The Individual is Nothing, the Class Everything: Psychophysics and Modeling of Recognition in Obect Classes

Most psychophysical studies of object recognition have focussed on the recognition and representation of individual objects subjects had previously explicitely been trained on. Correspondingly, modeling studies have often employed a 'grandmother'-type representation where the objects to be recognized were represented by individual units. However, objects in the natural world are commonly members of a class containing a number of visually similar objects, such as faces, for which physiology studies have provided support for a representation based on a sparse population code, which permits generalization from the learned exemplars to novel objects of that class. In this paper, we present results from psychophysical and modeling studies intended to investigate object recognition in natural ('continuous') object classes. In two experiments, subjects were trained to perform subordinate level discrimination in a continuous object class - images of computer-rendered cars - created using a 3D morphing system. By comparing the recognition performance of trained and untrained subjects we could estimate the effects of viewpoint-specific training and infer properties of the object class-specific representation learned as a result of training. We then compared the experimental findings to simulations, building on our recently presented HMAX model of object recognition in cortex, to investigate the computational properties of a population-based object class representation as outlined above. We find experimental evidence, supported by modeling results, that training builds a viewpoint- and class-specific representation that supplements a pre-existing repre-sentation with lower shape discriminability but possibly greater viewpoint invariance.

Modeling Invariances in Inferotemporal Cell Tuning

In macaque inferotemporal cortex (IT), neurons have been found to respond selectively to complex shapes while showing broad tuning ("invariance") with respect to stimulus transformations such as translation and scale changes and a limited tuning to rotation in depth. Training monkeys with novel, paperclip-like objects, Logothetis et al. could investigate whether these invariance properties are due to experience with exhaustively many transformed instances of an object or if there are mechanisms that allow the cells to show response invariance also to previously unseen instances of that object. They found object-selective cells in anterior IT which exhibited limited invariance to various transformations after training with single object views. While previous models accounted for the tuning of the cells for rotations in depth and for their selectivity to a specific object relative to a population of distractor objects, the model described here attempts to explain in a biologically plausible way the additional properties of translation and size invariance. Using the same stimuli as in the experiment, we find that model IT neurons exhibit invariance properties which closely parallel those of real neurons. Simulations show that the model is capable of unsupervised learning of view-tuned neurons. The model also allows to make experimentally testable predictions regarding novel stimulus transformations and combinations of stimuli.

Performance-Driven Microfabrication-Oriented Methodology for MEMS Conceptual Design with Application in Microfluidic Device Design

Performance and manufacturability are two important issues that must be taken into account during MEMS design. Existing MEMS design models or systems follow a process-driven design paradigm, that is, design starts from the specification of process sequence or the customization of foundry-ready process template. There has been essentially no methodology or model that supports generic, high-level design synthesis for MEMS conceptual design. As a result, there lacks a basis for specifying the initial process sequences. To address this problem, this paper proposes a performance-driven, microfabrication-oriented methodology for MEMS conceptual design. A unified behaviour representation method is proposed which incorporates information of both physical interactions and chemical/biological/other reactions. Based on this method, a behavioural process based design synthesis model is proposed, which exploits multidisciplinary phenomena for design solutions, including both the structural components and their configuration for the MEMS device, as well as the necessary substances for the chemical/biological/other reactions. The model supports both forward and backward synthetic search for suitable phenomena. To ensure manufacturability, a strategy of using microfabrication-oriented phenomena as design knowledge is proposed, where the phenomena are developed from existing MEMS devices that have associated MEMS-specific microfabrication processes or foundry-ready process templates. To test the applicability of the proposed methodology, the paper also studies microfluidic device design and uses a micro-pump design for the case study.