Spike-timing-dependent Plasticity Research Articles

Improvement of synaptic property of GeSe based CBRAM by engineering the deposition condition of switching matrix

A study of effect of deposition condition of germanium selenide (GeSe) switching layer to Conductive bridging random access memory (CBRAM) device property was conducted to enhance memory property and synaptic property. The devices deposited under the varied deposition conditions were analyzed by current distribution of each state (Low-resistance state [LRS], High-resistance state [HRS]), conduction mechanism analysis, chemical analysis, and synaptic analysis according to pulse conditions. When we deposited switching layer at lowest power (40 W), more Ag ions move to the switching matrix and make lower operation voltage. But there were some disadvantages, which were worst distribution and higher HRS current, less memory window, nonlinear and rapid increase of potentiation process. Conversely, although the device deposited at high voltage increased the operating voltage, but there are improvements, such as the highest memory window and improved linearity of potentiation. The best sample was conducted the synaptic property measurement, and the device successfully mimicked the biological synaptic property with the good linearity of potentiation and depression, spike-rate dependent plasticity (SRDP) and spike-timing dependent plasticity (STDP) properties.

Bi-sigmoid spike-timing dependent plasticity learning rule for magnetic tunnel junction-based SNN

In this study, we explore spintronic synapses composed of several Magnetic Tunnel Junctions (MTJs), leveraging their attractive characteristics such as endurance, nonvolatility, stochasticity, and energy efficiency for hardware implementation of unsupervised neuromorphic systems. Spiking Neural Networks (SNNs) running on dedicated hardware are suitable for edge computing and IoT devices where continuous online learning and energy efficiency are important characteristics. We focus in this work on synaptic plasticity by conducting comprehensive electrical simulations to optimize the MTJ-based synapse design and find the accurate neuronal pulses that are responsible for the Spike Timing Dependent Plasticity (STDP) behavior. Most proposals in the literature are based on hardware-independent algorithms that require the network to store the spiking history to be able to update the weights accordingly. In this work, we developed a new learning rule, the Bi-Sigmoid STDP (B2STDP), which originates from the physical properties of MTJs. This rule enables immediate synaptic plasticity based on neuronal activity, leveraging in-memory computing. Finally, the integration of this learning approach within an SNN framework leads to a 91.71% accuracy in unsupervised image classification, demonstrating the potential of MTJ-based synapses for effective online learning in hardware-implemented SNNs.

Learning with filopodia and spines: Complementary strong and weak competition lead to specialized, graded, and protected receptive fields.

Filopodia are thin synaptic protrusions that have been long known to play an important role in early development. It has recently been found that they are more abundant in the adult cortex than previously thought, and more plastic than spines (button-shaped mature synapses). Inspired by these findings, we introduce a new model of synaptic plasticity that jointly describes learning of filopodia and spines. The model assumes that filopodia exhibit strongly competitive learning dynamics -similarly to additive spike-timing-dependent plasticity (STDP). At the same time it proposes that, if filopodia undergo sufficient potentiation, they consolidate into spines. Spines follow weakly competitive learning, classically associated with multiplicative, soft-bounded models of STDP. This makes spines more stable and sensitive to the fine structure of input correlations. We show that our learning rule has a selectivity comparable to additive STDP and represents input correlations as well as multiplicative models of STDP. We also show how it can protect previously formed memories and act as a synaptic consolidation mechanism. Overall, our results can be seen as a phenomenological description of how filopodia and spines could cooperate to overcome the difficulties that strong and weak competition each have.

Anti-Hebbian plasticity drives sequence learning in striatum

Spatio-temporal activity patterns have been observed in a variety of brain areas in spontaneous activity, prior to or during action, or in response to stimuli. Biological mechanisms endowing neurons with the ability to distinguish between different sequences remain largely unknown. Learning sequences of spikes raises multiple challenges, such as maintaining in memory spike history and discriminating partially overlapping sequences. Here, we show that anti-Hebbian spike-timing dependent plasticity (STDP), as observed at cortico-striatal synapses, can naturally lead to learning spike sequences. We design a spiking model of the striatal output neuron receiving spike patterns defined as sequential input from a fixed set of cortical neurons. We use a simple synaptic plasticity rule that combines anti-Hebbian STDP and non-associative potentiation for a subset of the presented patterns called rewarded patterns. We study the ability of striatal output neurons to discriminate rewarded from non-rewarded patterns by firing only after the presentation of a rewarded pattern. In particular, we show that two biological properties of striatal networks, spiking latency and collateral inhibition, contribute to an increase in accuracy, by allowing a better discrimination of partially overlapping sequences. These results suggest that anti-Hebbian STDP may serve as a biological substrate for learning sequences of spikes.

