Above are papers related to the following paper: [Paper_ID] Paper #330208. 	[Title] Finding Compact and Sparse Distributed Representations of Visual Images [Abstract] Some recent work has investigated the dichotomy between compact coding using dimensionality reduction and sparse distributed coding in the context of understanding biological information processing. We introduce an artificial neural network which self organises on the basis of simple Hebbian learning and negative feedback of activation and show that it is capable of both forming compact codings of data distributions and also of identifying filters most sensitive to sparse distributed codes. The network is extremely simple and its biological relevance is investigated via its response to a set of images which are typical of everyday life. However, an analysis of the network's identification of the filter for sparse coding reveals that this coding may not be globally optimal and that there exists an innate limiting factor which cannot be transcended. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #469504. 	[Title] BCM Network develops Orientation Selectivity and Ocular Dominance in Natural Scene Environment. [Abstract] A two-eye visual environment is used in training a network of BCM neurons. We study the effect of misalignment between the synaptic density functions from the two eyes, on the formation of orientation selectivity and ocular dominance in a lateral inhibition network. The visual environment we use is composed of natural images. We show that for the BCM rule a natural image environment with binocular cortical misalignment is sufficient for producing networks with orientation selective cells and ocular dominance columns. This work is an extension of our previous single cell misalignment model (Shouval et al., 1996). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #5348. 	[Title] Feature Extraction Using an Unsupervised Neural Network [Abstract] A novel unsupervised neural network for dimensionality reduction that seeks directions emphasizing multimodality is presented, and its connection to exploratory projection pursuit methods is discussed. This leads to a new statistical insight into the synaptic modification equations governing learning in Bienenstock, Cooper, and Munro (BCM) neurons (1982). The importance of a dimensionality reduction principle based solely on distinguishing features is demonstrated using a phoneme recognition experiment. The extracted features are compared with features extracted using a back-propagation network. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2696. 	[Title] Objective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability Conditions [Abstract] In this paper, we present an objective function formulation of the BCM theory of visual cortical plasticity that permits us to demonstrate the connection between the unsupervised BCM learning procedure and various statistical methods, in particular, that of Projection Pursuit. This formulation provides a general method for stability analysis of the fixed points of the theory and enables us to analyze the behavior and the evolution of the network under various visual rearing conditions. It also allows comparison with many existing unsupervised methods. This model has been shown successful in various applications such as phoneme and 3D object recognition. We thus have the striking and possibly highly significant result that a biological neuron is performing a sophisticated statistical procedure. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2698. 	[Title] Face Recognition using a Hybrid Supervised/Unsupervised Neural Network [Abstract] A system for automatic face recognition is presented. It consists of several steps; Automatic detection of the eyes and mouth is followed by a spatial normalization of the images. The classification of the normalized images is carried out by a hybrid (supervised and unsupervised) Neural Network. Two methods for reducing the overfitting a common problem in high dimensional classification schemes are presented, and the superiority of their combination is demonstrated. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #99025. 	[Title] Neuronal Goals: Efficient Coding and Coincidence Detection [Abstract] Barlow's seminal work on minimal entropy codes and unsupervised learning is reiterated. In particular, the need to transmit the probability of events is put in a practical neuronal framework for detecting suspicious events. A variant of the BCM learning rule [15] is presented together with some mathematical results suggesting optimal minimal entropy coding. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #43639. 	[Title] Natural image statistics and efficient coding [Abstract] Natural images contain characteristic statistical regularities that set them apart from purely random images. Understanding what these regularities are can enable natural images to be coded more efficiently. In this paper, we describe some of the forms of structure that are contained in natural images, and we show how these are related to the response properties of neurons at early stages of the visual system. Many of the important forms of structure require higher-order (i.e., more than linear, pairwise) statistics to characterize, which makes models based on linear Hebbian learning, or principal components analysis, inappropriate for finding efficient codes for natural images. We suggest that a good objective for an efficient coding of natural scenes is to maximize the sparseness of the representation, and we show that a network that learns sparse codes of natural scenes succeeds in developing localized, oriented, bandpass receptive fields similar to those in the primate striate cortex. Please summarise all the given information.

Paper #330208 has the title `Finding Compact and Sparse Distributed Representations of Visual Images  
` and the abstract `Some recent work has investigated the dichotomy between compact coding using dimensionality reduction and sparse distributed coding in the context of understanding biological information processing. We introduce an artificial neural network which self organises on the basis of simple Hebbian learning and negative feedback of activation and show that it is capable of both forming compact codings of data distributions and also of identifying filters most sensitive to sparse distributed codes. The network is extremely simple and its biological relevance is investigated via its response to a set of images which are typical of everyday life. However, an analysis of the network's identification of the filter for sparse coding reveals that this coding may not be globally optimal and that there exists an innate limiting factor which cannot be transcended. 
`.
Question: Which category should Paper #330208 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #330208 has the title `Finding Compact and Sparse Distributed Representations of Visual Images  
` and the abstract `Some recent work has investigated the dichotomy between compact coding using dimensionality reduction and sparse distributed coding in the context of understanding biological information processing. We introduce an artificial neural network which self organises on the basis of simple Hebbian learning and negative feedback of activation and show that it is capable of both forming compact codings of data distributions and also of identifying filters most sensitive to sparse distributed codes. The network is extremely simple and its biological relevance is investigated via its response to a set of images which are typical of everyday life. However, an analysis of the network's identification of the filter for sparse coding reveals that this coding may not be globally optimal and that there exists an innate limiting factor which cannot be transcended. 
`.
Question: Which category should Paper #330208 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks