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Developing computational models of neural response is crucial forunderstanding sensory processing and neural computations. Currentstate-of-the-art neural network methods use temporal filters to handle temporaldependencies, resulting in an unrealistic and inflexible processing paradigm.Meanwhile, these methods target trial-averaged firing rates and fail to captureimportant features in spike trains. This work presents the temporalconditioning spiking latent variable models (TeCoS-LVM) to simulate the neuralresponse to natural visual stimuli. We use spiking neurons to produce spikeoutputs that directly match the recorded trains. This approach helps to avoidlosing information embedded in the original spike trains. We exclude thetemporal dimension from the model parameter space and introduce a temporalconditioning operation to allow the model to adaptively explore and exploittemporal dependencies in stimuli sequences in a {\it natural paradigm}. We showthat TeCoS-LVM models can produce more realistic spike activities andaccurately fit spike statistics than powerful alternatives. Additionally,learned TeCoS-LVM models can generalize well to longer time scales. Overall,while remaining computationally tractable, our model effectively captures keyfeatures of neural coding systems. It thus provides a useful tool for buildingaccurate predictive computational accounts for various sensory perceptioncircuits.

Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes