1. Introduction
Neurocomputing originally referred to hardware that mimics neuroscience structures to create models of the nervous system [
1]. This concept is further extended to computing systems that operate using bioinspired computing models, including neural networks [
2] and deep-learning networks [
3]. In recent years, widespread research on neurocomputing technology has been driven by the rapid development of cognitive learning applications and the limited computing power of the Von Neumann architecture. In addition, the exponential development of big data and artificial intelligence (AI) has challenged the data processing speed and scalability of conventional computing systems. A report by PwC indicated that new computing technologies and the Internet of Everything are indispensable technologies in the march toward the fourth industrial revolution [
4]. A Deloitte report even directly stated that companies’ use of technologies related to neurocomputing, such as machine learning, is conducive to observing changes in customer population statistics [
5]. Between 2019 and 2024, the fastest growing area in smart machine technology, namely neurocomputing, will grow at a 22.2% compound annual growth rate (CAGR) [
6]. The changing face of Big Science is posing a crucial challenge to scholars. Critical topics include how human brain functions and consciousness can be recreated on supercomputers. Therefore, the development of technological platforms in the field of neurocomputing warrants further attention [
7].
Research has mostly focused on improving neurocomputing methods [
8,
9,
10] or application fields [
11,
12,
13], whereas the hotspot fields required for the sustainable development of neurocomputing technology have been overlooked. In other words, research has not explained the current situations and positions of specific technological fields on a technology map or identified technology hotspots from a comprehensive perspective. Furthermore, development of neurocomputing technology involves numerous technological fields [
2,
14,
15], and such technology has limitless development potential. A hotspot refers to a study repeatedly cited in other different studies [
16]. Namely, if one study frequently appears in other studies, it is a hotspot study. In the present study, the concept described by Mukherjee et al. [
16] is used. A technology hotspot is defined as a technological field present in various patent documents. In other words, if multiple patents belong to the same technological field, then that field is a technology hotspot. The Cooperative Patent Classification (CPC) structure was used in this study to define a technological field. Research has indicated that, under most circumstances, the CPC structure is more detailed than that of the International Patent Classification (IPC) [
17]. Namely, compared with IPC, CPC is a classification system with more detailed classifications and more additional texts.
Frost and Sullivan selected the top 50 emerging technologies from hundreds of technologies classified within nine technology clusters; their selections were based on industry adoption rate, internet protocol activity, funding, market potential, and whether the technology had the highest score on the global technology innovation index [
18]. Convolutional neural network technology, a subset of the neurocomputing field, requires precise automatic processing, and numerous industries have been attracted to using the technology and investing in relevant research. Because neurocomputing offers limitless future business opportunities, defining the hotspots in the field is crucial. Research universities and operators yearn to determine the optimal resource allocation; namely, into which technological fields they should invest funding or their research workforce. To fill in the research gap, this study conducts technology hotspot network analysis to determine the current development conditions and positioning of particular technologies and to highlight the hotspot technological fields.
This study focuses on neurocomputing technology networks and employs patent analysis to construct a neurocomputing technology network model. Because patents are the most direct form of evaluation of innovation output, they serve as an indicator of trends in technological development [
19,
20,
21]. Patent analysis methods are suitable for exploring technology transfer topics [
22,
23,
24] and evaluating the performance of industry–academia technology collaborations [
25,
26]. Thus, patent information is the most direct evaluation indicator for technological development. The present study employs patent information to identify the trends in technological development and the critical fields in neurocomputing technology.
This study differs from other studies discussing the aspects and applications of neurocomputing technology because it focuses on neurocomputing technology hotspots, constructs a technology hotspot network model, and analyzes technological development trends. The technology hotspot network model uses networks to analyze the connections between different technology classification nodes to identify hotspot technologies. The principle is that one patent may be involved in multiple technological fields (technology nodes), and different patents may include overlapping technological fields. Therefore, similar to the relations among social members in a social network, a patent technological network comprises the relations among technology nodes. In addition, the network can be studied using social network analysis [
27]. The findings of this study can serve as a reference for both academia and industry.
5. Conclusions
5.1. Discussion
This study employs network analysis to explore hotspots in the field of neurocomputing technology. The empirical research results indicate that rather than concentrating on a particular field, innovators have focused on algorithms, machine learning, methods or devices for recognizing patterns, arrangements for program control, and electric- or magnetic-based digital storage. This indicates that neurocomputing is a field of multidisciplinary technological development. In addition, through network analysis and by comparing the most frequent co-classifications of G06N3, we learned that G06N3, G06K9, G06F9, and G11C11 ranked in the top five in the network and co-classification analyses. However, although G06N20 was ranked among the top five in the network analysis, its frequency did not reach the top five in the co-classification analysis. Therefore, through network analysis, we gained the additional insight that, although machine learning (G06N60) exhibited a relatively low frequency in patents, in the overall technology network, the technology nodes it connected were diverse and exhibited characteristics of interdisciplinary application. For example, the degree centrality of G06N60 demonstrated that it was connected to many nodes. Eigenvector centrality showed whether G06N20 was connected to nodes with high centrality. Betweenness centrality revealed that G06N20 occupied critical channels in network communication, reflecting the degree to which connections among technology sets rely on G06N20. Patentee analysis further revealed that IBM (Armonk, NY, US) and Qualcomm Inc. (San Diego, CA, USA) obtained the most patents in the study period. Therefore, development in the field of neurocomputing is mainly being conducted by technology giants that are developing AI and the IoT, and neurocomputing technology has the potential for development in future markets. Driven by anticipated market profit, private-sector corporations have begun active research and development of neurocomputing technology while planning to introduce commercial neurocomputing technology applications to the market in the near future.
