Hybrid Spiking Neural Network -- Transformer Video Classification Model
This work addresses video classification for AI researchers, but it appears incremental as it builds on prior hybrid models without clear breakthrough claims.
The paper tackles video classification by proposing a hybrid spiking neural network-transformer model inspired by brain structure, achieving results on a time-series dataset but without specific performance numbers.
In recent years, Spiking Neural Networks (SNNs) have gathered significant interest due to their temporal understanding capabilities. This work introduces, to the best of our knowledge, the first Cortical Column like hybrid architecture for the Time-Series Data Classification Task that leverages SNNs and is inspired by the brain structure, inspired from the previous hybrid models. We introduce several encoding methods to use with this model. Finally, we develop a procedure for training this network on the training dataset. As an effort to make using these models simpler, we make all the implementations available to the public.