Spike-timing-dependent Plasticity Research Articles

Inverse stochastic resonance in adaptive small-world neural networks.

Inverse stochastic resonance (ISR) is a counterintuitive phenomenon where noise reduces the oscillation frequency of an oscillator to a minimum occurring at an intermediate noise intensity, and sometimes even to the complete absence of oscillations. In neuroscience, ISR was first experimentally verified with cerebellar Purkinje neurons [Buchin et al., PLOS Comput. Biol. 12, e1005000 (2016)]. These experiments showed that ISR enables a locally optimal information transfer between the input and output spike train of neurons. Subsequent studies have further demonstrated the efficiency of information processing and transfer in neural networks with small-world network topology. We have conducted a numerical investigation into the impact of adaptivity on ISR in a small-world network of noisy FitzHugh-Nagumo (FHN) neurons, operating in a bi-metastable regime consisting of a metastable fixed point and a metastable limit cycle. Our results show that the degree of ISR is highly dependent on the value of the FHN model's timescale separation parameter ε. The network structure undergoes dynamic adaptation via mechanisms of either spike-time-dependent plasticity (STDP) with potentiation-/depression-domination parameter P or homeostatic structural plasticity (HSP) with rewiring frequency F. We demonstrate that both STDP and HSP amplify the effect of ISR when ε lies within the bi-stability region of FHN neurons. Specifically, at larger values of ε within the bi-stability regime, higher rewiring frequencies F are observed to enhance ISR at intermediate (weak) synaptic noise intensities, while values of P consistent with depression-domination (potentiation-domination) consistently enhance (deteriorate) ISR. Moreover, although STDP and HSP control parameters may jointly enhance ISR, P has a greater impact on improving ISR compared to F. Our findings inform future ISR enhancement strategies in noisy artificial neural circuits, aiming to optimize local information transfer between input and output spike trains in neuromorphic systems and prompt venues for experiments in neural networks.

Developmental spike timing-dependent long-term depression requires astrocyte D-serine at L2/3-L2/3 synapses of the mouse somatosensory cortex.

Spike timing-dependent plasticity (STDP) is a learning rule important for synaptic refinement and for learning and memory during development. While different forms of presynaptic t-LTD have been deeply investigated, little is known about the mechanisms of somatosensory cortex postsynaptic t-LTD. In the present work, we investigated the requirements and mechanisms for induction of developmental spike timing-dependent long-term depression (t-LTD) at L2/3-L2/3 synapses in the juvenile mouse somatosensory cortex. We found that P13-21 mice of either sex show t-LTD at L2/3-L2/3 synapses induced by pairing single presynaptic activity with single postsynaptic action potentials at low stimulation frequency (0.2 Hz) is expressed postsynaptically, and requires the activation of ionotropic postsynaptic NMDA-type glutamate receptors containing GluN2B subunits. In addition, it requires postsynaptic Ca2+, Ca2+ release from internal stores, calcineurin, postsynaptic endocannabinoid (eCB) synthesis, activation of CB1 receptors and astrocytic signalling to release the gliotransmitter d-serine to activate postsynaptic NMDARs to induce t-LTD. These results show direct evidence of the mechanism involved in developmental postsynaptic t-LTD at L2/3-L2/3 synapses, revealing a central role of astrocytes and their release of D-serine in its induction.Significance statement We show here the mechanisms and role of astrocytes and gliotransmitters in a postsynaptic spike timing dependent long-term depression (t-LTD) form defined at layer (L)2/3-L2/3 synapses of the somatosensory cortex. We have discovered that this form of plasticity involves N-methyl D-aspartate receptors (NMDAR) containing the GluN2B subunit and requires astrocytes and the gliotransmitter D-serine to co-activate (together with glutamate) postsynaptic NMDAR to mediate LTD. This can be a general mechanism in the brain to define different forms of plasticity. Defining the mechanisms of synaptic plasticity may have important implications for brain repair, sensorial recovery, the treatment of neurodevelopmental disorders and even, for educational policy.

