Spike Timing Dependent Plasticity Learning Research Articles

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.

An Efficient Neuromorphic Implementation of Temporal Coding-Based On-Chip STDP Learning

Fulfilling brain intelligence on the chip has long been a fascinating and challenging endeavor. Numerous neuromorphic computing platforms based on spiking neural networks have simulated the architecture and information processing of the brain, providing new insights to realize artificial intelligence. However, the critical issue still remains how to effectively learn on site. In this brief, we present a neuromorphic processor aimed at performing instant on-chip learning for edge processing purposes. This design adopts a temporal coding based implementation of nearest-neighbor and additive spike-timing-dependent plasticity (STDP) learning method. A pair of spike tracing counters and a reconfigurable look-up table are deployed to perform the STDP weight updating. And a 16-core architecture is also leveraged to enhance learning parallelism and scalability. Integrating up to 2048 neurons and 2M 8-bit synapses, the processor achieves peak performance of 81.92GSOPS and 22.80GSOPS, with energy efficiency of 1.28pJ/SOP and 4.99pJ/SOP in the inference and learning phases, respectively. Validation experiments demonstrate that it achieves recognition accuracies of 93.54%, 59.46%, 72.67% and 79.77% for MNIST, Fashion-MNIST, EMNIST and Chinese-MNIST datasets by on-chip labeling methods, respectively.

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.

Digital Hardware Implementation of ReSuMe Learning Algorithm for Spiking Neural Networks.

Within this paper, we demonstrate the feasibility of the FPGA implementation as well as the 180nm CMOS circuit design of a particular biologically plausible supervised learning algorithm (ReSuMe). Based on the Spike-Timing-Dependent Plasticity (STDP) learning phenomenon, this design proposes a fully configurable implementation of STDP learning window function to adjust the learning process for different applications, optimizing results for each use case. The CMOS implementation in 180nm technology node supplied with 1.8V shows a core area of 0.78mm2 and verifies the suitability of an on-chip ReSuMe learning algorithm implementation and its capability of integration with a multitude of external and already designed structures of Spiking Neural Networks (SNNs).

Adaptive STDP-based on-chip spike pattern detection.

A spiking neural network (SNN) is a bottom-up tool used to describe information processing in brain microcircuits. It is becoming a crucial neuromorphic computational model. Spike-timing-dependent plasticity (STDP) is an unsupervised brain-like learning rule implemented in many SNNs and neuromorphic chips. However, a significant performance gap exists between ideal model simulation and neuromorphic implementation. The performance of STDP learning in neuromorphic chips deteriorates because the resolution of synaptic efficacy in such chips is generally restricted to 6 bits or less, whereas simulations employ the entire 64-bit floating-point precision available on digital computers. Previously, we introduced a bio-inspired learning rule named adaptive STDP and demonstrated via numerical simulation that adaptive STDP (using only 4-bit fixed-point synaptic efficacy) performs similarly to STDP learning (using 64-bit floating-point precision) in a noisy spike pattern detection model. Herein, we present the experimental results demonstrating the performance of adaptive STDP learning. To the best of our knowledge, this is the first study that demonstrates unsupervised noisy spatiotemporal spike pattern detection to perform well and maintain the simulation performance on a mixed-signal CMOS neuromorphic chip with low-resolution synaptic efficacy. The chip was designed in Taiwan Semiconductor Manufacturing Company (TSMC) 250 nm CMOS technology node and comprises a soma circuit and 256 synapse circuits along with their learning circuitry.

Python-Based Circuit Design for Fundamental Building Blocks of Spiking Neural Network

Spiking neural networks (SNNs) are considered a crucial research direction to address the “storage wall” and “power wall” challenges faced by traditional artificial intelligence computing. However, developing SNN chips based on CMOS (complementary metal oxide semiconductor) circuits remains a challenge. Although memristor process technology is the best alternative to synapses, it is still undergoing refinement. In this study, a novel approach is proposed that employs tools to automatically generate HDL (hardware description language) code for constructing neuron and memristor circuits after using Python to describe the neuron and memristor models. Based on this approach, HR (Hindmash–Rose), LIF (leaky integrate-and-fire), and IZ (Izhikevich) neuron circuits, as well as HP, EG (enhanced generalized), and TB (the behavioral threshold bipolar) memristor circuits are designed to construct the most basic connection of a SNN: the neuron–memristor–neuron circuit that satisfies the STDP (spike-timing-dependent-plasticity) learning rule. Through simulation experiments and FPGA (field programmable gate array) prototype verification, it is confirmed that the IZ and LIF circuits are suitable as neurons in SNNs, while the X variables of the EG memristor model serve as characteristic synaptic weights. The EG memristor circuits best satisfy the STDP learning rule and are suitable as synapses in SNNs. In comparison to previous works on hardware spiking neurons, the proposed method needed fewer area resources for creating spiking neurons models on FPGA. The proposed SNN basic components design method, and the resulting circuits, are beneficial for architectural exploration and hardware–software co-design of SNN chips.

