Information Processing Operations Research Articles

Cleaning up data work: Negotiating meaning, morality, and inequality in a tech startup

Data work—the routinized, information-processing operations that support artificial intelligence systems—has been portrayed as a source of both economic opportunity and exploitation. Existing research on the moral economy of data work focuses on platforms where individuals anonymously complete one-off projects for as little as one cent per task. However, data work is increasingly performed inside organizational settings to promote more consistent and accurate output. How do technologists and data workers construct and morally justify these arrangements? This article is based on 19 months of participant-observation research inside a San Francisco-based startup. Drawing on theories of relational work, I show how managers in San Francisco and contractors in the Philippines collaborated to “clean up” the morally questionable status of data work. Managers attempted to engineer interactions with data workers to emphasize fun and friendship while obscuring vast inequalities. Filipino data workers framed American managers as benevolent patrons and themselves as grateful clients to reinforce managers’ sense of responsibility for their well-being. By shifting attention from the structure of roles to the structure of relationships in organization-based data work, this article demonstrates the function of culture and meaning-making in both generating reliable and accurate data and reproducing status hierarchies in the tech industry. Additionally, this article's examination of the complex and often contradictory dynamics of organizational attachment and marginalization has implications for debates about how the conditions of data work can be improved.

Controlled pathways and sequential information processing in serially coupled mechanical hysterons

The complex sequential response of frustrated materials results from the interactions between material bits called hysterons. Hence, a central challenge is to understand and control these interactions, so that materials with targeted pathways and functionalities can be realized. Here, we show that hysterons in serial configurations experience geometrically controllable antiferromagnetic-like interactions. We create hysteron-based metamaterials that leverage these interactions to realize targeted pathways, including those that break the return point memory property, characteristic of independent or weakly interacting hysterons. We uncover that the complex response to sequential driving of such strongly interacting hysteron-based materials can be described by finite state machines. We realize information processing operations such as string parsing in materia, and outline a general framework to uncover and characterize the FSMs for a given physical system. Our work provides a general strategy to understand and control hysteron interactions, and opens a broad avenue toward material-based information processing.

Computação quântica: aplicações atuais e potenciais na agricultura digital

Abstract Quantum computers use the properties of quantum physics to perform information storage and processing operations. The operation of these computers involves concepts such as entanglement and superposition, which endow them with a great processing power that even surpasses that of the most powerful current supercomputers, while consuming significantly lower amounts of energy. The different studies analyzed in this review article suggest that quantum computing will have a deep impact in areas such as finance, logistics, transportation, space and automotive technology, materials science, energy, pharmaceutical and healthcare industry, cybersecurity, and agriculture. In digital agriculture, several applications that could be executed more efficiently in quantum computers for data processing and understanding of biological processes were identified and exemplified. These applications are grouped here into the following four areas: bioinformatics, remote sensing, climate modeling, and smart farming. This article also explores the strategic importance of mastering quantum computing, highlights some advantages in relation to classical computing, and presents a mapping of the services already available, enabling institutions to undertake strategic planning for the incorporation of quantum computing into their development processes. Finally, the challenges for the implementation of quantum computing are highlighted, along with some ongoing initiatives aimed at furthering research at the forefront of knowledge in this area applied to digital agriculture.

Decomposing Neural Circuit Function into Information Processing Primitives.

It is challenging to measure how specific aspects of coordinated neural dynamics translate into operations of information processing and, ultimately, cognitive functions. An obstacle is that simple circuit mechanisms-such as self-sustained or propagating activity and nonlinear summation of inputs-do not directly give rise to high-level functions. Nevertheless, they already implement simple the information carried by neural activity. Here, we propose that distinct functions, such as stimulus representation, working memory, or selective attention, stem from different combinations and types of low-level manipulations of information or information processing primitives. To test this hypothesis, we combine approaches from information theory with simulations of multi-scale neural circuits involving interacting brain regions that emulate well-defined cognitive functions. Specifically, we track the information dynamics emergent from patterns of neural dynamics, using quantitative metrics to detect where and when information is actively buffered, transferred or nonlinearly merged, as possible modes of low-level processing (storage, transfer and modification). We find that neuronal subsets maintaining representations in working memory or performing attentional gain modulation are signaled by their boosted involvement in operations of information storage or modification, respectively. Thus, information dynamic metrics, beyond detecting which network units participate in cognitive processing, also promise to specify how and when they do it, that is, through which type of primitive computation, a capability that may be exploited for the analysis of experimental recordings.

