We discuss the implementation of a novel approach to computing named memcomputing using memelements - in particular memcapacitive systems. It is shown that solid-state memcapacitive systems are ideal to implement such a concept since they support information processing and storage at the same physical location at low-energy cost. This feature can be used to perform boolean logic functions “on demand” directly in memory. In this paper, we develop the idea of a memcapacitive dynamic computing random access memory introducing a novel computing scheme based on Voltage Sense Amplifiers, and discussing some features of information storage in internal states of memcapacitive systems in the presence of leakage currents.

Adaptive response to varying environment is a common feature of biological organisms. Reproducing such features in electronic systems and circuits is of great importance for a variety of applications. We consider memory models inspired by an intriguing ability of slime molds to both memorize the period of temperature and humidity variations and anticipate the next variations to come, when appropriately trained. Effective circuit models of such behavior are designed using: 1) a set of LC contours with memristive damping and 2) a single memcapacitive system-based adaptive contour with memristive damping. We consider these two approaches in detail by comparing their results and predictions. Finally, possible biological experiments that would discriminate between the models are discussed. In this paper, we also introduce an effective description of certain memory circuit elements.

Memory effects are ubiquitous in nature and the class of memory circuit elements—which includes memristive, memcapacitive, and meminductive systems—shows great potential to understand and simulate the associated physical processes. Here, we show that such elements can also be used in electronic schemes mimicking biologically inspired computer architectures, performing digital logic and arithmetic operations, and can expand the capabilities of certain quantum computation schemes. In particular, we will discuss some examples where the concept of memory elements is relevant to the realization of associative memory in neuronal circuits, spike-timing-dependent plasticity (STDP) of synapses, and digital and field-programmable quantum computing.

We extend the notion of memristive systems to capacitive and inductive elements, namely, capacitors and inductors whose properties depend on the state and history of the system. All these elements typically show pinched hysteretic loops in the two constitutive variables that define them: current-voltage for thememristor,charge-voltage for thememcapacitor,and current-flux for thememinductor. We argue that these devices are common at the nanoscale, where the dynamical properties of electrons and ions are likely to depend on the history of the system, at least within certain time scales. These elements and their combination in circuits open up new functionalities in electronics and are likely to find applications in neuromorphic devices to simulate learning, adaptive, and spontaneous behavior.