Time–frequency analysis using spiking neural network

Abstract Time–frequency analysis plays a crucial role in various fields, including signal processing and feature extraction. In this article, we propose an alternative and innovative method for time–frequency analysis using a biologically inspired spiking neural network (SNN), encompassing both a specific spike-continuous-time-neuron-based neural architecture and an adaptive learning rule. We aim to efficiently detect frequencies embedded in a given signal for the purpose of feature extraction. To achieve this, we suggest using an SN-based network functioning as a resonator for the detection of specific frequencies. We developed a modified supervised spike timing-dependent plasticity learning rule to effectively adjust the network parameters. Unlike traditional methods for time–frequency analysis, our approach obviates the need to segment the signal into several frames, resulting in a streamlined and more effective frequency analysis process. Simulation results demonstrate the efficiency of the proposed method, showcasing its ability to detect frequencies and generate a Spikegram akin to the fast Fourier transform (FFT) based spectrogram. The proposed approach is applied to analyzing EEG signals, demonstrating an accurate correlation to the equivalent FFT transform. Results show a success rate of 94.3% in classifying EEG signals.

Fractional-order spike-timing-dependent gradient descent for multi-layer spiking neural networks

Accumulated detailed knowledge about the neuronal activities in human brains has brought more attention to bio-inspired spiking neural networks (SNNs). In contrast to non-spiking deep neural networks (DNNs), SNNs can encode and transmit spatiotemporal information more efficiently by exploiting biologically realistic and low-power event-driven neuromorphic architectures. However, the supervised learning of SNNs still remains a challenge because the spike-timing-dependent plasticity (STDP) of connected spiking neurons is difficult to implement and interpret in existing backpropagation learning schemes. This paper proposes a fractional-order spike-timing-dependent gradient descent (FO-STDGD) learning model by considering a derived nonlinear activation function that describes the relationship between the quasi-instantaneous firing rate and the temporal membrane potentials of nonleaky integrate-and-fire neurons. The training strategy can be generalized to any fractional orders between 0 and 2 since the FO-STDGD incorporates the fractional gradient descent method into the calculation of spike-timing-dependent loss gradients. The proposed FO-STDGD model is tested on the MNIST and DVS128 Gesture datasets and its accuracy under different network structure and fractional orders is analyzed. It can be found that the classification accuracy increases as the fractional order increases, and specifically, the case of fractional order 1.9 improves by 155 % relative to the case of fractional order 1 (traditional gradient descent). In addition, our scheme demonstrates the state-of-the-art computational efficacy for the same SNN structure and training epochs.

SNN using color-opponent and attention mechanisms for object recognition

The current spiking neural network (SNN) relies on spike-timing-dependent plasticity (STDP) primarily for shape learning in object recognition tasks, overlooking the equally critical aspect of color information. To address this gap, our study introduces an unsupervised variant of STDP that incorporates principles from color-opponency mechanisms (COM) and classical receptive fields (CRF) found in the biological visual system, facilitating the integration of color information during parameter updates within the SNN architecture. Our approach initially preprocesses images into two distinct feature maps: one for shape and another for color. Then, signals derived from COM and intensity concurrently drive the STDP process, thereby updating parameters associated with both color and shape feature maps. Furthermore, we propose a channel-wise attention mechanism to enhance differentiation among objects sharing similar shapes or colors. Specifically, this mechanism utilizes convolution to generate an output spike-wave, identifying a winner based on earliest spike timing and maximal potential. The winning kernel computes attention, which is then applied via convolution to each input image feature map, generating post-feature maps. A STDP-like normalization rule compares firing times between pre- and post-feature maps, dynamically adjusting channel weights to optimize object recognition during the training phase.We assessed the proposed algorithm using SNN with both single-layer and multi-layer architectures across three datasets. Experimental findings highlight its efficacy and superiority in complex object recognition tasks compared to state-of-the-art (SOTA) algorithms. Notably, our approach achieved a significant 20% performance improvement over the SOTA on the Caltech-101 dataset. Moreover, the algorithm is well-suited for hardware implementation and energy efficiency, leveraging a winner-selection mechanism based on the earliest spike time.

Adaptive Synaptic Adjustment Mechanism to Improve Learning Performances of Spiking Neural Networks

