This paper surveys the extensive usage of pulse coupled neural networks. The visual cortex system of mammalians was the backbone for the development of pulse coupled neural network. PCNN (Pulse Coupled Neural Networks) is unique from other techniques due to its synchronous pulsed output, adjustable threshold and controllable parameters. is Hence the uniqueness of this network utilized in the fields of image processing. The basic model of PCNN and the consecutive changes implemented, to strengthen the pulse coupled neural network are discussed initially. Then the applications of PCNN are broadly discussed. The other miscellaneous applications utilizing pulse coupled neural networks are thrown light in the last section.

Review: Pulse coupled neural networks and its applications

http://www2.tissch.ir/fa/07706e6de5d8dfb7c726f3027e4a50c4.pdf

A novel algorithm for all pairs shortest path problem based on matrix multiplication and pulse coupled neural network

This paper proposes a novel technique to deal with pose variations in 3D object recognition. This technique uses pulse-coupled neural network (PCNN) for image features generation from two different viewing angles. These signatures qualities are then evaluated, using a proposed fitness function. The features evaluation step is taken before any classification steps are performed. The evaluation results dynamic weighting factors for each camera express the features quality from the current viewing angles. The proposed technique uses the two 2D image features and their corresponding calculated weighting factors to construct optimized quality 3D features. An experiment was conducted in Arabic sign language recognition application which multiple views are necessary to distinguish some signs. The proposed technique obtained a 96% recognition accuracy for pose-invariant restrictions with a degree of freedom from 0 to 90.

A proposed PCNN features quality optimization technique for pose-invariant 3D Arabic sign language recognition

Boundedness and periodicity for linear threshold discrete-time quaternion-valued neural network with time-delays

This paper develops a neural network architecture and a new processing method for solving in real time, the nonlinear equality constrained multiobjective optimization problem (NECMOP), where several nonlinear objective functions must be optimized in a conflicting situation In this processing method, the NECMOP is converted to an equivalent scalar optimization problem (SOP) The SOP is then decomposed into several-separable subproblems processable in parallel and in a reasonable time by multiplexing switched capacitor circuits The approach which we propose makes use of a decomposition–coordination principle that allows nonlinearity to be treated at a local level and where coordination is achieved through the use of Lagrange multipliers The modularity and the regularity of the neural networks architecture herein proposed make it suitable for very large scale integration implementation An application to the resolution of a physical problem is given to show that the approach used here possesses some advantages of the point of algorithmic view, and provides processes of resolution often simpler than the usual techniques

Solving Nonlinear Equality Constrained Multiobjective Optimization Problems Using Neural Networks

Switching analysis of 2-D neural networks with nonsaturating linear threshold transfer functions

Multistable neural networks have attracted much interests in recent years, since the monostable networks are computationally restricted. This paper studies a N linear threshold neurons recurrent networks without Self-Excitatory connections. Our studies show that this network performs a Winner-Take-All (WTA) behavior, which has been recognized as a basic computational model done in brain. The contributions of this paper are: (1) It proves by mathematics that the proposed model is Non-Divergent. (2) An important implication (Winner-Take-All) of the proposed network model is studied. (3) Digital computer simulations are carried out to validate the performance of the theory findings.

A Winner-Take-All Neural Networks of N Linear Threshold Neurons without Self-Excitatory Connections

Shortest path tree (SPT) computation is a critical issue in many real world problems, such as routing in networks. It is also a constrained optimization problem, which has been studied by many authors in recent years. Typically, it is solved by heuristic algorithms, such as the famous Dijkstra's algorithm, which can quickly provide a good solution in most instances. However, with the scale of problem increasing, these methods are inefficient and may consume a considerable amount of CPU time. Neural networks, which are massively parallel models, can solve this question easily. This paper presents an efficient modified continued pulse coupled neural network (MCPCNN) model for SPT computation in a large scale instance. The proposed model is topologically organized with only local lateral connections among neurons. The start neuron fires first, and then the firing event spreads out through the lateral connections among the neurons, like the propagation of a wave. Each neuron records its parent, that is, the neighbor which caused it to fire. It proves that the generated wave in the network spreads outward with travel times proportional to the connection weight between neurons. Thus, the generated path is always the global optimal shortest path from the source to all destinations. The proposed model is also applied to generate SPTs for a real given graph step by step. The effectiveness and efficiency of the proposed approach is demonstrated through simulation and comparison studies.

A novel neural network method for shortest path tree computation

Poker game has become one of the most prevailing benchmark environment to discover algorithms for sequential games with imperfect information (SGII). However, in games with large state space, it is hard to traverse the whole game tree. This is because the space of history is exponentially increasing with the input size of the game. Other attempts like truncating the game tree with certain length have also been made to solve this problem. But determine the most suitable length could require enormous amount of resources. All of these obstacles make algorithms for SGII much harder to design. To solve this kind of problem, we propose the adaptive memory sampling method which aims to find the distribution of the sampling length by using posterior sampling to update it iteratively. In the real-world human interaction, to what extent a human memory can last often varies significantly depending on the importance of the interaction trajectory. So we also adopted the Long Short-Term Memory (LSTM) network as the sub-procedure to classify the histories and making prediction of future game states and actions based on historical sampled data. According to our theoretical analysis, our method performs better than the state-of-the-art algorithms. On the other hand, The empirical results support our results.

Solving Poker Games Efficiently: Adaptive Memory based Deep Counterfactual Regret Minimization

We consider the trade-off problem between exploration and exploitation under finite discounted Markov Decision Process, where the state transition matrix of the underlying environment stays unknown. We propose a double Thompson sampling reinforcement learning algorithm(DTS) to solve this kind of problem. This algorithm achieves a total regret bound of Õ(D √ SAT ) in time horizon T with S states, A actions and diameter D. DTS consists of two parts, the first part is the traditional part where we apply the posterior sampling method on transition matrix based on prior distribution. In the second part, we employ a count-based posterior update method to balance between the local optimal action and the longterm optimal action in order to find the global optimal game value. We established a regret bound of Õ( √ T/S). Which is by far the best regret bound for finite discounted Markov Decision Process to our knowledge. Numerical results proves the efficiency and superiority of our approach.

Double Thompson Sampling in Finite stochastic Games

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