Deploying　energy-efficient　deep　learning　algorithms　on　computational-limited　devices,　such　as　robots,　is　still　a　pressing　issue　for　real-world　applications.　Spiking　Neural　Networks　(SNNs),　a　novel　brain-inspired　algorithm,　offer　a　promising　solution　due　to　their　low-latency　and　low-energy　properties　over　traditional　Artificial　Neural　Networks　(ANNs).　Despite　their　advantages,　the　dense　structure　of　deep　SNNs　can　still　result　in　extra　energy　consumption.　The　Lottery　Ticket　Hypothesis　(LTH)　posits　that　within　dense　neural　networks,　there　exist　winning　Lottery　Tickets　(LTs),　namely　sub-networks,　that　can　be　obtained　without　compromising　performance.　Inspired　by　this,　this　paper　delves　into　the　spiking-based　LTs　(SLTs),　examining　their　unique　properties　and　potential　for　extreme　efficiency.　Then,　two　significant　sparse　Rewards　are　gained　through　comprehensive　explorations　and　meticulous　experiments　on　SLTs　across　various　dense　structures.　Moreover,　a　sparse　algorithm　tailored　for　spiking　transformer　structure,　which　incorporates　convolution　operations　into　the　Patch　Embedding　Projection　(ConvPEP)　module,　has　been　proposed　to　achieve　Multi-level　Sparsity　(MultiSp).　MultiSp　refers　to　(1)　Patch　number　sparsity;　(2)　ConvPEP　weights　sparsity　and　binarization;　and　(3)　ConvPEP　activation　layer　binarization.　Extensive　experiments　demonstrate　that　our　method　achieves　extreme　sparsity　with　only　a　slight　performance　decrease,　paving　the　way　for　deploying　energy-efficient　neural　networks　in　robotics　and　beyond.　©　2024　IEEE.