Deep unsupervised learning using spike-timing-dependent plasticity

Spike-timing-dependent plasticity (STDP) is an unsupervised learning mechanism for spiking neural networks that has received significant attention from the neuromorphic hardware community. However, scaling such local learning techniques to deeper networks and large-scale tasks has remained elusive. In this work, we investigate a Deep-STDP framework where a rate-based convolutional network, that can be deployed in a neuromorphic setting, is trained in tandem with pseudo-labels generated by the STDP clustering process on the network outputs. We achieve 24.56% higher accuracy and 3.5 × faster convergence speed at iso-accuracy on a 10-class subset of the Tiny ImageNet dataset in contrast to a k-means clustering approach.

Synapse Neurotransmitter Channel-Inspired AlOx Memristor with "V" Type Oxygen Vacancy Distribution.

Memristor possesses great potential and advantages in neuromorphic computing, while consistency and power consumption issues have been hindering its commercialization. Low cost and accuracy are the advantages of human brain, so memristors can be used to construct brain-like synaptic devices to solve these problems. In this work, a five-layer AlOx device with a V-shaped oxygen distribution is used to simulate biological synapses. The device simulates synapse structurally. Further, under electrical stimulation, O2- moves to the Ti electrode and oxygen vacancy (Vo) moves to the Pt electrode, thus forming a conductive filament (CF), which simulates the Ca2+ flow and releases neurotransmitters to the postsynaptic membrane, thus realizing the transmission of information. By controlling applied voltage, the regulation of Ca2+ gated pathway is realized to control the Ca2+ internal flow and achieve different degrees of information transmission. Long-term Potentiation (LTP)/Long-term Depression (LTD), Spike Timing Dependent Plasticity (STDP), these basic synaptic performances can be simulated. The AlOx device realizes low power consumption (56.7 pJ/392 fJ), high switching speed (25ns/60ns), and by adjusting the window value, the nonlinearity is improved (0.133/0.084), a high recognition accuracy (98.18%) is obtained in neuromorphic simulation. It shows a great prospect in multi-value storage and neuromorphic computing.

Minicolumn-Based Episodic Memory Model With Spiking Neurons, Dendrites and Delays.

Episodic memory is fundamental to the brain's cognitive function, but how neuronal activity is temporally organized during its encoding and retrieval is still unknown. In this article, combining hippocampus structure with a spiking neural network (SNN), a new bionic spiking temporal memory (BSTM) model is proposed to explore the encoding, formation, and retrieval of episodic memory. For encoding episodic memory, the spike-timing-dependent-plasticity (STDP) learning algorithm and a proposed minicolumn selection algorithm are used to encode each input item into several active minicolumns. For the formation of episodic memory, a sequential memory algorithm is proposed to store the contexts between items. For retrieval of episodic memory, the local retrieval algorithm and the global retrieval algorithm are proposed to retrieve sequence information, achieving multisentence prediction and multitime step prediction. All functions of BSTM are based on bionic spiking neurons, which have biological characteristics including columnar and dendritic structures, firing and receiving spikes, and delaying transmission. To test the performance of the BSTM model, the Children's Book Test (CBT) data set was used to conduct a series of experiments under different settings, including changing the number of minicolumns, neurons and sequences, modifying sequence items, etc. Compared to other sequence memory algorithms, the experimental results show that the proposed BSTM achieves higher accuracy and better robustness.

Developmental fine-tuning of medial superior olive neurons mitigates their predisposition to contralateral sound sources.

Having two ears enables us to localize sound sources by exploiting interaural time differences (ITDs) in sound arrival. Principal neurons of the medial superior olive (MSO) are sensitive to ITD, and each MSO neuron responds optimally to a best ITD (bITD). In many cells, especially those tuned to low sound frequencies, these bITDs correspond to ITDs for which the contralateral ear leads, and are often larger than the ecologically relevant range, defined by the ratio of the interaural distance and the speed of sound. Using in vivo recordings in gerbils, we found that shortly after hearing onset the bITDs were even more contralaterally leading than found in adult gerbils, and travel latencies for contralateral sound-evoked activity clearly exceeded those for ipsilateral sounds. During the following weeks, both these latencies and their interaural difference decreased. A computational model indicated that spike timing-dependent plasticity can underlie this fine-tuning. Our results suggest that MSO neurons start out with a strong predisposition toward contralateral sounds due to their longer neural travel latencies, but that, especially in high-frequency neurons, this predisposition is subsequently mitigated by differential developmental fine-tuning of the travel latencies.