Reconfigurable logic gates in biological crossbar neural networks using STDP learning.

McCulloch and Pitts provided a way to describe brain functions in logic gates. The idea of Boolean neurons made a tremendous contribution to the development of artificial intelligence. However, logic gates based on biological neural networks have never been implemented. To investigate where logic gates can be demonstrated using biological neural networks. We constructed biological crossbar neural networks on multi-electrodes arrays (MEA) using PDMS microfluidic channels. The fluorescence optical images revealed that crossbar array was connected by axons. We measured the activation curves using the stimulus-evoked spiking probability, and found that the threshold voltage of activation curves were affected by the spike-timing-dependent-plasticity (STDP) learning. As a result, reconfigurable logic gates of AND and OR could be successfully implemented through STDP learning. Many neuromorphic systems have the crossbar array structure; thus, our study may contribute to the development of neuromorphic research.

Energy- and Area-Efficient CMOS Synapse and Neuron for Spiking Neural Networks With STDP Learning

This paper proposes CMOS synapse and neuron for use in spiking neural networks to perform cognitive functions in a bio-inspired manner. The proposed synapse can trace the eligibility of the timing relationship between pre- and post-synaptic spikes, supporting a bio-plausible local learning rule called the spike timing-dependent plasticity (STDP) in an energy- and area-efficient manner. The proposed neuron can support neural functions such as synaptic current integration, threshold-based firing, neuronal leaking, membrane potential resetting, and adjustable refractory period with improved energy and area efficiency. The STDP curve shape of the synapse and the firing rate of the neuron can be adjusted as desired. Their variability due to process, voltage, and temperature (PVT) variations can also be minimized. The proposed CMOS neuron and synapse circuits were designed in a 28-nm CMOS process. The performance evaluation results indicate that the proposed synapse reduces energy consumption and area by up to 94% and 43% compared to conventional CMOS synapses. They also indicate that the proposed neuron achieves energy and area reductions of 37% and 23%, respectively, compared to conventional CMOS neurons. An associative neural network composed of the proposed neuron and synapse was designed to verify that they together work well for performing a cognitive function of associative learning and inferencing.

Spike-timing-dependent plasticity enhances chaotic resonance in small-world network

Weak signals can be detected by a nonlinear system with an appropriate chaotic current input, known as the chaotic resonance (CR). Based on Watts–Strogatz small-world network, the effects of Spike-timing-dependent plasticity (STDP) on CR were systematically investigated. Numerical simulations showed that, under moderately strong chaotic current inputs, CR is enhanced due to the increase in average coupling strength after STDP learning. The above-mentioned phenomenon was observed even with changes in frequency and the network topology. For networks with different initial coupling strengths, their responses to weak signals after STDP learning are almost the same, indicating that networks with weaker coupling strengths have better plasticity. In addition, the effects of adjusting STDP windows on CR is also investigated. It was found that CR is promoted by STDP with a relatively larger long-time potentiation window, while, suppressed by a STDP with a relatively larger long-time depression window. Also, the area difference between long-time depression and long-time potentiation windows is highly correlated with average coupling strength after STDP learning. These conclusions might provide novel insights into weak signal detection and information transmission in different adaptive neural networks.

