Hello Dilkhaz Haji Mohammed

Yes, it is certainly possible to integrate Spiking Neural Networks (SNNs) with other AI technologies to create more powerful and versatile AI systems. SNNs are a type of artificial neural network that closely mimics the way biological neurons communicate through spikes. They may be used in areas like Hybrid Architectures, Sensor Fusion, and Cognitive Computing.

This paper may help :

Shen, J., Xu, Q., Liu, J. K., Wang, Y., Pan, G., & Tang, H. (2023, June 26). ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 37(1), 86–93. https://doi.org/10.1609/aaai.v37i1.25079

Dilkhaz Haji Mohammed,

By combining SNNs with other AI technologies, we can create even more powerful AI systems that have the potential to revolutionize various industries. For example, in the field of healthcare, an integrated AI system could use SNNs to analyze complex medical data and provide accurate diagnoses in real-time. This would not only improve patient outcomes but also alleviate the burden on healthcare professionals.

Moreover, integrating SNNs with other AI technologies could greatly enhance autonomous vehicles. SNNs excel at processing sensory information and making quick decisions based on real-time data. By incorporating this capability into self-driving cars, we can significantly improve their ability to navigate complex road conditions and avoid accidents.

Another area where integrated AI systems could have a profound impact is in cybersecurity. With cyber threats becoming increasingly sophisticated, traditional rule-based algorithms often fall short in detecting and preventing attacks. However, by combining SNNs with machine learning algorithms, we can create more adaptive and robust cybersecurity solutions that can identify emerging threats and respond effectively.

In conclusion, integrating spiking neural networks with other AI technologies holds great promise for creating even more powerful AI systems. Whether it's improving healthcare outcomes, enhancing autonomous vehicles' capabilities or strengthening cybersecurity measures – the possibilities are endless. It is crucial that researchers and developers continue exploring this integration to unlock the full potential of artificial intelligence for a better future.

There are several problems integrating SNN and traditional ANNs:

(1) SNNs process physical time while ANNs are not capable of processing time (there are series but it is not the same taking into account STDP learning), time encoding consumes usually a lot of resources,

(2) Spike-timing-dependent plasticity STDP is not possible in traditional ANNs due to limitations of the Rosenblatt model of neurons,

(3) As a result of the listed above in ANNs we usually do not use feedback loops that are crucial for SNN self-learning.

Advantages of SNNs: (1) self-learning, (2) energy efficiency, (3) real-time processing in spiking chips.

Taking into account all listed above I would suggest accelerating research in SNNs and leaving outdated ANNs to the fields where they are good: NLP and CV.