Allosteric DNAzyme-based encoder for molecular information transfer

Dynamic DNA nanotechnology plays a significant role in nanomedicine and information science due to its high programmability based on Watson-Crick base pairing and nanoscale dimensions. Intelligent DNA machines and networks have been widely used in various fields, including molecular imaging, biosensors, drug delivery, information processing, and logic operations. Encoders serve as crucial components for information compilation and transfer, allowing the conversion of information from diverse application scenarios into a format recognized and applied by DNA circuits. However, there are only a few encoder designs with DNA outputs. Moreover, the molecular priority encoder is hardly designed. In this study, we introduce allosteric DNAzyme-based encoders for information transfer. The design of the allosteric domain and the recognition arm allows the input and output to be independent of each other and freely programmable. The pre-packaged mode design achieves uniformity of baseline dynamics and dynamics controllability. We also integrated non-nucleic acid molecules into the encoder through the aptamer design of the allosteric domain. Furthermore, we developed the 2n-n encoder and the Endo IV-assisted priority encoder inspired by immunoglobulin's molecular structure and effector patterns. To our knowledge, the proposed encoder is the first enzyme-free DNA encoder with DNA output, and the priority encoder is the first molecular priority encoder in the DNA reaction network. Our encoders avoid complex operations on a single molecule, and their simple structure facilitates their application in complex DNA circuits and biological scenarios.

An efficient damage imaging method for composite structure based on self-correcting phase error compensation MUSIC algorithm

The increasing use of composite materials on aircraft structures, most of which are plate-like, has attracted much attention for damage monitoring based on guided wave as a kind of structural health monitoring (SHM) method. The multiple signal classification (MUSIC) algorithm is a directional scanning and searching method to unbiasedly estimate the signal features in terms of the orthogonal attributes between signal subspace and noise subspace. The MUSIC algorithm is gradually shifted into the field of SHM used for damage localization. However, the anisotropy of the composites and the sensor position errors cause the phase errors of the array response signal, resulting in a decrease in the accuracy of the MUSIC method. In addition, the complexity of the array monitoring network causes a significant increase in the amount of operations for signal information processing, making online real-time monitoring challenging. In order to reduce the impact of multiple forms of array errors on the MUSIC algorithm and to improve the efficiency of it, this paper proposes an efficient damage imaging method for composite structure based on self-correcting phase error compensation MUSIC algorithm, which compensates for the array phase errors caused by structural anisotropy and sensor position errors, and achieves high efficiency. The method has been verified on a stiffened composite structure, and the results show its superiority and effectiveness.

Machine Learning Based Recommendation System for Web-Search Learning

Nowadays, e-learning and web-based learning are the most integrated new learning methods in schools, colleges, and higher educational institutions. The recent web-search-based learning methodological approach has helped online users (learners) to search for the required topics from the available online resources. The learners extracted knowledge from textual, video, and image formats through web searching. This research analyzes the learner’s significant attention to searching for the required information online and develops a new recommendation system using machine learning (ML) to perform the web searching. The learner’s navigation and eye movements are recorded using sensors. The proposed model automatically analyzes the learners’ interests while performing online searches and the origin of the acquired and learned information. The ML model maps the text and video contents and obtains a better recommendation. The proposed model analyzes and tracks online resource usage and comprises the following steps: information logging, information processing, and word mapping operations. The learner’s knowledge of the captured online resources using the sensors is analyzed to enhance the response time, selectivity, and sensitivity. On average, the learners spent more hours accessing the video and the textual information and fewer hours accessing the images. The percentage of participants addressing the two different subject quizzes, Q1 and Q2, increased when the learners attempted the quiz after the web search; 43.67% of the learners addressed the quiz Q1 before completing the web search, and 75.92% addressed the quiz Q2 after the web search. The average word counts analysis corresponding to text, videos, overlapping text or video, and comprehensive resources indicates that the proposed model can also apply for a continuous multi sessions online search learning environment. The experimental analysis indicates that better measures are obtained for the proposed recommender using sensors and ML compared with other methods in terms of recall, ranking score, and precision. The proposed model achieves a precision of 27% when the recommendation size becomes 100. The root mean square error (RMSE) lies between 8% and 16% when the number of learners < 500, and the maximum value of RMSE is 21% when the number of learners reaches 1500. The proposed recommendation model achieves better results than the state-of-the-art methods.