Learning, Fast and Slow: Single- and Many-Shot Learning in the Hippocampus.

The hippocampus is critical for memory and spatial navigation. The ability to map novel environments, as well as more abstract conceptual relationships, is fundamental to the cognitive flexibility that humans and other animals require to survive in a dynamic world. In this review, we survey recent advances in our understanding of how this flexibility is implemented anatomically and functionally by hippocampal circuitry, during both active exploration (online) and rest (offline). We discuss the advantages and limitations of spike timing-dependent plasticity and the more recently discovered behavioral timescale synaptic plasticity in supporting distinct learning modes in the hippocampus. Finally, we suggest complementary roles for these plasticity types in explaining many-shot and single-shot learning in the hippocampus and discuss how these rules could work together to support the learning of cognitive maps.

A recurrence model capturing interface traps for non-zero bandgap GFETs towards dynamic mimicking of synaptic plasticity

This paper primarily focusses on developing an analytical model with a non-zero bandgap of boron (B)/nitrogen (N) substitution doped graphene field-effect transistors (GFETs) to mimic the synaptic behaviour. The trap charges at the channel and gate-insulator interface are utilized to induce the hysteresis conduction mechanism, which is further exploited to accomplish synaptic plasticity. The proposed recurrence, that is the time-dependent trap drain current model, accurately captures the physical insights of trap charges using an equivalent metal–insulator–graphene model. An interesting feature of the proposed model is that it is compatible with both the doped (B/N) and the undoped GFETs. The model is also investigated to generate the hysteresis characteristics of the GFET that are further utilized to simulate the synaptic behaviour. Another fact that must be noticed is the existence of complete OFF regions for doped B/N GFETs, unlike the undoped case, which manifest undesirable ambipolar behaviour. The synapse made up of B/N-doped GFETs predicts an optimistic learning and memory mechanism, termed as spike time-dependent plasticity (STDP). The STDP characteristics of B/N doped synaptic GFETs have been enhanced by more than 18 × compared to artificial synapses made of undoped GFETs. Hence, the hysteresis behaviour along with the non-zero bandgap of B/N substitution doped GFETs makes them highly favourable for the dynamic mimicking of synaptic plasticity to be efficiently biologically plausible.

Low-Power Perovskite Neuromorphic Synapse with Enhanced Photon Efficiency for Directional Motion Perception.

The advancement of artificial intelligent vision systems heavily relies on the development of fast and accurate optical imaging detection, identification, and tracking. Framed by restricted response speeds and low computational efficiency, traditional optoelectronic information devices are facing challenges in real-time optical imaging tasks and their ability to efficiently process complex visual data. To address the limitations of current optoelectronic information devices, this study introduces a novel photomemristor utilizing halide perovskite thin films. The fabrication process involves adjusting the iodide proportion to enhance the quality of the halide perovskite films and minimize the dark current. The photomemristor exhibits a high external quantum efficiency of over 85%, which leads to a low energy consumption of 0.6 nJ. The spike timing-dependent plasticity characteristics of the device are leveraged to construct a spiking neural network and achieve a 99.1% accuracy rate of directional perception for moving objects. The notable results offer a promising hardware solution for efficient optoneuromorphic and edge computing applications.

Artificial synaptic properties of zirconium-doped barium titanate film for neuromorphic computing

The traditional Von Neumann Architecture relies on the separation of memory and CPU, leading to bottlenecks like slow data transfer and high energy consumption. This is especially challenging when addressing the demands of large-scale parallel computing tasks such as artificial intelligence. Devices capable of simulating synaptic properties have gotten significant attention. Neuromorphic computing combines those devices with methods that work with neural networks. They are considered a viable approach to overcoming the Von Neumann Architecture bottleneck. This paper presents a Zr-doped BaTiO3 ferroelectric thin film prepared by a sol-gel process, which mimics the synaptic properties of the nerve: Short-Term Synaptic Plasticity (STP), paired-pulse facilitated (PPD), Long-Term Synaptic Plasticity (LTP) and Spike-Timing-Dependent Plasticity (STDP). An enhanced stochastic adaptive method is used to a built-in Convolutional Neural Network (CNN) to evaluate the device's neuromorphic computing capabilities. According to the results, the device obtains recognition accuracies of 96.5 % and 75.1 % for the MNIST and Fashion-MNIST datasets, respectively. This significantly contributes to the advancement of ferroelectric materials in neuromorphic computing applications.