Optimal Resonances in Multiplex Neural Networks Driven by an STDP Learning Rule

In this paper, we numerically investigate two distinct phenomena, coherence resonance (CR) and self-induced stochastic resonance (SISR), in multiplex neural networks in the presence of spike-timing-dependent plasticity (STDP). The high degree of CR achieved in one layer network turns out to be more robust than that of SISR against variations in the network topology and the STDP parameters. This behavior is the opposite of the one presented by Yamakou and Jost (Phys. Rev. E 100, 022313, 2019), where SISR is more robust than CR against variations in the network parameters but in the absence of STDP. Moreover, the degree of SISR in one layer network increases with a decreasing (increasing) depression temporal window (potentiation adjusting rate) of STDP. However, the poor degree of SISR in one layer network can be significantly enhanced by multiplexing this layer with another one exhibiting a high degree of CR or SISR and suitable inter-layer STDP parameter values. In addition, for all inter-layer STDP parameter values, the enhancement strategy of SISR based on the occurrence of SISR outperforms the one based on CR. Finally, the optimal enhancement strategy of SISR based on the occurrence of SISR (CR) occurs via long-term potentiation (long-term depression) of the inter-layer synaptic weights.

Investigation of STDP mechanisms for memristor circuits

Memristive systems have become popular as a research field after the experimental realization of a memristor by the HP Labs research team. Researchers have demonstrated the synaptic behavior of the memristor in addition to its nonlinear and nonvolatile characteristics. Synapses play an important role in the learning process and spike-timing-dependent plasticity (STDP) is a fundamental synaptic learning rule. Because of problems with finding the memristor on the market as a discrete circuit element, researchers have designed many types of memristor circuits to emulate memristors. However, no information is available about the STDP behavior of these designed memristor emulators. This paper investigated the STDP property of some basic memristor circuits and the mathematical and simulation results demonstrated that none of them could support the STDP learning rule.

ALSA: Associative Learning Based Supervised Learning Algorithm for SNN.

Spiking neural network (SNN) is considered to be the brain-like model that best conforms to the biological mechanism of the brain. Due to the non-differentiability of the spike, the training method of SNNs is still incomplete. This paper proposes a supervised learning method for SNNs based on associative learning: ALSA. The method is based on the associative learning mechanism, and its realization is similar to the animal conditioned reflex process, with strong physiological plausibility and rationality. This method uses improved spike-timing-dependent plasticity (STDP) rules, combined with a teacher layer to induct spikes of neurons, to strengthen synaptic connections between input spike patterns and specified output neurons, and weaken synaptic connections between unrelated patterns and unrelated output neurons. Based on ALSA, this paper also completed the supervised learning classification tasks of the IRIS dataset and the MNIST dataset, and achieved 95.7 and 91.58% recognition accuracy, respectively, which fully proves that ALSA is a feasible SNNs supervised learning method. The innovation of this paper is to establish a biological plausible supervised learning method for SNNs, which is based on the STDP learning rules and the associative learning mechanism that exists widely in animal training.

Magnetic tunnel junction based implementation of spike time dependent plasticity learning for pattern recognition

Abstract We present a magnetic tunnel junction (MTJ) based implementation of the spike time-dependent (STDP) learning for pattern recognition applications. The proposed hybrid scheme utilizes the spin–orbit torque (SOT) driven neuromorphic device-circuit co-design to demonstrate the Hebbian learning algorithm. The circuit implementation involves the (MTJ) device structure, with the domain wall motion in the free layer, acting as an artificial synapse. The post-spiking neuron behaviour is implemented using a low barrier MTJ. In both synapse and neuron, the switching is driven by the SOTs generated by the spin Hall effect in the heavy metal. A coupled model for the spin transport and switching characteristics in both devices is developed by adopting a modular approach to spintronics. The thermal effects in the synapse and neuron result in a stochastic but tuneable domain wall motion in the synapse and a superparamagnetic behaviour of in neuron MTJ. Using the device model, we study the dimensional parameter dependence of the switching delay and current to optimize the device dimensions. The optimized parameters corresponding to synapse and neuron are considered for the implementation of the Hebbian learning algorithm. Furthermore, cross-point architecture and STDP-based weight modulation scheme is used to demonstrate the pattern recognition capabilities by the proposed neuromorphic circuit.

Zn2SnO4 Thin Film Based Nonvolatile Positive Optoelectronic Memory for Neuromorphic Computing

An optoelectronic synaptic device based on the ITO/Zn2SnO4(ZTO)/ITO structure is fabricated which integrates the electronic memory and optical sensing properties along with synaptic functions. The fabricated device shows over 80% optical transparency for the entire visible region (400–800 nm). Post-oxide annealing treatment is performed in a nitrogen environment at 200 °C. Significant improvements in bipolar resistive switching properties of the device with low SET voltage (+0.93 V) and long DC endurance cycles (∼12000) are observed in the annealed device. The linearity of such memristive synapse is improved for 350 training epochs with a total number of 175000 pulses. The spike time dependent plasticity learning rule for the annealed device is demonstrated through the electric field. The optical sensing capabilities of this device including photonic potentiation (responsivity: 0.52 μA/W), photonic paired pulse facilitation by adjusting time interval between two identical light pulses, learning experience behavior, and multilevel memory feature by the repetition of optical pulse for ∼103 s are demonstrated under the blue light (wavelength “λ” = 405 nm) illumination at 50 mW/cm2. Photonic potentiation and electric depression behavior of the device mimic its nonvolatile synaptic plasticity. The linear fitting of I–V curve illustrates the dominance of Schottky emission and Poole–Frenkel conduction mechanisms at high and low resistance states, respectively. The electric response of the device is explained by the oxygen vacancy based filamentary model. The trapping and detrapping of electrons during the adsorption and desorption processes of atmospheric oxygen molecules on the ITO surface are responsible for the photoconduction phenomenon. To train the Hopfield neural network (HNN) model for image processing of 28 × 28 pixels, the normalized experimental data of long-term potentiation/depression are employed to mimic the learning behavior of the human brain. The convergence of electronic data storage and optical sensor has high potential which provides a path toward the future smart invisible optoelectronics for artificial intelligence.

Memristive synaptic device based on a natural organic material—honey for spiking neural network in biodegradable neuromorphic systems

Spiking neural network (SNN) in future neuromorphic architectures requires hardware devices to be not only capable of emulating fundamental functionalities of biological synapse such as spike-timing dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP), but also biodegradable to address current ecological challenges of electronic waste. Among different device technologies and materials, memristive synaptic devices based on natural organic materials have emerged as the favourable candidate to meet these demands. The metal–insulator-metal structure is analogous to biological synapse with low power consumption, fast switching speed and simulation of synaptic plasticity, while natural organic materials are water soluble, renewable and environmental friendly. In this study, the potential of a natural organic material—honey-based memristor for SNNs was demonstrated. The device exhibited forming-free bipolar resistive switching, a high switching speed of 100 ns set time and 500 ns reset time, STDP and SRDP learning behaviours, and dissolving in water. The intuitive conduction models for STDP and SRDP were proposed. These results testified that honey-based memristive synaptic devices are promising for SNN implementation in green electronics and biodegradable neuromorphic systems.

Reliable analog resistive switching behaviors achieved using memristive devices in AlO x /HfO x bilayer structure for neuromorphic systems

Human brain synaptic memory simulation based on resistive random access memory (RRAM) has enormous potential to replace the traditional von Neumann digital computer thanks to several advantages, including its simple structure, its high-density integration, and its capabilities regarding information storage and neuromorphic computing. Herein, the reliable resistive switching (RS) behaviors of RRAM are demonstrated by engineering the AlO x /HfO x bilayer structure. This allows for uniform multibit information storage. Further, the analog switching behaviors are capable of imitating several synaptic learning functions, including learning experience behaviors, short-term plasticity, long-term plasticity transition, and spike-timing-dependent plasticity (STDP). In addition, the memristor based on STDP learning rules is implemented in image pattern recognition. These results may show the potential of HfO x -based memristors for future information storage and neuromorphic computing applications.

Earliest Experience of a Relatively Rare Sound But Not a Frequent Sound Causes Long-Term Changes in the Adult Auditory Cortex.

Sensory experience during a critical period alters sensory cortical responses and organization. We find that the earliest sound-driven activity in the mouse auditory cortex (ACX) starts before ear-canal opening (ECO). The effects of auditory experience before ECO on ACX development are unknown. We find that in mouse ACX subplate neurons (SPNs), crucial in thalamocortical maturation, respond to sounds before ECO showing oddball selectivity. Before ECO, SPNs are more selective to oddball sounds in auditory streams than thalamo-recipient layer 4 (L4) neurons and not after ECO. We hypothesize that SPN's oddball selectivity can direct the development of L4 responses before ECO. Exposing mice, of either sex, before ECO to a rarely occurring tone in a stream of another tone occurring frequently leads to strengthening the adult cortical representation of the rare tone, but not that of the frequent tone. Results of control exposure experiments at multiple developmental windows that also use only a single tone corroborate the observations. We further explain the strengthening of deviant inputs before ECO and not after ECO using a binary network model mimicking the hierarchical structure of subplate and L4 neurons and response properties derived from our data, with synapses following Hebbian spike time-dependent plasticity learning rule. Information-theoretic analysis with sparse coding assumptions also predicts the observations. Thus, relatively salient low probability sounds in the earliest auditory environment cause long-term changes in the ACX.SIGNIFICANCE STATEMENT Early auditory experience can change the organization and responses of the auditory cortex in adulthood. However, little is known about how auditory experience at prenatal ages changes neural circuits and response properties. In mice at equivalent early developmental stages, we find that auditory experience of a particular kind, with a less frequently occurring sound in a stream of another sound, alters adult cortical responsiveness, specifically of the less frequent sound. However, at the previously known critical period of development, the opposite is observed, where the more frequent sound's representation is strengthened in the adult compared with the less frequent sound. We thus show that a specific type of auditory environment can influence adult auditory processing at the earliest ages.

Neuromorphic Functional Modules of Spiking Neural Network

In the current era, design and development of artificial neural networks exploiting the architecture of the human brain have evolved rapidly. Artificial neural networks effectively solve a wide range of common for artificial intelligence tasks involving data classification and recognition, prediction, forecasting and adaptive control of object behavior. Biologically inspired underlying principles of ANN operation have certain advantages over the conventional von Neumann architecture including unsupervised learning, architectural flexibility and adaptability to environmental change and high performance under significantly reduced power consumption due to heavy parallel and asynchronous data processing. In this paper, we present the circuit design of main functional blocks (neurons and synapses) intended for hardware implementation of a perceptron-based feedforward spiking neural network. As the third generation of artificial neural networks, spiking neural networks perform data processing utilizing spikes, which are discrete events (or functions) that take place at points in time. Neurons in spiking neural networks initiate precisely timing spikes and communicate with each other via spikes transmitted through synaptic connections or synapses with adaptable scalable weight. One of the prospective approach to emulate the synaptic behavior in hardware implemented spiking neural networks is to use non-volatile memory devices with analog conduction modulation (or memristive structures). Here we propose a circuit design for functional analogues of memristive structure to mimic a synaptic plasticity, pre- and postsynaptic neurons which could be used for developing circuit design of spiking neural network architectures with different training algorithms including spike-timing dependent plasticity learning rule. Two different circuits of electronic synapse were developed. The first one is an analog synapse with photoresistive optocoupler used to ensure the tunable conductivity for synaptic plasticity emulation. While the second one is a digital synapse, in which the synaptic weight is stored in a digital code with its direct conversion into conductivity (without digital-to-analog converter andphotoresistive optocoupler). The results of the prototyping of developed circuits for electronic analogues of synapses, pre- and postsynaptic neurons and the study of transient processes are presented. The developed approach could provide a basis for ASIC design of spiking neural networks based on CMOS (complementary metal oxide semiconductor) design technology.

Photonic implementation of spike timing dependent plasticity with weight-dependent learning window based on VCSOA

Based on the well-known Fabry–Pérot approach, after taking into account the variation of bias current of the vertical-cavity semiconductor optical amplifier (VCSOA) according to the present synapse weight, we implement the optical spike timing dependent plasticity (STDP) with weight-dependent learning window in a VCSOA with double optical spike injections, and numerically investigate the corresponding weight-dependent STDP characteristics. The simulation results show that, the bias current of VCSOA has significant effect on the optical STDP curve. After introducing an adaptive variation of the bias current according to the present synapse weight, the optical weight-dependent STDP based on VCSOA can be realized. Moreover, the weight training based on the optical weight-dependent STDP can be effectively controlled by adjusting some typical external or intrinsic parameters and the excessive adjusting of synaptic weight is avoided, which can be used to balance the stability and competition among synapses and pave a way for the future large-scale energy efficient optical spiking neural networks based on the weight-dependent STDP learning mechanism.

Characterization of Generalizability of Spike Timing Dependent Plasticity Trained Spiking Neural Networks.

A Spiking Neural Network (SNN) is trained with Spike Timing Dependent Plasticity (STDP), which is a neuro-inspired unsupervised learning method for various machine learning applications. This paper studies the generalizability properties of the STDP learning processes using the Hausdorff dimension of the trajectories of the learning algorithm. The paper analyzes the effects of STDP learning models and associated hyper-parameters on the generalizability properties of an SNN. The analysis is used to develop a Bayesian optimization approach to optimize the hyper-parameters for an STDP model for improving the generalizability properties of an SNN.