Моделювання процесу митної переробки вантажів при проходженні транспортних засобів через державний кордон України

Developing a model of the customs cargo handling process that allows making sound technological, design, and management decisions, changing input parameters, and carrying out long-term planning based on information on the occupancy of control points, customs zones of the border customs office, customs teams, throughput capacity of checkpoints, technical means of customs control and communication, types of customs regimes, and standardisation of the duration of all components of customs cargo handling. To develop a model of the customs cargo processing process, it is advisable to use systems of parallel information processing and parallel operation of objects, which include, for example, modelling with the help of Petri nets. The model developed on the basis of Petri nets for describing the customs cargo handling process shows that, in addition to being clear and easy to use, it makes it possible to take into account various probabilistic factors. Having developed a mathematical model of customs processing of cargo when vehicles cross the state border, it can be concluded that along with technological operations (time for customs clearance at the internal customs, time for customs procedures at the checkpoint), there are non-productive operations - downtime (time for waiting for customs clearance at the internal customs, time for waiting for customs procedures at the checkpoint), which significantly affect the time of customs border crossing. The mathematical apparatus of Petri nets will allow to study the dynamics of the system under study and its behaviour under different initial conditions. It has been found that the number of customs control and customs clearance officers has the greatest impact on the time of customs processing of goods; By building a mathematical model based on Petri nets, an effect of 1.11% was achieved. As a result of applying the developed mathematical model of the customs cargo processing process, an effect of 42 minutes was obtained.

High-dimensional SO(4)-symmetric Rydberg manifolds for quantum simulation

We develop a toolbox for manipulating arrays of Rydberg atoms prepared in high-dimensional hydrogen-like manifolds in the regime of linear Stark and Zeeman effect. We exploit the SO(4) symmetry to characterize the action of static electric and magnetic fields as well as microwave and optical fields on the well-structured manifolds of states with principal quantum number n. This enables us to construct generalized large-spin Heisenberg models for which we develop state-preparation and readout schemes. Due to the available large internal Hilbert space, these models provide a natural framework for the quantum simulation of quantum field theories, which we illustrate for the case of the sine-Gordon and massive Schwinger models. Moreover, these high-dimensional manifolds also offer the opportunity to perform quantum information processing operations for qudit-based quantum computing, which we exemplify with an entangling gate and a state-transfer protocol for the states in the neighborhood of the circular Rydberg level.

Generalized Landauer Bound for Information Processing: Proof and Applications.

A generalized form of Landauer's bound on the dissipative cost of classical information processing in quantum-mechanical systems is proved using a new approach. This approach sidesteps some prominent objections to standard proofs of Landauer's bound-broadly interpreted here as a nonzero lower bound on the amount of energy that is irreversibly transferred from a physical system to its environment for each bit of information that is lost from the system-while establishing a far more general result. Specializations of our generalized Landauer bound for ideal and non-ideal information processing operations, including but not limited to the simplified forms for erasure and logical operations most familiar from the literature, are presented and discussed. These bounds, taken together, enable reconsideration of the links between logical reversibility, physical reversibility, and conditioning of operations in contexts that include but are far more general than the thermodynamic model systems that are most widely invoked in discussions of Landauer's Principle. Because of the strategy used to prove the generalized bounds and these specializations, this work may help to illuminate and resolve some longstanding controversies related to dissipation in computation.

Molecular Visual Sensing, Boolean Logic Computing, and Data Security Using a Droplet-Based Superwetting Paradigm.

Inspired by information processing and logic operations of life, many artificial biochemical systems have been designed for applications in molecular information processing. However, encoding the binary synergism between matter, energy, and information in a superwetting system remains challenging. Herein, a superwetting paradigm was proposed for multifunctional applications including molecular visual sensing and data security on a superhydrophobic surface. A Triton X-100-encapsulated gelatin (TeG) hydrogel was prepared and selectively decomposed by trypsin, releasing the surfactant to decrease the surface tension of a droplet. Integrating the droplet with the superhydrophobic surface, the superwetting behavior was utilized for visual detection and information encoding. Interestingly, the proposed TeG hydrogel can function as an artificial gelneuron for molecular-level logic computing, where the combination of matters (superhydrophobic surface, trypsin, and leupeptin) acts as inputs to interact with energy (liquid surface tension and solid surface energy) and information (binary character), resulting in superwettability transitions (droplet surface tension, contact angle, rolling angle, and bounce) as outputs. Impressively, the TeG gelneuron can be further developed as molecular-level double cryptographic steganography to encode, encrypt, and hide specific information (including the maze escape route and content of the classical literature) due to its programmability, stimuli responsive ability, and droplet concealment. This study will encourage the development of advanced molecular paradigms and their applications, such as superwetting visual sensing, molecular computing, interaction, and data security.

