Controlling Ion Conductance and Channels to Achieve Synaptic-like Frequency Selectivity

Memristive Artificial Synapses for Neuromorphic Computing

Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units. Mimicking synaptic functions with these devices is critical in neuromorphic systems. In the last decade, electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions. In this review, these devices are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals. The working mechanisms of the devices are analyzed in detail. This is followed by a discussion of the progress in mimicking synaptic functions. In addition, existing application scenarios of various synaptic devices are outlined. Furthermore, the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.

Phase-change nanoclusters embedded in a memristor for simulating synaptic learning

A type of memristor with a structure of Pd/Nb : AlNO/Pd was designed and fabricated in this work. Its filaments are embedded by phase-change NbO nanoclusters confirmed by the analysis of cross-sectional profiles. The resistive switching mechanism includes the contribution of oxygen vacancy (VO) migration and the structural evolution of phase-change nanoclusters. The system experiences two types of kinetics under external stimulations to replicate the critical dynamics in real synapses: VO migration corresponding to the dynamics of the Ca2+ flux and transmitter release at the pre-synapse, and the phase change of the NbO nanoclusters corresponding to the ionic flux modulated by the post-synaptic potential (current). It was found that the memristor can respond to a set of pulse stimulations in a pattern containing a slow linear increase term and a periodic oscillation term, suggesting that the output signals might be encoded. The simulation of long-term plasticity indicates that the memristor is suitable for diverse learning protocols, including spike-rate dependent plasticity and spike-timing-dependent plasticity. Our work proposes an elementary cell that closely approximates biological synapses and is usable for brain-like computing.

Diverse Synaptic Plasticity Induced by the Interplay of Ionic Polarization and Doping at Salt-Doped Electrolyte/Semiconducting Polymer Interface

Pt/Ca²⁺-polyethylene oxide/polymer poly[3-hexylthiophene-2,5-diyl]/Pt devices were fabricated, and their pulse responses were studied. The discharging peak, represented by the postsynaptic current (PSC), first increases and then decreases with increasing input number in a pulse train. The weight of the PSC decreased for low-frequency stimulations but increased for high-frequency stimulations. However, the peak of the negative differential resistance during the charging process varied following the opposite trend. These behaviors suggested the ability for transferring the signal bidirectionally, confirming the equivalence between the ionic kinetics of our device and the transmitter kinetics of one kind of synapse. A facilitation (F)-depression (D) interplay model corresponding to the ionic polarization and doping interplay at the electrolyte/semiconducting polymer interface was adopted to successfully mimic the weight modification of the PSC. The simulation results showed that the observed synaptic plasticity was caused by the great disparity between the recovery time constants of F and D (τ _F and τ _D ). Moreover, such an interplay could inspire the features of responses to post-tetanic stimulations. Our study suggested a means to realize synaptic computation.

Spatial summation of the short-term plasticity of a pair of organic heterogeneous junctions

Short-term plasticity of a pair of organic heterogeneous junctions could be linearly summed from those of the two sources.

Simulation of synaptic short-term plasticity using Ba(CF3SO3)2-doped polyethylene oxide electrolyte film

The simulation of synaptic plasticity using new materials is critical in the study of brain-inspired computing. Devices composed of Ba(CF3SO3)2-doped polyethylene oxide (PEO) electrolyte film were fabricated and with pulse responses found to resemble the synaptic short-term plasticity (STP) of both short-term depression (STD) and short-term facilitation (STF) synapses. The values of the charge and discharge peaks of the pulse responses did not vary with input number when the pulse frequency was sufficiently low(~1 Hz). However, when the frequency was increased, the charge and discharge peaks decreased and increased, respectively, in gradual trends and approached stable values with respect to the input number. These stable values varied with the input frequency, which resulted in the depressed and potentiated weight modifications of the charge and discharge peaks, respectively. These electrical properties simulated the high and low band-pass filtering effects of STD and STF, respectively. The simulations were consistent with biological results and the corresponding biological parameters were successfully extracted. The study verified the feasibility of using organic electrolytes to mimic STP.

Effect of heavy-ion on frequency selectivity of semiconducting polymer/electrolyte heterojunction

Long-term bidirectional frequency selectivity has been achieved in MEH-PPV/PEO–Nd³⁺cells, which suggests spike-rate-dependent plasticity learning protocol. It depends on pulse shape due to variation of ionic type.