tf sumpooling layer 1d vs 2d

I am currently working on a paper by Sturm et al. (2016) published in the Journal of Neuroscience trying to replicate their results using python and TensorFlow, Keras libraries.

I have strong doubts about whether if I have understood the way they designed the model as explained in section 2.1.

I couldn't fully understand the following points because of my lack of experience in the field.

1. Did they use 1d or 2d sumpooling layers?
2. What were the exact output shapes after each layer?
3. Did they use a categorical format for output?
4. Did they use dropout and any other layers?

How would you go about designing the described model?

Thank you in advance for your valuable comments.

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