A Theoretical Scientific Programming Framework for Application of Linear Matrix Transformation in Plane Computer Graphics

Plane computer graphics are basic information carriers in many industrial scenarios, such as engineering simulation, automatic control, and software design. Plane computer graphics are generally a kind of digital signals guided by mathematical symbols, and each vertex of a plane computer graph forms a graph matrix. Therefore, linear matrix transformation serves as the most common algorithmic unit to realize various information processing operations. To improve ease of graph matrix computing in practical engineering scenarios, this paper proposes a theoretical scientific programming framework for application of linear matrix transformation in plane computer graphics. Firstly, theoretical basis of linear matrix transformation in homogeneous plane coordinates is displayed and analyzed. Then, the universal theorem about linear transformation of graph matrices is deduced, and corresponding proofs are also given. Finally, a case study is set up to demonstrate the main workflow of the proposed theoretical scientific programming framework. The simulative results reveal feasibility of the proposal.

(Digital Presentation) Reservoir Computing Device Using Faradaic Current of Electroactive Species in Ionic Liquids

Physical Reservoir Computing (PRC) has recently attracted significant attention as a computational method suitable for the edge AI computing, which requires both the high performance of information processing and energy conservative operation. There are two standard methods for evaluating PRC performance: the short-term memory (STM) task for the memory capacity and the parity check (PC) task for the nonlinear conversion capacity. We have developed PR device utilizing Faradaic currents generated by the redox reaction of metal ions in ionic liquids (ILs) and the impact of metal ions in ILs on the STM characteristics has been evaluated by comparing the RC device using metal-ion doped IL with that using non-doped (pure) IL [1]. In this study, we investigated the effect of Faradaic current on the STM and PC tasks by extracting the Faradaic current from the output signal when the triangular shaped input voltage pulse was applied to the PR device. It was found out that the peak shaped Faradaic current in the output signal improves not only the memory capacity but also the nonlinear conversion capability.A reservoir device with a transverse Pt/SiO2/Pt structure was prepared (Fig. 1) and solvated IL, Cu(Tf2N)2-Glyme(G3)=1:1 [2], was provided between the Pt electrodes as a reaction field where electrochemical reaction of Cu actively takes place. In order to prevent unnecessary copper deposition on the top surface of the Pt electrode, all areas other than the Pt electrode tip was covered with SiO2. As a result, a structure that allows deposition only between the terminals was realized. Furthermore, to prevent the migration of the IL by the application of an electric field, an IL pool surrounded by a resist wall was formed by patterning a spin-coated photoresist AZ5214E with a thickness of 2 µm. Au/Ti (100/10 nm) was deposited as the contact pad. The device characteristics were evaluated by cyclic voltammetry. In addition, a response of the device to the triangular pulses was investigated by using the B1530A WGFMU (waveform generator / fast measurement unit). STM and PC tasks were used to evaluate the time series data processing ability.In the present study, an artificial time-series data consisting of randomly connected binary data (0 and 1) was input to one of the Pt electrodes as the triangular shaped voltage pulse stream, while the other electrode was grounded. The positive and negative voltages were defined as 2bit data, ‘1’ and ‘0’, respectively. As shown in Fig. 2, triangular voltage pulse streams shown by the blue line were input to the reservoir device, and output current shown by red line was observed. Current peak appears when the polarity of the input signal is switched from positive to negative and vice versa (black arrow), but the peak intensity decreases when application of voltage pulses with the same polarity are continuously repeated. In addition, Cu deposition on the Pt electrode was observed. These results indicate that the origin of the peak is the Faradaic current generated by the electroactive species near the electrode. We divided output current data into two parts; the first half (yellow region), the latter half (green region). The first half corresponds to the rising part of the triangular pulse and involves the Faradaic current. On the other hand, the latter half corresponds to the descending part of the triangular pulse and is more featureless compared with the first half. We found that the accuracy of STM task was much higher when the first half was used. This tendency was also confirmed for the PC task. In PRC based on the concept of virtual nodes using a single physical device [3], the output signal complexity is considered to be related to the multidimensional transformation capability, which is one of indispensable property for PRC [4]. The present results suggest that the redox reaction of electroactive species in the IL increases the complexity of the output signal and improves the ability to extract time-series features.We thank Mr. Hiroshi Sato for supporting device fabrication processes. A part of this work was supported by "Nanotechnology Platform Program", Grant Number JPMXPF21NM0006.[1] D. Sato et al., MEMRISYS 2021, 4A-7 (2021).[2] H. Yamaoka et al., Chem. Lett., 46, 1832-1835 (2017).[3] L. Appeltant et al., Nat. Commun., 2, 468 (2011).[4] L. Appeltant et al., Sci. Rep., 4, 3629 (2014). Figure 1

