Neural Learning Circuits Utilizing Nano-Crystalline Silicon Transistors and Memristors

Abstract

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.

Spatio-temporal pattern recognition in neural circuits with memory-transistor-driven memristive synapses

“…Although ideal memristors are difficult to realize in practice, there are many different material systems and device structures that are close approximations. One of the main advantages of this network design is that circuit parameters are tunable to deal with asymmetric weight changes in non-linear devices [38]. Specifically, tuning can be accomplished by changing the injection voltage Vinj on the drain of the np-TFT (see Table 1) as well as the value of the feedback voltage pulse Vdep.…”

Dynamic evolution evoked by external inputs in memristor-based wavelet neural networks with different memductance functions

“…1 College of Mathematics and Statistics, Hubei Normal University, Huangshi, 435002, China. 2 Institute for Information and System Science, Xi'an Jiaotong University, Xi'an, 710049, China. 3 School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, China.…”

Analysis of Adaptive Synchronization for Stochastic Neutral-Type Memristive Neural Networks with Mixed Time-Varying Delays

“…In 2008, Hewlett-Packard research team [5] obtained a practical memristor device and exhibited its characteristic, such as nanoscale and the memory ability. It has been shown that memristors can be used to work as biological synapses in artificial neural network and replace resistor to simulate the human brain in memristor-based neural networks (MNNs) model, which would benefit many practical applications (see [6,7]). …”