It is proposed to conceive of representation as an emergent phenomenon that is supervenient on patterns of activity of coarsely tuned and highly redundant feature detectors. The computational underpinnings of the outlined theory of representation are (1) the properties of collections of overlapping graded receptive fields, as in the biological perceptual systems that exhibit hyperacuity-level performance, and (2) the sufficiency of a set of proximal distances between stimulus representations for the recovery of the corresponding distal contrasts between stimuli, as in multidimensional scaling. The present preliminary study appears to indicate that this concept of representation is computationally viable, and is compatible with psychological and neurobiological data. 1 Introduction A perceptual system confronted with a stimulus must (i) decide whether the stimulus belongs to an already encountered category, and (ii) if necessary, create a new category record for the stimulus a...

This paper sketches several aspects of a hypothetical cortical architecture for visual object recognition, based on a recent computational model. The scheme relies on modules for learning from examples, suchas Hyperbf-likenetworks, as its basic components. Such models are not intended to be precise theories of the biological circuitry but rather to capture a class of explanations we call Memory-Based Models (MBM) that contains sparse population coding, memory-based recognition and codebooks of prototypes. Unlike the sigmoidal units of some artificial neural networks, the units of MBMs are consistent with the usual description of cortical neurons as tuned to multidimensional optimal stimuli. We will describe howan example of MBM may be realized in terms of cortical circuitry and biophysical mechanisms, consistent with psychophysical and physiological data. A number of predictions, testable with physiological techniques, are made.

We propose that, for the task of object recognition, the visual system decom-poses shapes into parts, that it does so using a rule defining part boundaries rather than part shapes, that the rule exploits a uniformity of nature-transver-sal@, and that parts with their descriptions and spatial relations provide a first index into a memory of shapes. This rule allows an explanation of several visual illusions. We stress the role inductive inference in our theory and conclude with a p&is of unsolved problems. 1.

this paper summarizes the main results obtained to date in these studies. The second part examines two issues: the robustness of the 3-D interpretation of perspective velocity fields, and th'e 3-D information contained in orthographic velocity fields. The two are related because, under local analysis, limitations on the interpretation of orthographic velocity fields also apply to perspective projection. The following results are established: When the interpretation is applied locally, the 3-D interpretation of the perspective yelocity field is unstable

Are minds really dynamical or are they really symbolic? Because minds are bundles of computations, and because computation is always a matter of interpretation of one system by another, minds are necessarily symbolic. Because minds, along with everything else in the universe, are physical, and insofar as the laws of physics are dynamical, minds are necessarily dynamical systems. Thus, the short answer to the opening question is “yes.” It makes sense to ask further whether some of the computations that constitute a human mind are constrained by functional, algorithmic, or implementational factors to be essentially of the discrete symbolic variety (even if they supervene on an apparently continuous dynamical substrate). I suggest that here too the answer is “yes” and discuss the need for such discrete, symbolic cognitive computations in communication-related tasks.

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The human visual system has the ability to utilize motion information to infer the shapes of surfaces. More specifically, we are able to derive descriptions of rigidly rotating smooth surfaces entirely from tile orthographic projection of the motions of their surface markings. A computational analysis of this ability...

Computer vision systems are, on most counts, poor performers, when compared to their biological counterparts. The reason for this may be that computer vision is handicapped by an unreasonable assumption regarding what it means to see, which became prevalent as the notions of intrinsic images and of representation by reconstruction took over the field in the late 1970’s. Learning from biological vision may help us to overcome this handicap. 1.

In this paper we present a semantic analysis of the imperfective paradox based on the Event Calculus (van Lambalgen & Hamm 2004), a planning formalism characterizing a class of models which can be computed by connectionist networks. We report the results of a questionnaire that support the semantic theory and suggest that different aspectual classes of VPs in the progressive give rise to different entailment patterns. Further, a processing model is outlined, combining the semantic analysis with the psycholinguistic principle of immediacy in the framework of recurrent networks. The model is used to derive predictions concerning the electrophysiological correlates of the computations described by the Event Calculus. 1

FOREWORD This thesis was carried out in close collaboration with my supervisor Neslihan Serap ¸Sengör over the last two and a half years. I would like to thank her for introducing me the field, giving advices, sharing her opinions and listening to mine. It is certain that this work will not be possible without her generous support. I would like to thank Fatih ˙I¸sbecer, my manager at Pozitron, for his unfailing support which has enabled me to complete this work. In the first year of this research, I have attended to Behavioral Neurology course given by Hakan Gürvit at Istanbul University. I would like to thank him for giving me this opportunity and kindly supporting anyone interested in the subject. It was a great pleasure for me to meet Yuichi Sakumura and Ricardo Carnieri at Okinawa Computational Neuroscience Course in 2006. I would like to thank Yuichi Sakumura for his ongoing support and friendship. I would also like to thank Ricardo Carnieri for his friendship and the technical talks we had during the hours we spent on the project work. Finally, I would like to dedicate this thesis to my family. They are going to be happier than me seeing this work finished. May 2007 Mete Balcı iii CONTENTS FOREWORD iii