Recent Methodology in Connectionist Systems

In this research project, the features of biological and artificial neural networks were studied by reviewing the existing works of authorities in print and electronics on biological and artificial neural networks. The features were then assessed and evaluated and comparative analysis of the two networks was carried out. The metrics such as structures, layers, size and functional capabilities of neurons, learning capabilities, style of computation, processing elements, processing speed, connections, strength, information storage, information transmission, communication media selection, signal transduction and fault tolerance were used as basis for comparison. A major finding in the research showed that artificial neural networks served as the platform for neuro-computing technology and as such a major driver of the development of neuron-like computing system. It was also discovered that Information processing of the future computer systems will greatly be influenced by the adoption of artificial neural network model.

Russian Journal of Genetics: Applied Research, 2015

Despite the continuous growth of our knowledge of the functioning of nervous systems and sophisticated features of the neuroanatomy and neurophysiology of various animal species, the basic mech anisms that provide for such properties as the ability to learn, use memory, recognize patterns, and learn about the world are poorly understood. In this paper, we present an overview of artificial devices that model the brain and solve such cognitive tasks as navigation, pattern recognition, routing, and target site finding. We discuss both hybrid systems (hybrots), in which living neural networks control an artificial body, and systems in which such an artificial body is controlled by computer programs based on different models of the brain and its regions (animats). Two basic types of hybrid systems are considered: those in which the robot is connected to the brain of a living body, such as a rat, and those in which information is received from neurons taken from the body or neurons cultured on a microelectrode array detecting their electrical potentials. Among the com putational approaches that simulate nervous systems of living organisms, we can mark out the Darwin family of devices based on the theory of neuronal group selection (TNGS). In addition, we consider papers in which animats solve navigation tasks using different models of the rat hippocampus, based on such modeling meth ods as cognitive graph, view cells, place cells, and experience cells. The approaches under consideration pro vide researchers with new tools to analyze basic principles of neuron interaction between each other and with the outside world, the principles that provide higher brain functions.

Computational and Mathematical Methods in Medicine, 2012

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. of Hindawi Publishing Corporation

2011

We have proposed the glial network which was inspired from the feature of glias. The glias are nervous cell existing in the brain and transmit signal each other like neurons. In the glial network, the glias connect to the neurons and other glias. The glias trade information each other by this network. In this article, we investigate the glial network when only one glia is stimulated by an external noise. The external noise propagates the glial network and influences to the Multi-Layer Perceptron (MLP). By the effect of the one-way influence via the glial network, a kind of position-dependingfeature appears in the MLP. The simulation results show that the proposed network possesses better learning performance, more biased anti-damaging property, and better generalization capability than the conventional networks.

2012 IEEE Asia Pacific Conference on Circuits and Systems, 2012

A glia is the most number of nervous cells in a brain. The glia is investigated in a medical field, because the glia correlates to neuron works and composes a human cerebration. We consider that the glia function can be applied to an artificial neural network. In this study, we propose the Multi-Layer Perceptron (MLP) with the two different pulse glial networks. The proposed MLP has the glial network which is inspired from biological functions of the glia. One neuron is connected with two glias. Two glias generate the pulse depending on the output neurons. One glia connects the neuron for increasing the output of neuron. On the other hand, the glia connects the neuron for decreasing the output of neuron. Both glias composes the glial networks. These effects are propagated into the networks. The glial effects become complexity and affects the MLP learning performance. By the computer simulation, we confirm that the learning performance of the proposed MLP is better than the conventional MLP.

2008

Abstract—The latest advances in the gridbrain agent brain model are presented. The gridbrain models brains as networks of computational components. The components are used as building blocks for computation, and provide base functionalities like: input/output, boolean logic, arithmetic, clocks and memory. The multi-grid architecture as a way to process variable sized information from different sensory channels is addressed.

Ever since the publication of Santiago Ramón y Cajal's drawings of neurons - in his words, those "mysterious butterflies of the soul" - it has been clear that the nervous system is composed of a large number of such cells connected to one another to form a network. Long axons, ending in terminals which form synapses to the dendrites which branch out from neighbouring neurons, transmit bursts of electric current and enable neurons somehow to cooperate and yield the astonishing emergent phenomenon known as thought.

In this research project, the features of biological and artificial neural networks were studied by reviewing the existing works of authorities in print and electronics on biological and artificial neural networks. The features were then assessed and evaluated and comparative analysis of the two networks was carried out. The metrics such as structures, layers, size and functional capabilities of neurons, learning capabilities, style of computation, processing elements, processing speed, connections, strength, information storage, information transmission, communication media selection, signal transduction and fault tolerance were used as basis for comparison. A major finding in the research showed that artificial neural networks served as the platform for neuro-computing technology and as such a major driver of the development of neuron-like computing system. It was also discovered that Information processing of the future computer systems will greatly be influenced by the adoption of artificial neural network model.

2010

The first few pages of any good introductory book on neurocomputing contain a cursory description of neurophysiology and how it has been abstracted to form the basis of artificial neural networks as we know them today. In particular, artificial neurons simplify considerably the behavior of their biological counterparts. It is our view that in order to gain a better understanding of how biological systems learn and remember it is necessary to have accurate models on which to base computerized experimentation. In this paper we describe an artificial neuron that is more realistic than most other models used currently. The model is based on conventional artificial neural networks (and is easily computerized) and is currently being used in our investigations into learning and memory.