This problem of run-away excitation in wetware reminds me of exploding gradients in artificial neural nets. We try to handle this with data normalization, batch normalization, and gradient clipping of different sorts (although unless the clipping is incorporated into the loss as in PPO (Schulman), it's very brittle and dependent on the data and network architecture.). So I wonder if we can glean something from these inhibitory neurons for artificial nets. The opposite problem of vanishing gradients results from too much inhibition, which happens in recurrent neural nets - so it's definitely a balance. Currently PPO does the best job IMO, but is specific to reinforcement learning.