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    Hybrid Neural Networks: Fundamentals and Applications for Interacting Biological Neural Networks with Artificial Neuronal Models
    Hybrid Neural Networks: Fundamentals and Applications for Interacting Biological Neural Networks with Artificial Neuronal Models
    Hybrid Neural Networks: Fundamentals and Applications for Interacting Biological Neural Networks with Artificial Neuronal Models
    Ebook106 pages1 hourArtificial Intelligence

    Hybrid Neural Networks: Fundamentals and Applications for Interacting Biological Neural Networks with Artificial Neuronal Models

    By Fouad Sabry

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    About this ebook

    What Is Hybrid Neural Networks


    The phrase "hybrid neural network" can refer to either biological neural networks that interact with artificial neuronal models or artificial neural networks that also have a symbolic component. Both of these interpretations are possible.


    How You Will Benefit


    (I) Insights, and validations about the following topics:


    Chapter 1: Hybrid neural network


    Chapter 2: Connectionism


    Chapter 3: Computational neuroscience


    Chapter 4: Symbolic artificial intelligence


    Chapter 5: Neuromorphic engineering


    Chapter 6: Recurrent neural network


    Chapter 7: Neural network


    Chapter 8: Neuro-fuzzy


    Chapter 9: Spiking neural network


    Chapter 10: Hierarchical temporal memory


    (II) Answering the public top questions about hybrid neural networks.


    (III) Real world examples for the usage of hybrid neural networks in many fields.


    Who This Book Is For


    Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of hybrid neural networks.


    What Is Artificial Intelligence Series


    The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field.
    The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

    LanguageEnglish
    PublisherOne Billion Knowledgeable
    Release dateJun 20, 2023
    Hybrid Neural Networks: Fundamentals and Applications for Interacting Biological Neural Networks with Artificial Neuronal Models

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      Book preview

      Hybrid Neural Networks - Fouad Sabry

      Chapter 1: Hybrid neural network

      There are two interpretations that may be given to the phrase hybrid neural network:

      Interactions between biological brain networks and artificial neuronal models, and

      Synthetic neural networks that have a symbolic component (or, conversely, symbolic computations with a connectionist part).

      Concerning the first interpretation, the artificial neurons and synapses that make up hybrid networks may either be digital or analog in nature. Voltage clamps are used for the digital version in order to monitor the membrane potential of neurons, computationally mimic artificial neurons and synapses, and activate biological neurons by creating synaptic connections. In the analog version, electrodes are used to link specifically built electrical circuits to a network of live neurons.

      Regarding the second interpretation, the purpose of combining aspects of symbolic computing and artificial neural networks into a single model was an effort to capitalize on the positive aspects of both conceptual frameworks while avoiding the drawbacks of each. Advantages associated with symbolic representations include clear and direct control, rapid initial coding, dynamic variable binding, and knowledge abstraction. On the other hand, representations of artificial neural networks exhibit benefits in terms of biological plausibility, learning, resilience (fault-tolerant processing and graceful decay), and generalization to inputs that are analogous to those previously processed. Since the early 1990s, there have been several efforts made to find a middle ground between the two schools of thought.

      {End Chapter 1}

      Chapter 2: Connectionism

      Connectionism is a term that may refer to both a technique in the area of cognitive science that uses artificial neural networks in an attempt to explain mental events, as well as the theory itself (ANN)

      The fundamental tenet of the connectionist school of thought is that mental processes may be characterized by linked networks of straightforward and often standardized elements. The manner in which the connections and the units are arranged might change from one model to the next. As an example, the nodes in the network might stand in for neurons, and the links between them could stand in for synapses, much as in the human brain.

      The majority of connectionist theories predict that networks will undergo change throughout time. Activation is a feature of connectionist models that is closely connected to another and very often seen. An activation is a numerical number that is meant to represent some feature of a unit at any given moment, and each unit in the network has one. Activations may be found at any time. If the components of the model are neurons, for instance, the activation may stand for the chance that the neuron would send out an action potential spike. In most cases, activation will spread to any and all other units that are linked to it. Spreading activation is a characteristic that can always be found in models of neural networks, and it is quite prevalent in the connectionist models that are used by cognitive psychologists.

      Neural networks are now the connectionist model that are used by far the most often. Even though there is such a wide diversity of neural network models, practically all of them adhere to the same two fundamental assumptions of the human mind:

      An (N-dimensional) vector of numeric activation values across brain units in a network may be used to represent any mental state.

      Changing the intensity of the synaptic connections that take place between brain units is how memory is formed.

      The relative strengths of the connections, either weights or weight, are generally represented as an N×M matrix.

      The majority of the variability that can be seen across models of neural networks derives from:

      Units may either be understood as individual neurons or as groupings of neurons, depending on the context.

      The term activation may be defined in a number of different ways, depending on the context. For instance, in a Boltzmann machine, the activation is understood as the likelihood of producing an action potential spike. This probability is established by applying a logistic function to the total of a unit's inputs in order to arrive at a conclusion.

      The learning algorithm acknowledges that various networks adjust their connections in unique ways. The term learning algorithm is often used to refer to any mathematically specified change in connection weights that occurs over the course of time.

      The majority of connectionists believe that recurrent neural networks, which are directed networks in which the connections of the network may form a directed cycle, are a more accurate representation of the brain than feedforward neural networks (directed networks with no cycles, called DAG). The dynamical systems theory is included into a significant number of recurrent connectionist models. Many academics, including the connectionist Paul Smolensky, have claimed that connectionist models will eventually progress toward methods that are completely continuous, high-dimensional, non-linear, and dynamic.

      Since connectionist work, in general, does not need to be realistic from a biological perspective, it suffers from an absence of neuroscientific plausibility. In light of this, one of the fundamental presumptions underlying connectionist learning techniques receives some biological backing.

      A learning strategy or algorithm, such as Hebbian learning, is used in order to make adjustments to the weights that are included inside a neural network. Therefore, connectionists have developed a great deal of cutting-edge knowledge concerning neural networks' capabilities of learning. Modifying the weights of the connections is an essential part of learning. In general, they require the use of mathematical formulae in order to calculate the change in weights when given sets of data that consist of activation vectors for some subset of the neural units. Numerous research have been devoted to the development of teaching and learning strategies that are based on connectionism.

      It is possible to trace connectionism back to notions that are more than a century old; nevertheless, these ideas were not much more than conjecture until the middle to late 20th century.

      Parallel distributed processing was the initial name given to the connectionist technique that is now the most popular (PDP). The technique was based on artificial neural networks and emphasized the parallel nature

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