One role of theory is in guiding future experiments: What should we aim to measure? Which experimental results should we be surprised about? I will argue here that simple random networks models of neural dynamics explain, to the experimental accuracy, many features of large population recordings in a variety of nervous systems. Observation of such features is thus not surprising in the context of random null models. I will discuss how this affects, or can affect, our interpretation of experiments, our choices of which experiments to do next, and how it raises hopes for building next-level theories of nervous systems, finally leaving the neuron behind.
1. Explain how large simple neural networks can result in complex experimental recordings.
2. Introduce the concept of latent variables dynamics
3. Define coarse-graining modeling approaches