## 6 Conclusions
Furthermore, it is important to recognize that user-friendly tools for designing, training, and visualizing SNN models do exist in the literature but are in a small number. An example is the NENGO toolbox [20], which provides a comprehensive and accessible platform for building large-scale functional brain models. This tool has been instrumental in advancing SNN research by offering user-friendly interfaces and robust functionalities. Another limitation of the current version of SNNtrainer3D is its support for only the MNIST dataset, which restricts its application to picture recognition tasks. Additionally,the tool does not currently support the implementation of brain-like network structures necessary for decision-making and short-term and long-term memory, as described in the CMC model by [PERSON] et al. (2018). Future versions of SNNtrainer3D will aim to address these limitations by incorporating support for a wider range of datasets and functionalities for more complex cognitive models. Additionally, while SNNtrainer3D employs the backpropagation learning algorithm for training SNNs, it is important to note that backpropagation is not seen as biologically plausible. This presents a significant limitation for the software tool, as biologically suitable learning methods, such as STDP, are more aligned with the natural learning processes observed in the brain. Future work will explore integrating more biologically plausible learning algorithms to enhance the applicability of SNNtrainer3D in neuromorphic computing research. The current experiments conducted with SNNtrainer3D are limited by the use of only one hidden layer and a small number of neurons. This simplified architecture does not reflect the complexity of biologically plausible networks required for decision-making, short- and long-term memory, and more sophisticated sensory input processing. Future development of SNNtrainer3D will focus on supporting more complex network architectures that better mimic the structures and functions of biological neural networks, thereby expanding the tool's applicability in various cognitive and sensory processing tasks. In future work, further development is planned to enhance the application's capabilities, including more advanced visualization techniques, support for larger and more complex SNN models, and incorporation of real-time data processing to expand its applicability in various research fields. It is planned to add future features to the proposed application, such as (a) support for different layer types: this will enhance the flexibility of model design by adding support for various types of hidden layers, such as convolutional, recurrent, and pooling layers; (b) real-time training visualization: this feature will provide real-time visualization of the training process, including metrics like loss and accuracy; (c) integration with additional datasets: this feature will incorporate functionality to download and use additional datasets beyond MNIST for training and evaluation; (d bulk addition/editing of layers: this feature will let users add multiple layers simultaneously, with the option to specify the number of layers and their properties; (e) integrating different types of input and output encoding mechanisms: this will help SNNs extract meaning from temporal data, and users interpret the firing behavior of output neurons ([PERSON] et al., 2015). Future work also includes developing SNNs for inference using printed circuits on paper substrates. By training SNN models in software using SNNtrainer3D, with the provided weights, circuit designs and simulations will be realized in circuit simulators such as LTSpice and NGspice using PySpice (due to learning being done in software; there is no need for using printed memristors (Stein  
**Funding:** This research was funded by the German Federal Ministry of Education and Research (BMBF) within \"the Future of Value Creation\" program and implemented by the Project Management Agency Karlsruhe (PTKA). **Institutional Review Board Statement:** Not applicable.