Yuko Osana

Chaotic bidirectional associative memory

A chaotic bidirectional associative memory (CBAM) is proposed and simulated. It can deal with one-to-many associations such as {(A,a), (A,b), (A,c),/spl middot//spl middot//spl middot/}. In the CBAM, in order to enable one-to-many associations, each training pair is memorized together with its own contextual information and chaotic neurons are employed in a part of the network corresponding to the contextual information. Since the chaotic neurons change their states by chaos, the one-to-many associations can be realized in the CBAM.

Separation of superimposed pattern and many-to-many associations by chaotic neural networks

We propose a chaotic associative memory (CAM). It has two distinctive features: 1) it can recall correct stored patterns from superimposed input; and 2) it can deal with many-to-many associations. As for the first feature, when a stored pattern is given to the conventional chaotic neural network as an external input, the input pattern is continuously searched. The proposed model makes use of the above property to separate the superimposed patterns. As for the second feature, most of the conventional associative memories cannot deal with many-to-many associations due to the superimposed pattern caused by the stored common data. However, since the proposed model can separate the superimposed pattern, it can deal with many-to-many associations. A series of computer simulations shows the effectiveness of the proposed model.

Geometrical structure of the neuronal network of Caenorhabditis elegans

The neuronal network of the soil nematode Caenorhabditis elegans (C. elegans), which is a good prototype for biological studies, is investigated. Here, the neuronal network is simplified as a graph. We use three indicators to characterize the graph; vertex degree, generalized eccentricity (GE), and complete subgraphs. The graph has the central part and the strong clustering structure. We present a simple model, which shows that the neuronal network has a high-dimensional geometrical structure.

Successive learning in chaotic neural network

In this paper, we propose a successive learning method in a chaotic neural network using a continuous pattern input. It can distinguish an unknown pattern from the stored known patterns and learn the unknown pattern successively. In the proposed model, it makes use of the difference in the response to the input pattern in order to distinguish an unknown pattern from the stored known patterns. When an input pattern is regarded as an unknown pattern, the pattern is memorized. Furthermore, it can estimate and learn a correct pattern from a noisy unknown pattern or an incomplete unknown pattern by considering the temporal summation of the continuous pattern input. In addition, similarity to the physiological facts in the olfactory bulb of a rabbit found by Freeman (1991) is observed in the behavior of the proposed model. A series of computer simulations shows the effectiveness of the proposed model.

SUCCESSIVE LEARNING IN HETERO-ASSOCIATIVE MEMORY USING CHAOTIC NEURAL NETWORKS

In this paper, we propose a successive learning method in hetero-associative memories, such as Bidirectional Associative Memories and Multidirectional Associative Memories, using chaotic neural networks. It can distinguish unknown data from the stored known data and can learn the unknown data successively. The proposed model makes use of the difference in the response to the input data in order to distinguish unknown data from the stored known data. When input data is regarded as unknown data, it is memorized. Furthermore, the proposed model can estimate and learn correct data from noisy unknown data or incomplete unknown data by considering the temporal summation of the continuous data input. In addition, similarity to the physiological facts in the olfactory bulb of a rabbit found by Freeman are observed in the behavior of the proposed model. A series of computer simulations shows the effectiveness of the proposed model.

Chaotic multidirectional associative memory

A chaotic multidirectional associative memory (CMAM) is proposed and simulated. It can deal with many-to-many associations and the structure is very simple. Furthermore, similarity to a psychological fact (priming effect) is observed in the association of the CMAM. In order to enable many-to-many associations, the CMAM memorizes each training data together with its own contextual information and employs chaotic neurons. Since the chaotic neurons change their states by chaos, many-to-many associations can be realized in the CMAM.

Native Response of C. elegans Encoded in Its Neuron Network

For the physical study of the native responses of Caenorhabditis elegans ( C. elegans ), probability distribution of random walkers on the neuron graph is studied. Here, the neuron graph is a graph which represents the synaptic connection of neurons of the worm. Connection of a sensory neuron to various motor neurons are represented by an index named accessibility. Accessibilities from 39 sensory neurons to all of motor neurons are computed and the formers are classified into three groups. It is found that this classification coincides with the grouping by native responses caused by those sensory neurons. This coincidence implies the usefulness of the random walker model for the analysis of the neural network. The connectivity represented by the major paths of random walkers is a significant factor to interpret relation between external disturbance and resultant movement in the native response of the worm. This native response of the worm is encoded by the connectivity of vertices in the neuron graph.For the physical study of the native responses of Caenorhabditis elegans (C. elegans), probability distribution of random walkers on the neuron graph is studied. Here, the neuron graph is a graph which represents the synaptic connection of neurons of the worm. Connection of a sensory neuron to various motor neurons are represented by an index named accessibility. Accessibilities from 39 sensory neurons to all of motor neurons are computed and the formers are classified into three groups. It is found that this classification coincides with the grouping by native responses caused by those sensory neurons. This coincidence implies the usefulness of the random walker model for the analysis of the neural network. The connectivity represented by the major paths of random walkers is a significant factor to interpret relation between external disturbance and resultant movement in the native response of the worm. This native response of the worm is encoded by the connectivity of vertices in the neuron graph.

Knowledge processing system using improved chaotic associative memory

In this paper, we propose a knowledge processing system using improved chaotic associative memory (KPICAM). The proposed KPICAM is based on an improved chaotic associative memory (ICAM) composed of chaotic neurons. In the conventional chaotic neural network, when a stored pattern is given to the network as an external input continuously, around the input pattern is searched. The ICAM makes use of this property in order to separate superimposed patterns and to deal with many-to-many associations. In this research, the ICAM is applied to knowledge processing in which the knowledge is represented in a form of semantic network. The proposed KPICAM has the following features: (1) it can deal with the knowledge which is represented in a form of semantic network; (2) it can deal with characteristics inheritance; (3) it is robust for noisy input. A series of computer simulations shows the effectiveness of the proposed system.

Solving the binding problem with feature integration theory

We propose a neural network model of visual system based on the feature integration theory. The proposed model has a structure based on the hierarchical structure of visual system and selectiveness of information by visual attention. The proposed model consists of two stages: the feature recognition stage and the feature integration stage. In the feature recognition stage, there are two modules: the form recognition module and the color recognition module. In these modules, information of form and color is separately processed in parallel. The form recognition module is constructed using the neocognitron, and the color recognition module is based on the LVQ neural network. The feature integration stage is based on the feature integration theory, which is a representative theory for explaining all phenomena occurring in visual system as a consistent process. We carried out computer simulations and confirmed that the proposed model can recognize plural objects and solve the binding problem.

Chaotic associative memory for sequential patterns

We propose a chaotic associative memory for sequential patterns (CAMSP). The proposed CAMSP is based on a chaotic associative memory composed of chaotic neurons. In the conventional chaotic neural network, when a stored pattern is given to the network as an external input continuously, the input pattern is searched. The CAM makes use of this property in order to separate the superimposed patterns. In this research, the CAM is applied to associations for sequential patterns. The proposed model has the following features: 1) it can deal with associations for the sequential patterns; 2) it can realize associations by considering patterns history; and 3) it is robust for noisy input. A series of computer simulations shows the effectiveness of the proposed model.