ABSTRACTSpiking Neural Networks (SNNs) are currently attracting researchers' attention due to their efficiencies in various tasks. Spike‐timing‐dependent plasticity (STDP) is an unsupervised learning process that utilizes bio‐plausibility based on the relative timing of pre/post‐synaptic spikes of neurons. Integrated with STDP, SNNs perform well consuming less energy. However, it is hard to ensure that synaptic weights always converge to values guaranteeing accurate prediction because STDP does not change synaptic weights with supervision. To address this limitation, researchers have proposed mechanisms for inducing STDP to converge synaptic weights on the proper values referring to current synaptic weights. Thus, if the current weights fail to describe proper synaptic connections, they cannot induce STDP to update synaptic weights properly. To solve this problem, we propose an adaptive mechanism that helps STDP to converge synaptic weights directly based on input data features: Adaptive synaptic template (AST). AST leads synaptic weights to describe synaptic connections according to the data features. It prevents STDP from changing synaptic weights based on abnormal weights that fail to describe the proper synaptic connections. This is because it does not use the current synaptic weights that can disturb proper weight convergence. We integrate AST with an SNN and conduct experiments to compare it with a baseline (the SNN without AST) and benchmarks (previous works to improve STDP). Our experimental results show that the SNN using AST classifies various data sets with 6%–39% higher accuracy than the baseline and benchmarks.

Sustainable vertically-oriented graphene-electrode memristors for neuromorphic applications

Neuromorphic computing, an innovative field in electronic and computing engineering, aims to enhance computing paradigms by simulating brain processes. Memristors, a two-terminal device, hold promise in revolutionising neuromorphic architectures by circumventing the Von-Neumann bottleneck. The performance and applicability of memristors heavily rely on the materials and fabrication processes employed. Graphene exhibits unique properties that can be leveraged in memristor design. Moreover, graphene stands out as a material with the potential for large-scale, sustainable production through Plasma Enhanced Chemical Vapour Deposition (PECVD). Notably, the properties of graphene-electrode memristors vary with minor structural differences induced by different PECVD temperatures. This paper reports the synthesis of graphene electrodes by time- and cost-effective PECVD from a sustainable plant extract for memristors. In addition, this paper delves into investigating how these structural variations impact the properties of graphene memristors and explores their potential exploitation in neuromorphic applications for implementing the well-known Spike Timing Dependent Plasticity (STDP) learning mechanism. The paper also utilises the developed STDP learning to perform an unsupervised spike-based pattern classification task.

Advanced Dual-Input Artificial Optical Synapse for Recognition and Generative Neural Network

Perovskite materials have emerged as leading candidates for optical synaptic devices due to their superior photosensitivity and tunable optoelectronic properties. However, the practical application of perovskite-based optoelectronic synaptic transistors has been hindered by issues of poor stability and high toxicity. This study developed an artificial synaptic thin film transistor (TFT) based on a Cs2AgBiBr6/InOx bilayer. The device demonstrated synaptic behavior under optoelectronic hybrid stimulation (largest wavelength ~520nm), such as excitatory postsynaptic current (EPSC), inhibitory postsynaptic current (IPSC), paired-pulse facilitation (PPF), spike-timing-dependent plasticity (STDP), short-term memory (STM) and long-term memory (LTM). Notably, the “light writing and voltage erasing” characteristics of these devices could be utilized to construct a convolutional neural network (CNN) classifier for the CIFAR-10 dataset, demonstrating noise tolerance close to the human eye. The loss in recognition accuracy was within 1% when Gaussian white noise and salt pepper noise were added. Furthermore, these devices exhibited great potential in Generative Adversarial Networks (CycleGAN), with the generated image quality achieving levels of 0.637 and 0.715 in Improved Perceptual Image Processing System (IPIPS) and Structural Similarity Index (SSIM) evaluation metrics for the zebra dataset, indicating good image quality. This study indicates that our Cs2AgBiBr6-based artificial optical synaptic thin-film transistors (TFTs) are promising for sensing and in-memory computing applications.

Impact of the La2NiO4+δ Oxygen Content on the Synaptic Properties of the TiN/La2NiO4+δ/Pt Memristive Devices

AbstractThe rapid development of brain‐inspired computing requires new artificial components and architectures for its hardware implementation. In this regard, memristive devices emerged as potential candidates for artificial synapses because of their ability to emulate the plasticity of the biological synapses. In this work, the synaptic behavior of the TiN/La2NiO4+δ/Pt memristive devices based on thermally annealed La2NiO4+δ films is thoroughly investigated. Using electron energy loss spectroscopy (EELS), it is shown that post‐deposition annealing using inert (Ar) or oxidizing (O2) atmospheres affects the interstitial oxygen content (δ) in the La2NiO4+δ films. Electrical characterization shows that both devices exhibit long‐term potentiation/depression (LTP/LTD) and spike‐timing‐dependent plasticity (STDP). At the same time, the Ar annealed TiN/La2NiO4+δ/Pt device demonstrates filamentary‐like behavior, fast switching, and low energy consumption. On the other hand, the O2 annealed TiN/La2NiO4+δ/Pt devices are forming‐free, exhibiting interfacial‐like resistive switching with slower kinetics. Finally, the simulation tools show that spiking neural network (SNN) architectures with weight updates based on the experimental data achieve high inference accuracy in the digit recognition task, which proves the potential of TiN/La2NiO4+δ/Pt devices for artificial synapse applications.

