2014 International Joint Conference on Neural Networks (IJCNN), 2014
The property of changing resistance according to applied currents of memristors makes them candid... more The property of changing resistance according to applied currents of memristors makes them candidates for emulating synapses in artificial neural networks. In this paper, we introduce a memristive synapse design into neural network circuits. Combined with modified integrate-andfire (I&F) complementary metal-oxide-semiconducter (CMOS) neurons, the memristive neural network shows similarities to its biological counterpart, in respect of biologically realistic, current-controlled spikes and adaptive synaptic plasticity. Then, the spike-rate-dependent plasticity (SRDP) of the synapse, an extended protocol of the Hebbian learning rule, is originally implemented by the circuit. And some advanced neural activities including learning, associative memory and forgetting are realized based on the SRDP rule. These activities are comprehensively validated on a neural network circuit inspired by famous Pavlov's dog-experiment with simulations and quantitative analyses.
This paper investigates the problem of exponential lag synchronization control of memristive neur... more This paper investigates the problem of exponential lag synchronization control of memristive neural networks (MNNs) via the fuzzy method and applications in pseudorandom number generators. Based on the knowledge of memristor and recurrent neural networks, the model of MNNs is established. Then, considering the state-dependent properties of memristor, a fuzzy model of MNNs is employed to provide a new way of analyzing the complicated MNNs with only two subsystems, and update laws for the connection weights of slave systems and controller gain are designed to make the slave systems exponentially lag synchronized with the master systems. Two examples about synchronization problems are presented to show the effectiveness of the obtained results, and an application of the obtained theory is also given in the pseudorandom number generator.
2014 International Joint Conference on Neural Networks (IJCNN), 2014
The property of changing resistance according to applied currents of memristors makes them candid... more The property of changing resistance according to applied currents of memristors makes them candidates for emulating synapses in artificial neural networks. In this paper, we introduce a memristive synapse design into neural network circuits. Combined with modified integrate-andfire (I&F) complementary metal-oxide-semiconducter (CMOS) neurons, the memristive neural network shows similarities to its biological counterpart, in respect of biologically realistic, current-controlled spikes and adaptive synaptic plasticity. Then, the spike-rate-dependent plasticity (SRDP) of the synapse, an extended protocol of the Hebbian learning rule, is originally implemented by the circuit. And some advanced neural activities including learning, associative memory and forgetting are realized based on the SRDP rule. These activities are comprehensively validated on a neural network circuit inspired by famous Pavlov's dog-experiment with simulations and quantitative analyses.
This paper investigates the problem of exponential lag synchronization control of memristive neur... more This paper investigates the problem of exponential lag synchronization control of memristive neural networks (MNNs) via the fuzzy method and applications in pseudorandom number generators. Based on the knowledge of memristor and recurrent neural networks, the model of MNNs is established. Then, considering the state-dependent properties of memristor, a fuzzy model of MNNs is employed to provide a new way of analyzing the complicated MNNs with only two subsystems, and update laws for the connection weights of slave systems and controller gain are designed to make the slave systems exponentially lag synchronized with the master systems. Two examples about synchronization problems are presented to show the effectiveness of the obtained results, and an application of the obtained theory is also given in the pseudorandom number generator.
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Papers by Yide Zhang