Synaptopodin is required for long-term depression at Schaffer collateral-CA1 synapses

Synaptopodin (SP), an actin-associated protein found in telencephalic neurons, affects activity-dependant synaptic plasticity and dynamic changes of dendritic spines. While being required for long-term depression (LTD) mediated by metabotropic glutamate receptor (mGluR-LTD), little is known about its role in other forms of LTD induced by low frequency stimulation (LFS-LTD) or spike-timing dependent plasticity (STDP). Using electrophysiology in ex vivo hippocampal slices from SP-deficient mice (SPKO), we show that absence of SP is associated with a deficit of LTD at Sc-CA1 synapses induced by LFS-LTD and STDP. As LTD is known to require AMPA- receptors internalization and IP3-receptors calcium signaling, we tested by western blotting and immunochemistry if there were changes in their expression which we found to be reduced. While we were not able to induce LTD, long-term potentiation (LTP), albeit diminished in SPKO, can be recovered by using a stronger stimulation protocol. In SPKO we found no differences in NMDAR, which are the primary site of calcium signalling to induce LTP. Our study shows, for the first time, the key role of the requirement of SP to allow induction of activity-dependant LTD at Sc-CA1 synapses.

Federated Learning for Radar Gesture Recognition Based on Spike Timing-Dependent Plasticity

Federated Learning for Radar Gesture Recognition Based on Spike Timing-Dependent Plasticity

A Hybrid CMOS-Memristor Spiking Neural Network Supporting Multiple Learning Rules.

Artificial intelligence (AI) is changing the way computing is performed to cope with real-world, ill-defined tasks for which traditional algorithms fail. AI requires significant memory access, thus running into the von Neumann bottleneck when implemented in standard computing platforms. In this respect, low-latency energy-efficient in-memory computing can be achieved by exploiting emerging memristive devices, given their ability to emulate synaptic plasticity, which provides a path to design large-scale brain-inspired spiking neural networks (SNNs). Several plasticity rules have been described in the brain and their coexistence in the same network largely expands the computational capabilities of a given circuit. In this work, starting from the electrical characterization and modeling of the memristor device, we propose a neuro-synaptic architecture that co-integrates in a unique platform with a single type of synaptic device to implement two distinct learning rules, namely, the spike-timing-dependent plasticity (STDP) and the Bienenstock-Cooper-Munro (BCM). This architecture, by exploiting the aforementioned learning rules, successfully addressed two different tasks of unsupervised learning.

Stability against fluctuations: a two-dimensional study of scaling, bifurcations and spontaneous symmetry breaking in stochastic models of synaptic plasticity

Stochastic models of synaptic plasticity must confront the corrosive influence of fluctuations in synaptic strength on patterns of synaptic connectivity. To solve this problem, we have proposed that synapses act as filters, integrating plasticity induction signals and expressing changes in synaptic strength only upon reaching filter threshold. Our earlier analytical study calculated the lifetimes of quasi-stable patterns of synaptic connectivity with synaptic filtering. We showed that the plasticity step size in a stochastic model of spike-timing-dependent plasticity (STDP) acts as a temperature-like parameter, exhibiting a critical value below which neuronal structure formation occurs. The filter threshold scales this temperature-like parameter downwards, cooling the dynamics and enhancing stability. A key step in this calculation was a resetting approximation, essentially reducing the dynamics to one-dimensional processes. Here, we revisit our earlier study to examine this resetting approximation, with the aim of understanding in detail why it works so well by comparing it, and a simpler approximation, to the system’s full dynamics consisting of various embedded two-dimensional processes without resetting. Comparing the full system to the simpler approximation, to our original resetting approximation, and to a one-afferent system, we show that their equilibrium distributions of synaptic strengths and critical plasticity step sizes are all qualitatively similar, and increasingly quantitatively similar as the filter threshold increases. This increasing similarity is due to the decorrelation in changes in synaptic strength between different afferents caused by our STDP model, and the amplification of this decorrelation with larger synaptic filters.

