Hi,
As a newcomer to SNN and SLAYER, I am a bit confused about the implementation of incremental prediction in SNN. Initially, I thought it referred to the outputs at different time steps during the same inference procedure. However, I am unable to understand how to display the outputs of SNN at each time step in a single inference procedure since the PyTorch version of SLAYER only provides a forward() function without any details of each timestep. Now, I am thinking that the result is achieved through multiple inference procedures, each taking different lengths of original spike trains as input (i.e., different ∆T_test values). I am not entirely sure if this is correct, could you please confirm?
Hi,
As a newcomer to SNN and SLAYER, I am a bit confused about the implementation of incremental prediction in SNN. Initially, I thought it referred to the outputs at different time steps during the same inference procedure. However, I am unable to understand how to display the outputs of SNN at each time step in a single inference procedure since the PyTorch version of SLAYER only provides a forward() function without any details of each timestep. Now, I am thinking that the result is achieved through multiple inference procedures, each taking different lengths of original spike trains as input (i.e., different ∆T_test values). I am not entirely sure if this is correct, could you please confirm?