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74309d7132 Kohonen networks(PDF) 15.1 Self-organization 15.1.1 Charting input space 15.1.2 Topology preserving maps in the brain 15.2 Kohonens model 15.2.1 Learning algorithm 15.2.2 Mapping low dimensional spaces with high-dimensional grids 15.3 Analysis of convergence 15.3.1 Potential function - the one-dimensional case 15.3.2 The two-dimensional case 15.3.3 Effect of a units neighborhood 15.3.4 Metastable states 15.3.5 What dimension for Kohonen networks? 15.4 Applications 15.4.1 Approximation of functions 15.4.2 Inverse kinematics 15.5 Historical and bibliographical remarks 16. The practical details: hardware (optical setups) and software (optical templates) are published. permalink. Hardware for neural networks(PDF) 18.1 Taxonomy of neural hardware 18.1.1 Performance requirements 18.1.2 Types of neurocomputers 18.2 Analog neural networks 18.2.1 Coding 18.2.2 VLSI transistor circuits 18.2.3 Transistors with stored charge 18.2.4 CCD components 18.3 Digital networks 18.3.1 Numerical representation of weights and signals 18.3.2 Vector and signal processors 18.3.3 Systolic arrays 18.3.4 One-dimensional structures 18.4 Innovative computer architectures 18.4.1 VLSI microprocessors for neural networks 18.4.2 Optical computers 18.4.3 Pulse coded networks 18.5 Historical and bibliographical remarks References(PDF) .. They had been investigated during the last years with their practical limitations and considerations yielding the design of the first portable POAC version.
Properties that might be desirable in photonic materials for optical neural networks include the ability to change their efficiency of transmitting light, based on the intensity of incoming light. Introduction: Concepts of neural networks, Characteristics of Neural Networks, Historical Perspective, and Applications of Neural Networks. The backpropagation algorithm(PDF) 7.1 Learning as gradient descent 7.1.1 Differentiable activation functions 7.1.2 Regions in input space 7.1.3 Local minima of the error function 7.2 General feed-forward networks 7.2.1 The learning problem 7.2.2 Derivatives of network functions 7.2.3 Steps of the backpropagation algorithm 7.2.4 Learning with Backpropagation 7.3 The case of layered networks 7.3.1 Extended network 7.3.2 Steps of the algorithm 7.3.3 Backpropagation in matrix form 7.3.4 The locality of backpropagation 7.3.5 An Example 7.4 Recurrent networks 7.4.1 Backpropagation through time 7.4.2 Hidden Markov Models 7.4.3 Variational problems 7.5 Historical and bibliographical remarks 8. Unit 2. Optical interfaces to biological neural networks can be created with optogenetics, but is not the same as an optical neural networks. UNIT 3. However, POAC is a general purpose and programmable array computer that has a wide range of applications including:. This computer networking article is a stub.