Spike-timing-dependent Plasticity Rule Research Articles

Optimizing skyrmionium movement and stability via stray magnetic fields in trilayer nanowire constructs.

Skyrmioniums, known for their unique transport and regulatory properties, are emerging as potential cornerstones for future data storage systems. However, the stability of skyrmionium movement faces considerable challenges due to the skyrmion Hall effect, which is induced by deformation. In response, our research introduces an innovative solution: we utilized micro-magnetic simulations to create a sandwiched trilayer nanowire structure augmented with a stray magnetic field. This combination effectively guides the skyrmionium within the ferromagnetic (FM) layer. Our empirical investigations reveal that the use of a stray magnetic field not only reduces the size of the skyrmionium but also amplifies its stability. This dual-effect proficiently mitigates the deformation of skyrmionium movement and boosts their thermal stability. We find these positive outcomes are most pronounced at a particular intensity of the stray magnetic field. Importantly, the required stray magnetic field can be generated using a heavy metal (HM1) layer of suitable thickness, rendering the practical application of this approach plausible in real-world experiments. Additionally, we analyze the functioning mechanism based on the Landau-Lifshitz-Gilbert (LLG) equation and energy variation. We also develop a deep spiking neural network (DSNN), which achieves a remarkable recognition accuracy of 97%. This achievement is realized through supervised learning via the spike timing dependent plasticity rule (STDP), considering the nanostructure as an artificial synapse device that corresponds to the electrical properties of the nanostructure. In conclusion, our study provides invaluable insights for the design of innovative information storage devices utilizing skyrmionium technology. By tackling the issues presented by the skyrmion Hall effect, we outline a feasible route for the practical application of this advanced technology. Our research, therefore, serves as a robust platform for continued investigations in this field.

Self‐assembled vapor-transport-deposited SnS nanoflake-based memory devices with synaptic learning properties

The most salient features of resistive switching (RS) devices are low energy consumption, fast switching speed, and high-density integration, which render them promising candidates for realizing non-volatile memory and artificial synaptic devices. However, the growth of functional switching layers for RS devices needs innovative deposition techniques. Herein, we utilize a high-throughput vapor-transport-deposition (VTD) technique for synthesizing self-assembled tin-sulfide (SnS) nanoflakes, which are then used as a switching layer to fabricate an RS device. First principle calculations are conducted to understand the optoelectronic properties of SnS by employing density functional theory. The proposed Ag/SnS/Pt memory device exhibits substantial merits, including low-switching voltages (VSET: 0.22 V and VRESET: −0.20 V), suitable ON/OFF ratio (∼259), excellent endurance (106), and extended memory retention (106 s) characteristics. In addition, RS stochasticity is modeled using statistical time-series analysis via Holt’s exponential smoothing. Interestingly, the device can emulate multiple synaptic functionalities, including potentiation, depression, paired-pulse facilitation, paired-pulse depression, excitatory postsynaptic current, inhibitory postsynaptic current, and advanced spike-timing dependent plasticity rules. Moreover, the proposed synaptic device can detect the edge of images by utilizing a convolutional neural network. The unique and efficient VTD-SnS-based device will be a potential candidate for high-density non-volatile memory and neuromorphic computing applications.

A Memristor-Based Learning Engine for Synaptic Trace-Based Online Learning.

