A computational model of the anterior intraparietal area

Rezai, Omid and Kleinhans, Ashley and Matallanas de Avila, Eduardo and Selby, Ben and Tripp, Bryan Patrick (2014). A computational model of the anterior intraparietal area. In: "10th Bersntein Conference in Computational Neuroscience", 03/09/2014 - 05/09/2014, Göttingen, Germany. p. 88.


Title: A computational model of the anterior intraparietal area
  • Rezai, Omid
  • Kleinhans, Ashley
  • Matallanas de Avila, Eduardo
  • Selby, Ben
  • Tripp, Bryan Patrick
Item Type: Presentation at Congress or Conference (Poster)
Event Title: 10th Bersntein Conference in Computational Neuroscience
Event Dates: 03/09/2014 - 05/09/2014
Event Location: Göttingen, Germany
Title of Book: 10th Bersntein Conference in Computational Neuroscience
Date: 2014
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Tecnología Fotónica y Bioingeniería
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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The monkey anterior intraparietal area (AIP) encodes visual information about three-dimensional object shape that is used to shape the hand for grasping. In robotics a similar role has been played by modules that fit point cloud data to the superquadric family of shapes and its various extensions. We developed a model of shape tuning in AIP based on cosine tuning to superquadric parameters. However, the model did not fit the data well, and we also found that it was difficult to accurately reproduce these parameters using neural networks with the appropriate inputs (modelled on the caudal intraparietal area, CIP). The latter difficulty was related to the fact that there are large discontinuities in the superquadric parameters between very similar shapes. To address these limitations we adopted an alternative shape parameterization based on an Isomap nonlinear dimension reduction. The Isomap was built using gradients and curvatures of object surface depth. This alternative parameterization was low-dimensional (like superquadrics), but data-driven (similar to an alternative clustering approach that is also sometimes used in robotics) and lacked large discontinuities. Isomaps with 16 or more dimensions reproduced the AIP data fairly well. Moreover, we found that the Isomap parameters could be approximated from CIP-like input much more accurately than the superquadric parameters. We conclude that Isomaps, or perhaps alternative dimension reductions of CIP signals, provide a promising model of AIP tuning. We have now started to integrate our model with a robot hand, to explore the efficacy of Isomap shape reductions in grasp planning. Future work will consider dynamics of spike responses and integration with related visual and motor area models.

More information

Item ID: 36317
DC Identifier: https://oa.upm.es/36317/
OAI Identifier: oai:oa.upm.es:36317
Deposited by: Memoria Investigacion
Deposited on: 12 Jul 2015 07:43
Last Modified: 06 Jun 2016 07:43
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