, work together to play this information managing role. We construct a set of neural network models with these features and compare their recognition performance when given noisy or partial versions of contexts and their associated objects. We found that the anterior and posterior regions of the hippocampus naturally require different ratios of object and context input for optimal performance, due to the greater number of objects versus contexts. Additionally, we found that having separate processing regions in DG significantly aided recognition in situations where object inputs were degraded. However, split processing in both DG and CA3 resulted in performance tradeoffs, though the actual hippocampus may have ways of mitigating such losses.
Year
DOI
Venue
2013
10.1155/2013/294878
Comp. Int. and Neurosc.
Keywords
Field
DocType
optimal performance,recognition performance,actual hippocampus,object input,information call,performance tradeoffs,contextual information,hippocampal anatomy,object recognition,object feature,associated object,computational model,human hippocampus,recognition psychology,hippocampus
Contextual information,Pattern recognition,Computer science,Entorhinal cortex,Artificial intelligence,Artificial neural network,Hippocampal formation,Hippocampus,Cognitive neuroscience of visual object recognition
Journal
Volume
Issue
ISSN
2013
Issue-in-Progress
1687-5273
Citations 
PageRank 
References 
2
0.42
3
Authors
4
Name
Order
Citations
PageRank
Patrick Greene120.42
Mike Howard214814.36
Rajan Bhattacharyya3215.60
Jean-marc Fellous41157167.09