The memristor has been extensively used to facilitate the synaptic online learning of brain-inspired spiking neural networks (SNNs). However, the current memristor-based work can not support the widely used yet sophisticated trace-based learning rules, including the trace-based Spike-Timing-Dependent Plasticity (STDP) and the Bayesian Confidence Propagation Neural Network (BCPNN) learning rules. This paper proposes a learning engine to implement trace-based online learning, consisting of memristor-based blocks and analog computing blocks. The memristor is used to mimic the synaptic trace dynamics by exploiting the nonlinear physical property of the device. The analog computing blocks are used for the addition, multiplication, logarithmic and integral operations. By organizing these building blocks, a reconfigurable learning engine is architected and realized to simulate the STDP and BCPNN online learning rules, using memristors and 180 nm analog CMOS technology. The results show that the proposed learning engine can achieve energy consumption of 10.61 pJ and 51.49 pJ per synaptic update for the STDP and BCPNN learning rules, respectively, with a 147.03× and 93.61× reduction compared to the 180 nm ASIC counterparts, and also a 9.39× and 5.63× reduction compared to the 40 nm ASIC counterparts. Compared with the state-of-the-art work of Loihi and eBrainII, the learning engine can reduce the energy per synaptic update by 11.31× and 13.13× for trace-based STDP and BCPNN learning rules, respectively.

A new pre-conditioned STDP rule and its hardware implementation in neuromorphic crossbar array

This paper proposes a new pre-conditioned spike-timing-dependent plasticity (STDP) learning rule as well as an efficient system-level time-domain circuit modeling and simulation method for the whole crossbar array structure. First, the new pre-conditioned STDP rule is conceived by exercising a slight perturbation to the conventional STDP curve. Software-wise, it can be described in spiking neural network (SNN) codes, while hardware-wise, it is realized by the memristor crossbar array in conjunction with specific spike signals, just as the conventional STDP. The new STDP rule enables the neural network in a neuromorphic chip to achieve better performance both in terms of software and hardware implementation compared with the conventional STDP rule. Second, a time-domain circuit modeling method is proposed for the simulation of the whole crossbar array. Numerical experiments demonstrate that the new pre-conditioned STDP is superior to the conventional STDP both in terms of accuracy and low power consumption. At the software level, a 784 × 100 spiking neural network, trained by the pre-conditioned STDP, for the MNIST handwritten digit recognition achieves an accuracy of 85.90%, which is 3.09% higher than that trained by the conventional STDP. At the hardware level, a downscaled 196 × 10 crossbar array is simulated and trained. The crossbar array trained by the new STDP consumes 21.5% less power than that trained by the conventional STDP. Moreover, it does not compromise the original robustness when subject to non-ideal characteristics such as variation of conductance and resistance of the interconnect in the crossbar array.

Spike timing-dependent plasticity alters electrosensory neuron synaptic strength in vitro but does not consistently predict changes in sensory tuning in vivo.

How do sensory systems optimize detection of behaviorally relevant stimuli when the sensory environment is constantly changing? We addressed the role of spike timing-dependent plasticity (STDP) in driving changes in synaptic strength in a sensory pathway and whether those changes in synaptic strength could alter sensory tuning. It is challenging to precisely control temporal patterns of synaptic activity in vivo and replicate those patterns in vitro in behaviorally relevant ways. This makes it difficult to make connections between STDP-induced changes in synaptic physiology and plasticity in sensory systems. Using the mormyrid species Brevimyrus niger and Brienomyrus brachyistius, which produce electric organ discharges for electrolocation and communication, we can precisely control the timing of synaptic input in vivo and replicate these same temporal patterns of synaptic input in vitro. In central electrosensory neurons in the electric communication pathway, using whole cell intracellular recordings in vitro, we paired presynaptic input with postsynaptic spiking at different delays. Using whole cell intracellular recordings in awake, behaving fish, we paired sensory stimulation with postsynaptic spiking using the same delays. We found that Hebbian STDP predictably alters sensory tuning in vitro and is mediated by NMDA receptors. However, the change in synaptic responses induced by sensory stimulation in vivo did not adhere to the direction predicted by the STDP observed in vitro. Further analysis suggests that this difference is influenced by polysynaptic activity, including inhibitory interneurons. Our findings suggest that STDP rules operating at identified synapses may not drive predictable changes in sensory responses at the circuit level.NEW & NOTEWORTHY We replicated behaviorally relevant temporal patterns of synaptic activity in vitro and used the same patterns during sensory stimulation in vivo. There was a Hebbian spike timing-dependent plasticity (STDP) pattern in vitro, but sensory responses in vivo did not shift according to STDP predictions. Analysis suggests that this disparity is influenced by differences in polysynaptic activity, including inhibitory interneurons. These results suggest that STDP rules at synapses in vitro do not necessarily apply to circuits in vivo.

