Single-electron tunneling depressing synapse for cellular neural networks

In this paper, a cellular neural network with depressing synapses for contrast-invariant pattern classification and synchrony detection is presented, starting from the impulse model of the single-electron tunneling junction. The results of the impulse model and the network are simulated using simulation program with integrated circuit emphasis (SPICE). It is demonstrated that depressing synapses should be an important candidate of robust systems since they exhibit a rapid depression of excitatory postsynaptic potentials for successive presynaptic spikes.

Nanoelectronic Circuit Architectures Based on Single-Electron Turnstiles

Single-electron devices (SEDs) have ultra-low power dissipation and high integration density, which make them promising candidates as basic circuit elements of the next generation VLSI circuits. In this paper, we propose two novel circuit single-electron architectures: the single-electron simulated annealing algorithm (SAA) circuit and the single-electron cellular neural network (CNN). We used the MOSFET-based single-electron turnstile [1] as the basic circuit element. The SAA circuit consists of the voltage-controlled single-electron random number generator [2] and the single-electron multiple-valued memories (SEMVs) [3]. The random-number generation and variable variations in SAA are easily achieved by transferring electrons using the single-electron turnstile. The CNN circuit used the floating-gate single-electron turnstile as the neural synapses, and the number of electrons is used to represent the cells states. These novel circuits are promising in future nanoscale integrated circuits.

连续型联想细胞神经网络局部指数稳定性研究

由于细胞神经网络的潜在应用前景 ,它现已成为神经网络研究的新热点。首先给出连续型联想细胞神经网络的数学模型 ,得到了连续型细胞神经网络平衡点局部指数稳定的充要条件及平衡点指数吸引域的估计 ,研究表明对平衡点的指数吸引域的估计 ,只要计算平衡点处的导算子矩阵的对数范数即可。该研究对连续型联想细胞神经网络的设计和应用均有重要的作用。