Age-related changes in the interference between cognitive task components and concurrent sensorimotor coordination

Continuous sensorimotor coordinations (CSCs) such as driving, walking, using control interfaces or maintaining the body’s balance are often performed alongside concurrent cognitive tasks involving attention and executive function. A range of these task combinations show interference, particularly in older adults, but the timing, direction and reciprocity of interference is not yet understood at the level of the tasks’ information-processing operations. This paper compares the chronometry of dual task interference between a visual oddball task and a continuous visuomanual tracking task performed by young and older adults. The oddball task’s constituent operations were identified using electrophysiological correlates, and deviations in the tracking task reflected perturbations to state monitoring and adjustment characteristics of CSC tasks. Despite instructions to give equal priority to both tasks, older participants maintained a high level of resourcing of the oddball task when dual tasking whereas young participants reduced resourcing to accommodate the demands of the tracking task. Older participants had a longer period of tracking inaccuracy during the executive function component of the oddball task, and unlike in young participants, this decrement was also observed when the stimulus was not a target and the executive function of updating the target tally was not required. These detailed chronometric results clarify that age-related amplification of CSC-cognitive interference are largely due to greater inflexibility in task prioritization. Prioritization of the cognitive task over the CSC in this type of dual tasking may have safety implications in everyday task settings.

Backpropagation with biologically plausible spatiotemporal adjustment for training deep spiking neural networks

SummaryThe spiking neural network (SNN) mimics the information-processing operation in the human brain. Directly applying backpropagation to the training of the SNN still has a performance gap compared with traditional deep neural networks. To address the problem, we propose a biologically plausible spatial adjustment that rethinks the relationship between membrane potential and spikes and realizes a reasonable adjustment of gradients to different time steps. It precisely controls the backpropagation of the error along the spatial dimension. Secondly, we propose a biologically plausible temporal adjustment to make the error propagate across the spikes in the temporal dimension, which overcomes the problem of the temporal dependency within a single spike period of traditional spiking neurons. We have verified our algorithm on several datasets, and the experimental results have shown that our algorithm greatly reduces network latency and energy consumption while also improving network performance.

Dispersive optical systems for scalable Raman driving of hyperfine qubits

Hyperfine atomic states are among the most promising candidates for qubit encoding in quantum information processing. In atomic systems, hyperfine transitions are typically driven through a two-photon Raman process by a laser field which is amplitude modulated at the hyperfine qubit frequency. Here, we introduce a new method for generating amplitude modulation by phase modulating a laser and reflecting it from a highly dispersive optical element known as a chirped Bragg grating (CBG). This approach is passively stable, offers high efficiency, and is compatible with high-power laser sources, enabling large Rabi frequencies and improved quantum coherence. We benchmark this new approach by globally driving an array of $\sim 300$ neutral $^{87}$Rb atomic qubits trapped in optical tweezers, and obtain Rabi frequencies of 2 MHz with photon-scattering error rates of $< 2 \times 10^{-4}$ per $\pi$-pulse. This robust approach can be directly integrated with local addressing optics in both neutral atom and trapped ion systems to facilitate high-fidelity single-qubit operations for quantum information processing.

Early lock-in of structured and specialised information flows during neural development.

The brains of many organisms are capable of complicated distributed computation underpinned by a highly advanced information processing capacity. Although substantial progress has been made towards characterising the information flow component of this capacity in mature brains, there is a distinct lack of work characterising its emergence during neural development. This lack of progress has been largely driven by the lack of effective estimators of information processing operations for spiking data. Here, we leverage recent advances in this estimation task in order to quantify the changes in transfer entropy during development. We do so by studying the changes in the intrinsic dynamics of the spontaneous activity of developing dissociated neural cell cultures. We find that the quantity of information flowing across these networks undergoes a dramatic increase across development. Moreover, the spatial structure of these flows exhibits a tendency to lock-in at the point when they arise. We also characterise the flow of information during the crucial periods of population bursts. We find that, during these bursts, nodes tend to undertake specialised computational roles as either transmitters, mediators, or receivers of information, with these roles tending to align with their average spike ordering. Further, we find that these roles are regularly locked-in when the information flows are established. Finally, we compare these results to information flows in a model network developing according to a spike-timing-dependent plasticity learning rule. Similar temporal patterns in the development of information flows were observed in these networks, hinting at the broader generality of these phenomena.