Spike timing-dependent plasticity under imbalanced excitation and inhibition reduces the complexity of neural activity.

Excitatory and inhibitory neurons are fundamental components of the brain, and healthy neural circuits are well balanced between excitation and inhibition (E/I balance). However, it is not clear how an E/I imbalance affects the self-organization of the network structure and function in general. In this study, we examined how locally altered E/I balance affects neural dynamics such as the connectivity by activity-dependent formation, the complexity (multiscale entropy) of neural activity, and information transmission. In our simulation, a spiking neural network model was used with the spike-timing dependent plasticity rule to explore the above neural dynamics. We controlled the number of inhibitory neurons and the inhibitory synaptic weights in a single neuron group out of multiple neuron groups. The results showed that a locally increased E/I ratio strengthens excitatory connections, reduces the complexity of neural activity, and decreases information transmission between neuron groups in response to an external input. Finally, we argued the relationship between our results and excessive connections and low complexity of brain activity in the neuropsychiatric brain disorders.

Coherence resonance and stochastic synchronization in a small-world neural network: an interplay in the presence of spike-timing-dependent plasticity

Coherence resonance (CR), stochastic synchronization (SS), and spike-timing-dependent plasticity (STDP) are ubiquitous dynamical processes in biological neural networks. Whether there exists an optimal network and STDP configuration at which CR and SS are both pronounced is a fundamental question of interest that is still elusive. We expect such a configuration to enable the brain to make synergistic and optimal use of these phenomena to process information efficiently. This paper considers a small-world network of excitable Hodgkin–Huxley neurons driven by channel noise and STDP with an asymmetric Hebbian time window. Numerical results indicate specific network topology and STDP parameter intervals in which CR and SS can be simultaneously enhanced. Our results imply that an optimally tuned inherent background noise, STDP rule, and network topology can play a constructive role in enhancing both the time precision of firing and the synchronization in neural systems.

A Hebbian Approach to Non-Spatial Prelinguistic Reasoning.

This research integrates key concepts of Computational Neuroscience, including the Bienestock-CooperMunro (BCM) rule, Spike Timing-Dependent Plasticity Rules (STDP), and the Temporal Difference Learning algorithm, with an important structure of Deep Learning (Convolutional Networks) to create an architecture with the potential of replicating observations of some cognitive experiments (particularly, those that provided some basis for sequential reasoning) while sharing the advantages already achieved by the previous proposals. In particular, we present Ring Model B, which is capable of associating visual with auditory stimulus, performing sequential predictions, and predicting reward from experience. Despite its simplicity, we considered such abilities to be a first step towards the formulation of more general models of prelinguistic reasoning.

Toward Learning in Neuromorphic Circuits Based on Quantum Phase Slip Junctions.

We explore the use of superconducting quantum phase slip junctions (QPSJs), an electromagnetic dual to Josephson Junctions (JJs), in neuromorphic circuits. These small circuits could serve as the building blocks of neuromorphic circuits for machine learning applications because they exhibit desirable properties such as inherent ultra-low energy per operation, high speed, dense integration, negligible loss, and natural spiking responses. In addition, they have a relatively straight-forward micro/nano fabrication, which shows promise for implementation of an enormous number of lossless interconnections that are required to realize complex neuromorphic systems. We simulate QPSJ-only, as well as hybrid QPSJ + JJ circuits for application in neuromorphic circuits including artificial synapses and neurons, as well as fan-in and fan-out circuits. We also design and simulate learning circuits, where a simplified spike timing dependent plasticity rule is realized to provide potential learning mechanisms. We also take an alternative approach, which shows potential to overcome some of the expected challenges of QPSJ-based neuromorphic circuits, via QPSJ-based charge islands coupled together to generate non-linear charge dynamics that result in a large number of programmable weights or non-volatile memory states. Notably, we show that these weights are a function of the timing and frequency of the input spiking signals and can be programmed using a small number of DC voltage bias signals, therefore exhibiting spike-timing and rate dependent plasticity, which are mechanisms to realize learning in neuromorphic circuits.