This study also reveals that, in addition to G06N3 (computer systems based on biological models), G11C11 (digital stores characterized by the use of particular electric or magnetic storage elements) is also undergoing considerable development. Therefore, applications that combine neurocomputing technology with digital storage are a hotspot in the future development of big data. Inspired by the neuron and synaptic mechanism theory of the human brain, scientists have developed AI computing chips that can provide timely computing to satisfy the demand for smart devices [
59]. In 2018, the AI chip market was valued at US
$6638 million. By 2025, it is expected to reach US
$91,185 million, a compound annual growth rate of 45.2% from 2019 to 2025 [
60]. Additionally, digital storage technology for neuromorphic chips, which are components that simulate neuron cells (e.g., memristors) [
61], is a relatively popular neurocomputing field that has attracted considerable attention in recent years. In addition, from the perspective of causality in technology sequences, in the early period, biological models (G06N3) with high-technology characteristics were often applied in application fields such as pattern recognition methods (G06K9). In recent years, technologies and related applications have appeared that require large amounts of computing, such as machine learning (G06N20) with high-technology characteristics and neurocomputing applications in digital-store-related technologies (G11C11) with high-application characteristics. In the aforementioned causality analysis, we learned that as communications technology and big data have developed, in addition to early applications in biometric identification, digital storage memory and smart computing have gradually attracted attention and led to more technological development and relevant applications.
From the theoretical contribution viewpoint, studies conducted in the neurocomputing field have focused on neurocomputing technology improvements [
8,
9,
10] or applications [
11,
12,
13]. However, these studies failed to highlight the key hotspots, development trends, and network distribution and context of the neurocomputing field. Neurocomputing is a core technology in numerous technology-related fields. To fill in the research gap, this study uses a novel perspective to identify the critical fields in neurocomputing.
5.2. Industrial Implications
This study provides researchers in industry and academia with valuable information and proposes a technology map of the neurocomputing field. This map highlights the key technology developments in the neurocomputing field and finds answers to resource allocation problems. This study also reveals that neurocomputing, combined with digital storage technology, is a hotspot field. In the age of big data and AI, everyday appliances (e.g., IoT devices, cars, and cellphones) will be connected to the internet, generating big data and increasing the demand for storage devices with greater capacity and memory. One possible solution to the digital storage problem is to replace integrated circuits with neuromorphic computer systems. Such systems comprise numerous nodes, each representing a neuron. Information is transferred between nodes, with each node demonstrating the ability to compute data. Neuromorphic computer systems thus have processing-in-memory functionality and can compute and store information within each “neuron.” This technology involves numerous fields, including computer information, communications, mathematics, neurobiology, and cognitive science. Because pilot studies typically require substantial equipment and investment in expert personnel, industry–academia cooperation should lead the academic field by investing in neurocomputing-related basic and applied research, establishing goals to develop practical applications, making long-term investments, and cultivating relevant talent to respond to industry demands for the future age of AI. Specifically, neural networks are part of the broader field of AI. AI studies can be divided into two main schools; namely, numerical AI and symbolic AI. Symbolic AI can be considered classical AI, which primarily manages various problems in human life related to symbols. Currently, numerical AI uses data to express a problem, and its development is supported by major technologies such as machine learning. On the basis of the aforementioned description, cognitive learning applications and numerical calculations in AI are highly related to pattern recognition, machine learning, and computer systems based on biological models in neural networks. The technological development of neural networks is key in AI.
In addition, the patent clustering analysis in this study indicated that most patents for neurocomputing technology were in optics, ICT and medicine, pattern recognition, and robot development, as well as in the development of specific computational models. With the development of the Internet of Things and big data techniques, optical communication and related uses (such as remote medical diagnosis) are gradually increasing in value. Research by MarketsandMarkets indicated that the size of the market global optical communication and networking equipment will be US
$18.9 billion in 2020. However, it will grow to US
$27.8 billion by 2025, and its CAGR will be 8.0% [
62]. In the field of optical communications, a combination of math, programming, and algorithms related to neurocomputing technology is required. This is also a crucial development direction for future neurocomputing technologies. In addition, the analysis in this study demonstrated that pattern recognition and robots are also a key development direction for neurocomputing technology. Generally, training robots to perform round trajectory tracing is more complicated and difficult compared with straight-line tracing. The improvement of robot control and signal processing models will become a main stream of business research in the future.
5.3. Limitations and Future Research Directions
This study exclusively employs patents to investigate trends in technological development. Although patents reflect the development dynamics of commercial technologies, neurocomputing development is reflected by numerous different forms in addition to patents, including theses, technology market reports, and other particular products. However, these forms were not within the scope of this study, serving as a major limitation. This study is a quantitative study and considers large-scale and comprehensive neurocomputing technology networks. In an attempt to encompass wide-ranging research topics, this study could obtain limited insight on each research topic. Future studies should analyze the content of all patents and evaluate their uniqueness. Finally, this study exclusively employs the database of the largest commercial trade market in the world, namely the USPTO database, as the source of patent information due to limited resources and funding. Although the database used in this study has a certain history and representativeness [
42,
43,
49], future researchers should include observations and verifications using information obtained from other patent bureaus (e.g., the Worldwide Patent Statistical Database of the European Patent Office or the database of the Japan Patent Office) to expand the research scope.