This　paper　is　divided　into　three　parts,　firstly,　it　explores　the　implementation　and　efficacy　of　spiking　neural　networks　(SNNs)　in　various　image　processing　tasks.　The　primary　focus　is　on　leveraging　the　biological　properties　of　spiking　neurons　to　enhance　image　classification　and　feature　extraction.　Key　experiments　detailed　in　the　paper　include　the　use　of　the　Caltech　101　dataset　to　evaluate　SNNs　for　face　and　motorcycle　image　recognition,　achieving　high　accuracy　rates　even　with　limited　training　samples.　Additionally,　the　paper　discusses　the　integration　of　denoising　autoencoders　and　hardware　pulse　neural　networks　to　improve　image　quality　and　recognition　accuracy　under　noisy　conditions.　Another　significant　study　presents　a　novel　SNN　model　inspired　by　the　human　visual　system,　which　performs　wavelet　transforms　for　texture　analysis　and　classification.　Lastly,　a　spatio-temporal　interactive　image　classification　model　is　introduced,　showcasing　improvements　in　memory　efficiency　and　training　speed　across　multiple　datasets,　highlighting　the　potential　of　SNNs　for　low-power,　high-efficiency　applications　in　complex　environments.　©　The　Institution　of　Engineering　&　Technology　2024.