A Dynamic System Approach to Spiking Second Order Memristor Networks

• Published in: IEEE transactions on circuits and systems. I, Regular papers Regular papers, 2022-04, Vol.69 (4), p.1641-1654
• Main Authors: Marrone, Francesco, Zoppo, Gianluca, Corinto, Fernando, Gilli, Marco
• Format: Article
• Language: English
• Subjects: Algorithms; Biological system modeling; Calcium; Computational modeling; Dynamical systems; Machine learning; Mathematical models; Memristor; Memristors; Networks; Neuromorphic computing; Nonlinear dynamics; Nonlinear systems; Plastic properties; Spiking; spiking neural networks; STDP; Synapses; Temperature; Voltage
• ISSN: 1549-8328; 1558-0806
• DOI: 10.1109/TCSI.2021.3137713

Second order memristors are two terminal devices that present a conductance depending on two orders of variables, namely the geometric parameters and the internal temperature. They have shown to be able to mimic some specific features of neuron synapses, specifically Spike-Timing-Dependent-Plasticity (STDP), and consequently to be good candidates for neuromorphic computing. In particular, memristor crossbar structures appear to be suitable for implementing locally competitive algorithms and for tackling classification problems by exploiting temporal learning techniques. On the other hand, neuromorphic studies and experiments have revealed the existence of different kinds of plasticity and have shown the effect of calcium concentration on synaptic changes. Computational studies have investigated the behavior of spiking networks in the context of supervised, unsupervised, and reinforcement learning. In this paper, we first derive a simplified, almost analytical, model of a second-order memristor, only involving two variables, the mem-conductance, and the temperature, directly attributable to the synaptic efficacy and to the calcium concentration. Then we study in detail the response of a single memristive synapse to the most relevant plasticity models, including cycles of spike pairs, triplets, and quadruplets at different frequencies. Finally, we accurately characterize memristor spiking networks as discrete nonlinear dynamic systems, with mem-conductances as state variables and pre and postsynaptic spikes as inputs and outputs, respectively. The result shows that the model developed in this manuscript can explain and accurately reproduce a significant portion of observed synaptic behaviors, including those not captured by classical spike pair-based STDP models. Furthermore, under such an approach, the global dynamic behavior of memristor networks and the related learning mechanisms can be deeply analyzed by employing advanced nonlinear dynamic techniques.