Control-Etched Ti3C2Tx MXene Nanosheets for a Low-Voltage-Operating Flexible Memristor for Efficient Neuromorphic Computation.

Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Overcoming the challenge of developing a fully synaptic plastic network, we demonstrate a low-operating-voltage PET/ITO/p-MXene/Ag flexible memristor device by controlling the etching of aluminum metal ions in Ti3C2Tx MXene. The presence of a small fraction of Al ions in partially etched MXene (p-Ti3C2Tx) significantly suppresses the operating voltage to 1 V compared to 7 V from fully Al etched MXene (f-Ti3C2Tx)-based devices. Former devices exhibit excellent non-volatile data storage properties, with a robust ∼103 ON/OFF ratio, high endurance of ∼104 cycles, multilevel resistance states, and long data retention measured up to ∼106 s. High mechanical stability up to ∼73° bending angle and environmental robustness are confirmed with consistent switching characteristics under increasing temperature and humid conditions. Furthermore, a p-Ti3C2Tx MXene memristor is employed to mimic the biological synapse by measuring the learning-forgetting pattern for ∼104 cycles as potentiation and depression. Spike time-dependent plasticity (STDP) based on Hebb's Learning rules is also successfully demonstrated. Moreover, a remarkable accuracy of ∼95% in recognizing modified patterns from the National Institute of Standards and Technology (MNIST) data set with just 29 training epochs is achieved in simulation. Ultimately, our findings underscore the potential of MXene-based flexible memristor devices as versatile components for data storage and neuromorphic computing.

Release 2.0 — NEMSIM-RT: A real-time distributed spiking neural network simulator

NESIM-RT is a specialized tool designed for simulating neuromorphic systems. In this new release we extend its capabilities to include state-of-the art models like the AdexLIF and Izhikevich, and to incorporate dynamic synaptic mechanisms such as Spike-Timing Dependent Plasticity (STDP). With these new features, researchers can now observe in real-time how different parameters influence these models and learning rules, thereby gaining deeper insights into neuronal function and network dynamics.

A fully spiking coupled model of a deep neural network and a recurrent attractor explains dynamics of decision making in an object recognition task.

Objective.Object recognition and making a choice regarding the recognized object is pivotal for most animals. This process in the brain contains information representation and decision making steps which both take different amount of times for different objects. While dynamics of object recognition and decision making are usually ignored in object recognition models, here we proposed a fully spiking hierarchical model, explaining the process of object recognition from information representation to making decision.Approach.Coupling a deep neural network and a recurrent attractor based decision making model beside using spike time dependent plasticity learning rules in several convolutional and pooling layers, we proposed a model which can resemble brain behaviors during an object recognition task. We also measured human choices and reaction times in a psychophysical object recognition task and used it as a reference to evaluate the model.Main results.The proposed model explains not only the probability of making a correct decision but also the time that it takes to make a decision. Importantly, neural firing rates in both feature representation and decision making levels mimic the observed patterns in animal studies (number of spikes (p-value < 10-173) and the time of the peak response (p-value < 10-31) are significantly modulated with the strength of the stimulus). Moreover, the speed-accuracy trade-off as a well-known characteristic of decision making process in the brain is also observed in the model (changing the decision bound significantly affect the reaction time (p-value < 10-59) and accuracy (p-value < 10-165)).Significance.We proposed a fully spiking deep neural network which can explain dynamics of making decision about an object in both neural and behavioral level. Results showed that there is a strong and significant correlation (r= 0.57) between the reaction time of the model and of human participants in the psychophysical object recognition task.

Memory device based on MoS2-polyvinyl alcohol for simulating synaptic behavior

A memory device based on MoS2-polyvinyl alcohol was fabricated. As a good dispersant, polyvinyl alcohol solution can disperse 2D layered materials more evenly in the solution. The 19 states of the device conductance are modulated step by step by direct current scanning, which makes it possible to perform the computing task in memory. Then, the presence of conductive filaments allows the resistance state of the memristor to be dynamically transformed, which is similar to some basic behavior of biological synapses. Further, a variety of conductivity states such as short-term potentiation, short term depression, long term potentiation and long-term depression behavior are modulated by applying voltage to simulate the basic behavior of biological synapses. Finally, the paired pulse facilitation, paired pulse depression, spiking timing dependent plasticity and spiking voltage dependent plasticity behaviors of biological synapses are simulated in memristor devices.