Supervised learning of spatial features with STDP and homeostasis using Spiking Neural Networks on SpiNNaker

Artificial Neural Networks (ANN) have gained significant popularity thanks to their ability to learn using the well-known backpropagation algorithm. Conversely, Spiking Neural Networks (SNNs), despite having broader capabilities than ANNs, have always posed challenges in the training phase. This paper shows a new method to perform supervised learning on SNNs, using Spike Timing Dependent Plasticity (STDP) and homeostasis, aiming at training the network to identify spatial patterns. Spatial patterns refer to spike patterns without a time component, where all spike events occur simultaneously. The method is tested using the SpiNNaker digital architecture. A SNN is trained to recognise one or multiple patterns and performance metrics are extracted to measure the performance of the network. Some considerations are drawn from the results showing that, in the case of a single trained pattern, the network behaves as the ideal detector, with 100% accuracy in detecting the trained pattern. However, as the number of trained patterns on a single network increases, the accuracy of identification is linked to the similarities between these patterns. This method of training an SNN to detect spatial patterns may be applied to pattern recognition in static images or traffic analysis in computer networks, where each network packet represents a spatial pattern. It will be stipulated that the homeostatic factor may enable the network to detect patterns with some degree of similarity, rather than only perfectly matching patterns. The principles outlined in this article serve as the fundamental building blocks for more complex systems that utilise both spatial and temporal patterns by converting specific features of input signals into spikes. One example of such a system is a computer network packet classifier, tasked with real-time identification of packet streams based on features within the packet content.

Defect-engineered monolayer MoS2 with enhanced memristive and synaptic functionality for neuromorphic computing

Two-dimensional transition metal dichalcogenides (TMDs)-based memristors are promising candidates for realizing artificial synapses in next-generation computing. However, practical implementation faces several challenges, such as high non-linearity and asymmetry in synaptic weight updates, limited dynamic range, and cycle-to-cycle variability. Here, utilizing optimal-power argon plasma treatment, we significantly enhance the performance matrix of memristors fabricated from monolayer MoS2. Our approach not only improves linearity and symmetry in synaptic weight updates but also increases the number of available synaptic weight updates and enhances Spike-Time Dependent Plasticity. Notably, it broadens the switching ratio by two orders, minimizes cycle-to-cycle variability, reduces non-linear factors, and achieves an energy consumption of ~30 fJ per synaptic event. Implementation of these enhancements is demonstrated through Artificial Neural Network simulations, yielding a learning accuracy of ~97% on the MNIST hand-written digits dataset. Our findings underscore the significance of defect engineering as a powerful tool in advancing the synaptic functionality of memristors.

Flexible Artificial Ag NPs:a-SiC0.11:H Synapse on Al Foil with High Uniformity and On/Off Ratio for Neuromorphic Computing.

A neuromorphic computing network based on SiCx memristor paves the way for a next-generation brain-like chip in the AI era. Up to date, the SiCx-based memristor devices are faced with the challenge of obtaining flexibility and uniformity, which can push forward the application of memristors in flexible electronics. For the first time, we report that a flexible artificial synaptic device based on a Ag NPs:a-SiC0.11:H memristor can be constructed by utilizing aluminum foil as the substrate. The device exhibits stable bipolar resistive switching characteristic even after bending 1000 times, displaying excellent flexibility and uniformity. Furthermore, an on/off ratio of approximately 107 can be obtained. It is found that the incorporation of silver nanoparticles significantly enhances the device's set and reset voltage uniformity by 76.2% and 69.7%, respectively, which is attributed to the contribution of the Ag nanoparticles. The local electric field of Ag nanoparticles can direct the formation and rupture of conductive filaments. The fitting results of I-V curves show that the carrier transport mechanism agrees with Poole-Frenkel (P-F) model in the high-resistance state, while the carrier transport follows Ohm's law in the low-resistance state. Based on the multilevel storage characteristics of the Al/Ag NPs:a-SiC0.11:H/Al foil resistive switching device, we successfully observed the biological synaptic characteristics, including the long-term potentiation (LTP), long-term depression (LTD), and spike-timing-dependent plasticity (STDP). The flexible artificial Ag NPs:a-SiC0.11:H/Al foil synapse possesses excellent conductance modulation capabilities and visual learning function, demonstrating the promise of application in flexible electronics technology for high-efficiency neuromorphic computing in the AI period.