Temporal Coding of Binary Patterns for Learning of Spiking Neuromorphic Systems Based on Nanocomposite Memristors

The metal/nanocomposite/metal (M/NC/M) memristive structures based on (Co40Fe40B20)x(LiNbO3)100–x have been studied. It has been shown that such memristors may change their conductance according to the bioinspired spike-timing-dependent plasticity (STDP) rules. Spiking neural network with 4 presynaptic inputs connected by memristor-synapses with a postsynaptic threshold neuron-integrator has been created, in which the images clustering with temporal coding has been implemented using the STDP rule. Thus, the fundamental possibility of using a temporal coding method, which is more effective than population-frequency coding, has been demonstrated for self-learning of spiking neuromorphic systems with synaptic weights based on nanocomposite memristors.

STDP and the distribution of preferred phases in the whisker system.

Rats and mice use their whiskers to probe the environment. By rhythmically swiping their whiskers back and forth they can detect the existence of an object, locate it, and identify its texture. Localization can be accomplished by inferring the whisker's position. Rhythmic neurons that track the phase of the whisking cycle encode information about the azimuthal location of the whisker. These neurons are characterized by preferred phases of firing that are narrowly distributed. Consequently, pooling the rhythmic signal from several upstream neurons is expected to result in a much narrower distribution of preferred phases in the downstream population, which however has not been observed empirically. Here, we show how spike timing dependent plasticity (STDP) can provide a solution to this conundrum. We investigated the effect of STDP on the utility of a neural population to transmit rhythmic information downstream using the framework of a modeling study. We found that under a wide range of parameters, STDP facilitated the transfer of rhythmic information despite the fact that all the synaptic weights remained dynamic. As a result, the preferred phase of the downstream neuron was not fixed, but rather drifted in time at a drift velocity that depended on the preferred phase, thus inducing a distribution of preferred phases. We further analyzed how the STDP rule governs the distribution of preferred phases in the downstream population. This link between the STDP rule and the distribution of preferred phases constitutes a natural test for our theory.

Sub-Optimality of the Early Visual System Explained Through Biologically Plausible Plasticity.

The early visual cortex is the site of crucial pre-processing for more complex, biologically relevant computations that drive perception and, ultimately, behaviour. This pre-processing is often studied under the assumption that neural populations are optimised for the most efficient (in terms of energy, information, spikes, etc.) representation of natural statistics. Normative models such as Independent Component Analysis (ICA) and Sparse Coding (SC) consider the phenomenon as a generative, minimisation problem which they assume the early cortical populations have evolved to solve. However, measurements in monkey and cat suggest that receptive fields (RFs) in the primary visual cortex are often noisy, blobby, and symmetrical, making them sub-optimal for operations such as edge-detection. We propose that this suboptimality occurs because the RFs do not emerge through a global minimisation of generative error, but through locally operating biological mechanisms such as spike-timing dependent plasticity (STDP). Using a network endowed with an abstract, rank-based STDP rule, we show that the shape and orientation tuning of the converged units are remarkably close to single-cell measurements in the macaque primary visual cortex. We quantify this similarity using physiological parameters (frequency-normalised spread vectors), information theoretic measures [Kullback–Leibler (KL) divergence and Gini index], as well as simulations of a typical electrophysiology experiment designed to estimate orientation tuning curves. Taken together, our results suggest that compared to purely generative schemes, process-based biophysical models may offer a better description of the suboptimality observed in the early visual cortex.

