The monkey anterior intraparietal area (AIP) encodes visual information about three-dimensional object shape that is used to shape the hand for grasping. We modeled shape tuning in visual AIP neurons and its relationship with curvature and gradient information from the caudal intraparietal area (CIP). The main goal was to gain insight into the kinds of shape parameterizations that can account for AIP tuning and that are consistent with both the inputs to AIP and the role of AIP in grasping. We first experimented with superquadric shape parameters. We considered superquadrics because they occupy a role in robotics that is similar to AIP, in that superquadric fits are derived from visual input and used for grasp planning. We also experimented with an alternative shape parameterization that was based on an Isomap dimension reduction of spatial derivatives of depth (i.e., distance from the observer to the object surface). We considered an Isomap-based model because its parameters lacked discontinuities between similar shapes. When we matched the dimension of the Isomap to the number of superquadric parameters, the superquadric model fit the AIP data somewhat more closely. However, higher-dimensional Isomaps provided excellent fits. Also, we found that the Isomap parameters could be approximated much more accurately than superquadric parameters by feedforward neural networks with CIP-like inputs. We conclude that Isomaps, or perhaps alternative dimension reductions of visual inputs to AIP, provide a promising model of AIP electrophysiology data. Further work is needed to test whether such shape parameterizations actually provide an effective basis for grasp control.

Neurons in the primate middle temporal area (MT) encode information about visual motion and binocular disparity. MT has been studied intensively for decades, so there is a great deal of information in the literature about MT neuron tuning. In this study, our goal is to consolidate some of this information into a statistical model of the MT population response. The model accepts arbitrary stereo video as input. It uses computer-vision methods to calculate known correlates of the responses (such as motion velocity), and then predicts activity using a combination of tuning functions that have previously been used to describe data in various experiments. To construct the population response, we also estimate the distributions of many model parameters from data in the electrophysiology literature. We show that the model accounts well for a separate dataset of MT speed tuning that was not used in developing the model. The model may be useful for studying relationships between MT activity and behavior in ethologically relevant tasks. As an example, we show that the model can provide regression targets for internal activity in a deep convolutional network that performs a visual odometry task, so that its representations become more physiologically realistic.

The mis̱āl, a type of administrative decree associated with the most important religious official in Safavid Iran (1501-1736), the ṣadr, has received little scholarly attention. This article attempts to lay the preliminary groundwork for a more comprehensive future study on the mis̱āls of the Safavid ṣadrs. In the first part, we introduce the ṣadr and his department, the dīwān al-ṣadāra. In the second part, we study how the scribal and archival practices of the mis̱āl construct the religious and administrative authority of the ṣadr and the dīwān al-ṣadāra. We focus on an unpublished mis̱āl relating to the endowment (waqf) of the shrine of a prominent Sufi shaykh of the Ṭayfūriyya tradition in Basṭām, Shaykh Abū ʿAbdallāh Muḥammad b. ʿAlī Dāstānī (d. 417/1026). The appendix includes the text, translation, and a facsimile of the document.

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