Reconfigurable dielectric engineered WSe2/HZO mem-transistor

Abstract Hybrid systems coupling 2D semiconductors with functional ferroelectrics are attracting increasing attention due to their excellent electronic/optoelectronic properties and new functionalities through the multiple heterointerface interaction. In our device architecture, interfacial states are introduced on the ferroelectric Hf0.5Zr0.5O2 thin film as gate dielectric layer for charge trapping effect. Utilizing the collaborative effects of charge trapping and ferroelectric polarization behavior, a multifunctional 2D WSe2/HZO memtransistor is demonstrated with ultra-low off-state (dark) current of 10-13 A, high on/off ratio of 106 and linear conductance update. This device exhibits reliable memory properties and tunable synaptic functions including short-term plasticity (STP)/ long-term plasticity (LTP), paired pulse facilitation (PPF), spike-timing dependent plasticity (STDP), synaptic potentiation/depression, and filtering in a single device. Extensive endurance tests ensure robust stability (1000 switching cycles, 2000 s holding time) and the synaptic weight update in the device exhibits excellent linearity. Based on the experimental data, our devices eventually achieve an accuracy of 94.8% in artificial neural network simulations. These results highlight a new approach for constructing hybrid systems coupling 2D semiconductors with functional ferroelectrics in a single device for tuning synaptic weight, optimizing circuit design, and building artificial neuromorphic computing systems.

Spike-Timing-Dependent Plasticity Device with Ga-Sn-O Conductance Change Layer Deposited by Mist-CVD Method

A spike-timing-dependent plasticity (STDP) device with a Ga-Sn-O (GTO) conductance change layer deposited by a mist-CVD method has been developed. First, the memristive characteristic is analyzed. Next, based on it, spike waveforms are determined. Finally, the STDP characteristic is successfully confirmed. This is an original report on the realization of an STDP characteristic using a thin film deposited by the mist-CVD method, which is achieved by the GTO properties and a well-designed clear methodology to realize a STDP characteristic from a memristive characteristic.

Parallel hyperparameter optimization of spiking neural networks

Hyperparameter optimization of spiking neural networks (SNNs) is a difficult task which has not yet been deeply investigated in the literature. In this work, we designed a scalable constrained Bayesian based optimization algorithm that prevents sampling in non-spiking areas of an efficient high dimensional search space. These search spaces contain infeasible solutions that output no or only a few spikes during the training or testing phases, we call such a mode a “silent network”. Finding them is difficult, as many hyperparameters are highly correlated to the architecture and to the dataset. We leverage silent networks by designing a spike-based early stopping criterion to accelerate the optimization process of SNNs trained by spike timing dependent plasticity and surrogate gradient. We parallelized the optimization algorithm asynchronously, and ran large-scale experiments on heterogeneous multi-GPU Petascale architecture. Results show that by considering silent networks, we can design more flexible high-dimensional search spaces while maintaining a good efficacy. The optimization algorithm was able to focus on networks with high performances by preventing costly and worthless computation of silent networks.

Neuromorphic learning and recognition in WO3−x thin film-based forming-free flexible electronic synapses

In pursuing advanced neuromorphic applications, this study introduces the successful engineering of a flexible electronic synapse based on WO3-x, structured as W/WO3-x/Pt/Muscovite-Mica. This artificial synapse is designed to emulate crucial learning behaviors fundamental to in-memory computing. We systematically explore synaptic plasticity dynamics by implementing pulse measurements capturing potentiation and depression traits akin to biological synapses under flat and different bending conditions, thereby highlighting its potential suitability for flexible electronic applications. The findings demonstrate that the memristor accurately replicates essential properties of biological synapses, including short-term plasticity (STP), long-term plasticity (LTP), and the intriguing transition from STP to LTP. Furthermore, other variables are investigated, such as paired-pulse facilitation, spike rate-dependent plasticity, spike time-dependent plasticity, pulse duration-dependent plasticity, and pulse amplitude-dependent plasticity. Utilizing data from flat and differently bent synapses, neural network simulations for pattern recognition tasks using the Modified National Institute of Standards and Technology dataset reveal a high recognition accuracy of ∼95% with a fast learning speed that requires only 15 epochs to reach saturation.

