In　this　paper,　we　proposed　a　novel　low　bitrate　neural　speech　codec　based　on　sequence　modeling　networks.　The　proposed　method　consists　of　a　convolution-based　encoder　and　decoder,　a　DFSMN-Mamba　module,　and　a　vector　quantizer.　In　the　proposed　method,　a　DFSMN-Mamba　module　is　designed　by　combining　Deep　Feedforward　Sequential　Memory　Network　(DFSMN)　with　selective　state　space　model　Mamba,　which　is　used　to　model　the　input　features　in　parallel　in　both　time　and　frequency　dimensions.　An　adversarial　loss　is　used　to　train　the　entire　codec　framework,　which　enables　compression　of　speech　waveforms　into　compact　discrete　representations　at　low　bitrates.　Experimental　results　show　that　the　proposed　method　achieves　better　performance　than　the　baseline　in　both　subjective　and　objective　evaluation.　©2024　IEEE.