Parylene-based memristive synapses for hardware neural networks capable of dopamine-modulated STDP learning

Nowadays there is a growing interest in wearable and biocompatible computing systems that are safe for the human body. Memristive devices are prospective for such tasks owing to a number of their attractive properties, in particular, the multilevel character of resistive switching, or plasticity, which allows them to emulate synapses in hardware neuromorphic networks (NNs). The use of local learning rules for such NNs, for example, bioinspired spike-timing-dependent plasticity (STDP), has firmly established itself in recent years. In biological systems the basic STDP can be modified in the presence of neuromodulators (e.g. dopamine). This effect is believed to be essential for important biological functions such as reinforcement learning (RL), memory and others. The goal of this work was to demonstrate that such dopamine-like modulated STDP can be used in memristors based on a biocompatible polymer, parylene (poly-p-xylylene, or PPX). We have studied memristors both in the form of single Cu/PPX/ITO devices and in the form of crossbar Cu/PPX/Au structures. It was found that, in addition to stable memristive characteristics suitable for NNs, these devices can also change their conductance by means of bioinspired STDP rules, including dopamine-like modulated STDP window realized by introducing the coefficients for neuron spike amplitudes. The amplitude coefficients from −1 (inhibitory mode) to 1 (excitatory mode) of pre- and post-spikes, reflecting the ‘dopamine’ concentration, in various combinations allow observing the STDP window not only of the usual shape, but also of the anti-STDP, bell and anti-bell shapes. The obtained results demonstrate that the development of memristors based on PPX provides prospects for hardware realization of bio-inspired spiking NNs with RL ability.

Robust rhythmogenesis via spike-timing-dependent plasticity.

Rhythmic activity has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. Various experimental and theoretical studies have investigated rhythmic activity. The theoretical efforts have mainly been focused on the neuronal dynamics, under the assumption that network connectivity satisfies certain fine-tuning conditions required to generate oscillations. However, it remains unclear how this fine-tuning is achieved. Here we investigated the hypothesis that spike-timing-dependent plasticity (STDP) can provide the underlying mechanism for tuning synaptic connectivity to generate rhythmic activity. We addressed this question in a modeling study. We examined STDP dynamics in the framework of a network of excitatory and inhibitory neuronal populations that has been suggested to underlie the generation of oscillations in the gamma range. Mean-field Fokker-Planck equations for the synaptic weight dynamics are derived in the limit of slow learning. We drew on this approximation to determine which types of STDP rules drive the system to exhibit rhythmic activity, and we demonstrate how the parameters that characterize the plasticity rule govern the rhythmic activity. Finally, we propose a mechanism that can ensure the robustness of self-developing processes in general, and for rhythmogenesis in particular.

A STDP Rule that Favours Chaotic Spiking over Regular Spiking of Neurons

We compare the number of states of a Spiking Neural Network (SNN) composed from chaotic spiking neurons versus the number of states of a SNN composed from regular spiking neurons while both SNNs implementing a Spike Timing Dependent Plasticity (STDP) rule that we created. We find out that this STDP rule favors chaotic spiking since the number of states is larger in the chaotic SNN than the regular SNN. This chaotic favorability is not general; it is exclusive to this STDP rule only. This research falls under our long-term investigation of STDP and chaos theory.

Memristor-based synaptic plasticity and unsupervised learning of spiking neural networks

Synaptic plasticity is studied herein using a voltage-driven memristor model. The bidirectional weight update technique is demonstrated, and significant synaptic features, including nonlinear and threshold-based learning and long-term potentiation and long-term depression, are emulated. The spike-timing-dependent plasticity (STDP) learning characteristic curve is obtained from exhaustive simulations. Then, using leaky integrate and fire neurons and memristive synapses, fully connected spiking neural networks with $$2\times 2$$ and $$4\times 2$$ architectures are constructed, and unsupervised learning using the STDP rule and winner-takes-all strategy is evaluated in those networks for pattern classification.

CRBA: A Competitive Rate-Based Algorithm Based on Competitive Spiking Neural Networks.

In this paper we present a Competitive Rate-Based Algorithm (CRBA) that approximates operation of a Competitive Spiking Neural Network (CSNN). CRBA is based on modeling of the competition between neurons during a sample presentation, which can be reduced to ranking of the neurons based on a dot product operation and the use of a discrete Expectation Maximization algorithm; the latter is equivalent to the spike time-dependent plasticity rule. CRBA's performance is compared with that of CSNN on the MNIST and Fashion-MNIST datasets. The results show that CRBA performs on par with CSNN, while using three orders of magnitude less computational time. Importantly, we show that the weights and firing thresholds learned by CRBA can be used to initialize CSNN's parameters that results in its much more efficient operation.

Short-term and spike-timing-dependent plasticity facilitate the formation of modular neural networks

The brain has the phenomenal ability to reorganise itself by forming new connections among neurons and by pruning others. The so-called neural or brain plasticity facilitates the modification of brain structure and function over different time scales. Plasticity might occur due to external stimuli received from the environment, during recovery from brain injury, or due to modifications within the body and brain itself. In this paper, we study the combined effect of short-term (STP) and spike-timing-dependent plasticity (STDP) on the synaptic strength of excitatory coupled Hodgkin-Huxley neurons and show that plasticity can facilitate the formation of modular neural networks with complex topologies that resemble those of networks with preferential attachment properties. In particular, we use an STDP rule that alters the synaptic coupling intensity based on time intervals between spikes of postsynaptic and presynaptic neurons. Previous work has shown that STDP may induce the emergence of directed connections from high to low frequency spiking neurons. On the other hand, STP is attributed to the release of neurotransmitters in the synaptic cleft of neurons that alter its synaptic efficiency. Our results suggest that the combined effect of STP and STDP with long recovery times facilitates the formation of connections among neurons with similar spike frequencies only, a kind of preferential attachment. We then pursue this further and show that, when starting with all-to-all neural configurations, depending on the STP recovery time and distribution of neural frequencies, modular neural networks can emerge as a direct result of the combined effect of STP and STDP.

Mathematical model of a neuromorphic network based on memristive elements

The article discusses the modeling of interconnected memory elements within a neuromorphic network. A mathematical model is proposed that describes a hardware analog implementation of a spiking neural network with memristive elements as synaptic weights and a learning mechanism based on the STDP (Spike-Timing Dependent Plasticity) rule. As a memristor model, a model describing a hafnium oxide (HfO2) element is used. A numerical simulation of the operation of one and two interconnected neurons with 64 and 128 synapses has been carried out. The process of training the network to recognize certain templates is demonstrated.

Synaptic plasticity rules with physiological calcium levels

Spike-timing-dependent plasticity (STDP) is considered as a primary mechanism underlying formation of new memories during learning. Despite the growing interest in activity-dependent plasticity, it is still unclear whether synaptic plasticity rules inferred from in vitro experiments are correct in physiological conditions. The abnormally high calcium concentration used in in vitro studies of STDP suggests that in vivo plasticity rules may differ significantly from in vitro experiments, especially since STDP depends strongly on calcium for induction. We therefore studied here the influence of extracellular calcium on synaptic plasticity. Using a combination of experimental (patch-clamp recording and Ca2+ imaging at CA3-CA1 synapses) and theoretical approaches, we show here that the classic STDP rule in which pairs of single pre- and postsynaptic action potentials induce synaptic modifications is not valid in the physiological Ca2+ range. Rather, we found that these pairs of single stimuli are unable to induce any synaptic modification in 1.3 and 1.5 mM calcium and lead to depression in 1.8 mM. Plasticity can only be recovered when bursts of postsynaptic spikes are used, or when neurons fire at sufficiently high frequency. In conclusion, the STDP rule is profoundly altered in physiological Ca2+, but specific activity regimes restore a classical STDP profile.