Neuromorphic hardware is built such that it imitates biological neural systems and systems that are very resource-efficient [14]. Spiking neural networks (SNNs) and event-driven architectures are more reliant on energy-efficient computing and are the most suitable for real-time decision-making, thus they are used mainly for edge AI and robotics applications [15].

The growing use of AI in critical industries such as healthcare and finance has led to a demand for models with a high level of transparency and interpretability [16]. The newly developed techniques of explainable AI (XAI) are giving insight into the neural network decision steps while ensuring fairness, accountability, and trustworthiness in AI systems [17].

Amidst the accelerating concern about privacy particularly that of health data Electronics and software engineers are among the world’s largest corporations [18]. As a result, federated learning appears to be the answer to the specific problem of decentralized AI model training without disclosing sensitive data. This technique is especially valuable when data privacy is critical whether in medical diagnostics, financial transactions, or personalized recommendations [19].

The integration of deep learning and control systems has led to the emergence of intelligent robots of a high level, namely autonomous robots. Drones powered by AI, robot assistants, and self-driving vehicles are using neural computing technology in the area of real-time perception, navigation, and predictive modeling, thereby expanding the potential of intelligent automation [20].

By leveraging data processing capacity, quantum computing principles are being explored to enhance the efficiency of neural networks. The introduction of quantum-inspired algorithms can lead to the provision of an adequate risk management system in finance and the forecast of climate change scenarios [21, 22].

Deep learning models often need a lot of computational power, which makes it difficult to use them in real-time situations and places with energy limitations. The design of feasible architectures, model size reduction methods, and neuromorphic computing mechanisms are important for the scalable employment of such systems [23].

Datasets used for training specific neural models can cause the models not to generalize well. Making AI systems robust to adversarial attacks, noise, and data shifts will be key in getting them deployed successfully in mission-critical applications [24].

AI-driven decisions, especially in hiring, healthcare, and law enforcement, can be unfair due to the bias of training data. Responsible AI development must include addressing bias mitigation, fairness-aware training, and ethical considerations in neural computing [25].

The need for personalization of user data has resulted in privacy risks becoming more significant in neural models. To keep users’ data secure and maintain the same level of AI efficacy, techniques such as differential privacy, homomorphic encryption, and federated learning are being studied [26].

Although neural computing has made great strides in academia, its adoption in industry is frequently held up by doubts about its scalability, dependability, and compliance with safety and other regulations [27]. The way to bring academic research and industry undertakings together is by creating strong links between them, thereby converting theoretical innovations into practical applications.

The journal strives to disseminate high-quality, original research articles that advance the neural computing frontier. We foster submissions that include new methodologies, innovative theories, and transformative applications [28].

Neural computing covers many areas such as artificial intelligence, machine learning, neuroscience, and quantum computing. In particular, IECE-TNC proudly supports research that encourages interdisciplinary collaboration, linking theoretical development with practical applications [29].

Due to the critical integration of AI and neural computing into various applications, ethical issues must be placed on priority lists. The Journal promotes ethical guidelines and the well-being of society by creating AI-driven neural computing systems that are fair, transparent, and accountable [30].

Taking into consideration the issues regarding knowledge accessibility, the IECE-TNC is for open-access research, reproducibility, and collaborative initiatives. We encourage authors to openly share their datasets, codes, and methodologies to increase the global impact of research [31].

The journal acts as a connecting link between the academic and the industrial world, encouraging researchers to work on projects that lead to real-life implementations, scalable solutions, and innovations in the field of neural computing. To facilitate the adoption of state-of-the-art neural computing methods and devices, IECE-TNC intends to forge partnerships between industry and researchers [32, 33].

Authors submit their manuscripts via the online submission system of the journal. Information regarding the submission must conform to the journal’s formatting and ethical guidelines, which also include requirements for data availability, reproducibility, and disclosure of any conflicts of interest.

The editorial team initiates a secondary investigation into the study under consideration in the journal, as well as its reliability and the way the author has adhered to ethical rules. Any manuscripts that do not fall within the scope of the journal or fail to meet the journal’s criteria could be desk-rejected here.

IECE-TNC adopts a single-blind peer-review method whereby the reviewers remain anonymous. At least two independent reviewers with expertise in the relevant research area are assigned to the manuscript. Reviewers assess the manuscript based on the criteria below:

Novelty and significance of the contribution.

The academic editor recommends the following to the Editor-in-Chief:

Accept as is (only minor editorial changes required).

The authors must have a time frame to submit revisions of their manuscripts.

If there is a need for a second round of review, the revised manuscript is reviewed again before the final decision is made. Papers that are accepted are fair and final version proofs are done, then published with a digital object identifier (DOI). Also, IECE-TNC encourages researchers to have their codes, datasets, and additional materials shared to establish transparency and reproducibility.

All the submissions are checked for plagiarism using automated tools. All manuscripts should be original and they must not be currently under consideration at any other journal or conference site.

All authors listed must possess a major amount of knowledge of the research. At the time of submission, all conflicts of interest must be highlighted.

Authors are highly encouraged to publish datasets, repositories, and methodologies online for others to duplicate the experiment. They also must indicate any use of any synthetic data, AI-generated data, or any proprietary datasets.

The journal has a detailed process for whenever unethical behaviors, like fabrication, falsification, and image manipulation take place. If such problems arise after the publication of the materials, the editorial committee might make corrections, and acknowledge the expressions of concern, however, in any case, retractions will follow if the need arises.

Research experts with not less than five years of experience are invited to join the journal’s reviewer pool and thereby, help in the manuscript assessment and the peer review process. Experts from the industry who are the first authors in prominent publications and patents may be accepted as a part of the editorial board to influence the decisions of the journal directly.

IECE-TNC is also advocating partnerships between academia and industry through presentations of applied cases and research projects which will give a voice to industry leaders on the real-world neural computing challenges. In addition, articles showcasing companies that are successfully using AI in healthcare, autonomous systems, telecommunications, finance, etc., among others will be published in the journal.

Young researchers are provided with a special and very good support system for the early stages of their careers. The journal aims to do this by:

We will make AI models that are very low energy, and they can run in real-time on very small power devices.

The IECE-TNC is dedicated to being an active player in these advances, a fundamental place for breakthrough research, knowledge dissemination, and community engagement.