IEEE Trans Neural Netw Learn Syst. 2012 Apr;23(4):565-73. doi: 10.1109/TNNLS.2012.2184801.
Properties of neural circuits are demonstrated via SPICE simulations and their applications are discussed. The neuron and synapse subcircuits include ambipolar nano-crystalline silicon transistor and memristor device models based on measured data. Neuron circuit characteristics and the Hebbian synaptic learning rule are shown to be similar to biology. Changes in the average firing rate learning rule depending on various circuit parameters are also presented. The subcircuits are then connected into larger neural networks that demonstrate fundamental properties including associative learning and pulse coincidence detection. Learned extraction of a fundamental frequency component from noisy inputs is demonstrated. It is then shown that if the fundamental sinusoid of one neuron input is out of phase with the rest, its synaptic connection changes differently than the others. Such behavior indicates that the system can learn to detect which signals are important in the general population, and that there is a spike-timing-dependent component of the learning mechanism. Finally, future circuit design and considerations are discussed, including requirements for the memristive device.
通过 SPICE 仿真展示了神经电路的特性，并讨论了它们的应用。神经元和突触子电路包括基于测量数据的双极纳米晶硅晶体管和忆阻器器件模型。神经元电路的特性和赫布学习规则与生物学相似。还介绍了平均发放率学习规则随各种电路参数的变化。然后将这些子电路连接成更大的神经网络，展示了包括联想学习和脉冲吻合检测在内的基本特性。演示了从噪声输入中提取基本频率分量的学习能力。然后表明，如果一个神经元输入的基波与其余部分不同相，其突触连接的变化与其他部分不同。这种行为表明，该系统可以学习检测一般人群中哪些信号是重要的，并且学习机制具有依赖于尖峰时间的成分。最后，讨论了未来的电路设计和考虑因素，包括对忆阻器器件的要求。
