Developing computational models of neural response is crucial for understanding sensory processing and neural computations.

After completely excluding the temporal dimension from the model parameter space, we introduced the temporal conditioning operation to handle the temporal information. In particular, this operation enables memory-dependent processing as in biological coding circuits. Figure 6: Performances under di erent hidden state and latent space dimension settings on Movie 2 Retina 2 data. For hidden state experiments, the latent space dimension is set to 32. And for latent space experiments, the hidden state dimension is 64.

Developing computational models of neural response is crucial for understanding sensory processing and neural computations. Current state-of-the-art neural network methods use temporal filters to handle temporal dependencies, resulting in an unrealistic and inflexible processing paradigm. Meanwhile, these methods target trial-averaged firing rates and fail to capture important features in spike trains. This work presents the temporal conditioning spiking latent variable models (TeCoS-LVM) to simulate the neural response to natural visual stimuli. We use spiking neurons to produce spike outputs that directly match the recorded trains.