СИСТЕМА АВТОНОМНОГО РУХУ І УПРАВЛІННЯ СЕРЕДНЬОТОНАЖНОГО ВІЙСЬКОВОГО АВТОМОБІЛЯ

The article considers the prospects of creating new types of military vehicles, including autonomous vehicles for military needs, analyzes foreign and domestic experience in the field of unmanned vehicles. The classification of levels of autonomy among the systems of assistance to the driver of modern cars is given. The technology of movement of unmanned vehicles in the «Leader-Follower» mode and the set of equipment that ensures its operation are revealed. Emphasis is placed on creating a system of autonomous movement and control of a medium-ton military vehicle and its features. The construction of the architecture of the system of autonomous movement and control of a medium-ton military vehicle is given, as well as the tasks and requirements for the subsystems that are part of it are determined. The general principle of operation of the autonomous movement and control system and the content of information processing operations that it performs are revealed. Based on the analysis, the technical characteristics of LiDAR sensors as components of the elements of the technical vision system of the autonomous motion subsystem are given. The general principle of operation of LiDAR technology and possibilities which are reached at its application in systems of autonomous movement are opened. The need to integrate various sensors to detect and recognize objects, obstacles and control the autonomous movement of the car is indicated. Keywords: medium-duty military vehicle, unmanned vehicle, autonomous movement, autonomous movement and control system, subsystem, autonomous movement subsystem, integrated navigation subsystem, control subsystem, elements of technical vision system, image interpretation unit, sensor, scanner, LiDAR, object recognition, information processing, digital elevation model, sensor integration.

Analytic Filter-Function Derivatives for Quantum Optimal Control

Autocorrelated noise appears in many solid-state qubit systems and hence needs to be taken into account when developing gate operations for quantum information processing. However, explicitly simulating this kind of noise is often less efficient than approximate methods. Here, we focus on the filter function formalism, which allows the computation of gate fidelities in the presence of autocorrelated classical noise. Hence, this formalism can be combined with optimal control algorithms to design control pulses, which optimally implement quantum gates. To enable the use of gradient-based algorithms with fast convergence, we present analytically derived filter function gradients with respect to control pulse amplitudes, and analyze the computational complexity of our results. When comparing pulse optimization using our derivatives to a gradient-free approach, we find that the gradient-based method is roughly 2 orders of magnitude faster for our test cases. We also provide a modular computational implementation compatible with quantum optimal control packages.

Backpropagation With Biologically Plausible Spatio-Temporal Adjustment for Training Deep Spiking Neural Networks

The spiking neural network (SNN) mimics the information processing operation in the human brain, represents and transmits information in spike trains containing wealthy spatial and temporal information, and shows superior performance on many cognitive tasks. In addition, the event-driven information processing enables the energy-efficient implementation on neuromorphic chips. The success of deep learning is inseparable from backpropagation. Due to the discrete information transmission, directly applying the backpropagation to the training of the SNN still has a performance gap compared with the traditional deep neural networks. Also, a large simulation time is required to achieve better performance, which results in high latency. To address the problems, we propose a biological plausible spatial adjustment, which rethinks the relationship between membrane potential and spikes and realizes a reasonable adjustment of gradients to different time steps. And it precisely controls the backpropagation of the error along the spatial dimension. Secondly, we propose a biologically plausible temporal adjustment making the error propagate across the spikes in the temporal dimension, which overcomes the problem of the temporal dependency within a single spike period of the traditional spiking neurons. We have verified our algorithm on several datasets, and the experimental results have shown that our algorithm greatly reduces the network latency and energy consumption while also improving network performance. We have achieved state-of-the-art performance on the neuromorphic datasets N-MNIST, DVS-Gesture, and DVS-CIFAR10. For the static datasets MNIST and CIFAR10, we have surpassed most of the traditional SNN backpropagation training algorithm and achieved relatively superior performance.