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1. RWKV: Reinventing RNNs for the Transformer Era

2. https://mattmahoney.net/dc/textdata.html

3. https://openwebtext2.readthedocs.io/en/latest/

4. Surrogate Gradient Learning in Spiking Neural Networks

5. Deep Residual Learning in Spiking Neural Networks

6. https://github.com/ridgerchu/SpikeGPT

7. Reformer: The Efficient Transformer

8. Synthesizer: Rethinking Self-Attention in Transformer Models

9. Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

10. Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers

11. Generating Sequences With Recurrent Neural Networks

12. Single Headed Attention RNN: Stop Thinking With Your Head

13. Attention Is All You Need

14. https://github.com/idiap/fast-transformers

15. Wikipedia Bibliography:

    1. Spiking neural network

    2. https://en.wikipedia.org/wiki/Computer_vision  :

       https://en.wikipedia.org/wiki/Computer_vision

    3. Backpropagation

    4. Transformer (deep learning architecture)

    5. Computational complexity theory

    6. https://en.wikipedia.org/wiki/Neuromorphic_engineering  :

       https://en.wikipedia.org/wiki/Neuromorphic_engineering