A new hybrid learning control system for robots based on spiking neural networks

This paper presents a new hybrid learning and control method that can tune their parameters based on reinforcement learning. In the new proposed method, nonlinear controllers are considered multi-input multi-output functions and then the functions are replaced with SNNs with reinforcement learning algorithms. Dopamine-modulated spike-timing-dependent plasticity (STDP) is used for reinforcement learning and manipulating the synaptic weights between the input and output of neuronal groups (for parameter adjustment). Details of the method are presented and some case studies are done on nonlinear controllers such as Fractional Order PID (FOPID) and Feedback Linearization. The structure and the dynamic equations for learning are presented, and the proposed algorithm is tested on robots and results are compared with other works. Moreover, to demonstrate the effectiveness of SNNFOPID, we conducted rigorous testing on a variety of systems including a two-wheel mobile robot, a double inverted pendulum, and a four-link manipulator robot. The results revealed impressively low errors of 0.01 m, 0.03 rad, and 0.03 rad for each system, respectively. The method is tested on another controller named Feedback Linearization, which provides acceptable results. Results show that the new method has better performance in terms of Integral Absolute Error (IAE) and is highly useful in hardware implementation due to its low energy consumption, high speed, and accuracy. The duration necessary for achieving full and stable proficiency in the control of various robotic systems using SNNFOPD, and SNNFL on an Asus Core i5 system within Simulink’s Simscape environment is as follows:– Two-link robot manipulator with SNNFOPID: 19.85656 hours– Two-link robot manipulator with SNNFL: 0.45828 hours– Double inverted pendulum with SNNFOPID: 3.455 hours– Mobile robot with SNNFOPID: 3.71948 hours– Four-link robot manipulator with SNNFOPID: 16.6789 hours.This method can be generalized to other controllers and systems like robots.

Harnessing a WOx-based flexible transparent memristor synapse with a hafnium oxide layer for neuromorphic computing.

Transparent memristor-based neuromorphic synapses are expected to be specialised devices for high-speed information transmission and processing. The synaptic linearity and potentiation/depression cycles are imperative issues for the application of memristors. This work explores a memristor for improving switching uniformity by introducing a thin HfOx interfacial layer as a diffusion-limiting layer sandwiched between WOx and ITO bottom electrodes. An optimized HfOx thickness not only provides the best switching properties but also shows superior synaptic properties. The optimized 15 nm thin WOx layer can retain the memristor's excellence in P/D linearity, a cycling stability of 494 epochs and image recognition up to 3 mm bending, making it suitable for flexible devices. The artificial synapse is capable of reversible short-term and long-term learning behaviors confirmed by spike-timing-dependent-plasticity (STDP) results. X-ray photoelectron spectroscopy confirms the device composition and provides the oxygen vacancy concentration at the WOx/HfOx interface to realize the switching mechanism. The thicknesses of the different layers are estimated from the high-resolution transmission electron microscopy observations. The fabricated device exhibits 92.2% transparency, as confirmed by the UV-Vis spectrum.

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 computational model elucidating mechanisms and variability in theta burst stimulation responses.

Theta burst stimulation (TBS) is a form of repetitive transcranial magnetic stimulation (rTMS) with unknown underlying mechanisms and highly variable responses across subjects. To investigate these issues, we developed a simple computational model. Our model consisted of two neurons linked by an excitatory synapse that incorporates two mechanisms: short-term plasticity (STP) and spike-timing-dependent plasticity (STDP). We applied a variable-amplitude current through I-clamp with a TBS time pattern to the pre- and post-synaptic neurons, simulating synaptic plasticity. We analyzed the results and provided an explanation for the effects of TBS, as well as the variability of responses to it. Our findings suggest that the interplay of STP and STDP mechanisms determines the direction of plasticity, which selectively affects synapses in extended neurons and underlies functional effects. Our model describes how the timing, number, and intensity of pulses delivered to neurons during rTMS contribute to induced plasticity. This not only successfully explains the different effects of intermittent TBS (iTBS) and continuous TBS (cTBS), but also predicts the results of other protocols such as 10 Hz rTMS. We propose that the variability in responses to TBS can be attributed to the variable span of neuronal thresholds across individuals and sessions. Our model suggests a biologically plausible mechanism for the diverse responses to TBS protocols and aligns with experimental data on iTBS and cTBS outcomes. This model could potentially aid in improving TBS and rTMS protocols and customizing treatments for patients, brain areas, and brain disorders.