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110
The Problem of Sparse Image Coding
 Journal of Mathematical Imaging and Vision
, 2001
"... Linear expansions of images nd many applications in image processing and computer vision. ..."
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Linear expansions of images nd many applications in image processing and computer vision.
Matching Performance of Binary Correlation Matrix Memories
"... We introduce a theoretical framework for estimating the matching performance of binary correlation matrices acting as heteroassociative memories. The framework is applicable to nonrecursive, fullyconnected systems with binary (0,1) Hebbian weights and hardlimited threshold. It can handle both fu ..."
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Cited by 20 (12 self)
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We introduce a theoretical framework for estimating the matching performance of binary correlation matrices acting as heteroassociative memories. The framework is applicable to nonrecursive, fullyconnected systems with binary (0,1) Hebbian weights and hardlimited threshold. It can handle both full and partial matching of single or multiple data items in nonsquare memories. Theoretical development takes place under a probability theory framework. Inherent uncertainties in the matching process are accommodated by the use of probability distributions to describe the numbers of correct and incorrect neuron responses during retrieval. Theoretical predictions are verified experimentally for mediumsized memories and used to aid the design of larger systems. The results highlight the Matching Performance of CMMs 2 fact that correlationbased models can act as highly efficient memories provided a small probability of retrieval error is accepted. Keywords Neural Associative Memories, Co...
Creative Thought as a NonDarwinian Evolutionary Process
 Journal of Creative Behavior
, 2004
"... Selection theory requires multiple, distinct, simultaneouslyactualized states. In cognition, each thought or cognitive state changes the `selection pressure' against which the next is evaluated; they are not simultaneously selected amongst. Creative thought is more a matter of honing in a vagu ..."
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Selection theory requires multiple, distinct, simultaneouslyactualized states. In cognition, each thought or cognitive state changes the `selection pressure' against which the next is evaluated; they are not simultaneously selected amongst. Creative thought is more a matter of honing in a vague idea through redescribing successive iterations of it from different real or imagined perspectives; in other words, actualizing potential through exposure to different contexts. It has been proven that the mathematical description of contextual change of state introduces a nonKolmogorovian probability distribution, and a classical formalism such as selection theory cannot be used. This paper argues that creative thought evolves not through a Darwinian process, but a process of contextdriven actualization of potential.
Selectivity and sparseness in the responses of striate complex cells
, 2005
"... Probability distributions of macaque complex cell responses to a large set of images were determined. Measures of selectivity were based on the overall shape of the response probability distribution, as quantified by either kurtosis or entropy. We call this nonparametric selectivity, in contrast to ..."
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Cited by 15 (0 self)
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Probability distributions of macaque complex cell responses to a large set of images were determined. Measures of selectivity were based on the overall shape of the response probability distribution, as quantified by either kurtosis or entropy. We call this nonparametric selectivity, in contrast to parametric selectivity, which measures tuning curve bandwidths. To examine how receptive field properties affected nonparametric selectivity, two models of complex cells were created. One was a standard Gabor energy model, and the other a slight variant constructed from a Gabor function and its Hilbert transform. Functionally, these models differed primarily in the size of their DC responses. The Hilbert model produced higher selectivities than the Gabor model, with the two models bracketing the data from above and below. Thus we see that tiny changes in the receptive field profiles can lead to major changes in selectivity. While selectivity looks at the response distribution of a single neuron across a set of stimuli, sparseness looks at the response distribution of a population of neurons to a single stimulus. In the model, we found that on average the sparseness of a population was equal to the selectivity of cells comprising that population, a property we call ergodicity. We raise the possibility that high sparseness is the result of distortions in the shape of response distributions caused by nonlinear, informationlosing transforms,
Associative Data Storage and Retrieval in Neural Networks
, 1995
"... Associative storage and retrieval of binary random patterns in various neural net models with onestep thresholddetection retrieval and local learning rules are the subject of this paper. For different heteroassociation and autoassociation memory tasks, specified by the properties of the pattern s ..."
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Cited by 14 (6 self)
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Associative storage and retrieval of binary random patterns in various neural net models with onestep thresholddetection retrieval and local learning rules are the subject of this paper. For different heteroassociation and autoassociation memory tasks, specified by the properties of the pattern sets to be stored and upper bounds on the retrieval errors, we compare the performance of various models of finite as well as asymptotically infinite size. In infinite models, we consider the case of asymptotically sparse patterns, where the mean activity in a pattern vanishes, and study two asymptotic fidelity requirements: constant error probabilities and vanishing error probabilities. A signaltonoise ratio analysis is carried out for one retrieval step where the calculations are comparatively straightforward and easy. As performance measures we propose and evaluate information capacities in bits/synapse which also take into account the important property of fault tolerance. For autoasso...
Aspects of Systems and Circuits for Nanoelectronics
 PROCEEDINGS OF THE IEEE
, 1997
"... This paper analyzes the effect of this technological progress on the design of nanoelectronic circuits and describes computational paradigms revealing novel features such as distributed storage, fault tolerance, selforganization, and local processing. In particular, linear threshold networks, the a ..."
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Cited by 10 (4 self)
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This paper analyzes the effect of this technological progress on the design of nanoelectronic circuits and describes computational paradigms revealing novel features such as distributed storage, fault tolerance, selforganization, and local processing. In particular, linear threshold networks, the associative matrix, selforganizing feature maps, and cellular arrays are investigated from the viewpoint of their potential significance for nanoelectronics. Although these concepts have already been implemented using present technologies, the intention of this paper is to give an impression of their usefulness to system implementations with quantumeffect devices.
The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work).
, 2012
"... ..."
Learning object representations using a priori constraints within ORASSYLL
 Neural Computation
, 2001
"... In this paper a biologically plausible and efficient object recognition system (called ORASSYLL) is introduced which is based on a set of a priori constraints motivated by findings of developmental psychology and neurophysiology. These constraints are concerned with the organisation of the input in ..."
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In this paper a biologically plausible and efficient object recognition system (called ORASSYLL) is introduced which is based on a set of a priori constraints motivated by findings of developmental psychology and neurophysiology. These constraints are concerned with the organisation of the input in local and corresponding entities, the interpretation of the input by its transformation in a highly structured feature space and the evaluation of features extracted from an image sequence by statistical evaluation criteria. In the context of the bias/variance dilemma the functional role of a priori knowledge within ORASSYLL is discussed. In contrast to systems in which object representations are defined manually the introduced constraints allow an autonomous learning from complex scenes. 1
A theory of the cerebral cortex
 In Proceedings of the 1998 International Conference on Neural Information Processing (ICONIP’98), JapaneseNeural NetworkSociety, Kitakyushu, Japan
, 1998
"... A theory of the function of human cerebral cortex and thalamus is sketched. 1. ..."
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A theory of the function of human cerebral cortex and thalamus is sketched. 1.