Neural Learning Circuits Utilizing Nano-Crystalline Silicon Transistors and Memristors

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.

I. Introduction

The use of nanoscale memristive devices as synapses in artificial neural circuits has been the subject of a great deal of recent research. Induced by the demonstration of resistive switches by HP Labs [1], a major focus has been on implementing a pair-based spike-timing-dependent plasticity learning mechanism [2]. To date, most solutions involve pulse width or height modulation [3], [4], require extensive circuitry, and fail to mimic neurobiological systems. A more plausible proposal uses specific shapes for bidirectional action potentials to obtain the desired temporally asymmetric characteristics [5]. And although emulation of part of the visual cortex has been reported with this method [6], it is unclear from the device density and power dissipation viewpoints whether a brain-scale system is achievable. In fact, designing layouts to realize the necessary metrics may prove difficult for all silicon complementary metal-oxide semiconductor (CMOS) neuromorphic systems due to their 2-D nature.