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My understanding is that neurons are prevented from firing too often by a 2-part refactory period: an immediate inability to fire again for a period of time followed by an increased threshold for firing for an extended period of time.

My question is simply whether or not there is an additional "collective" aspect of the refactory period or other mechanism which prevents too many firing based on frequency within a period of time. In other words, the refactory period as I understand it is the same each time a neuron fires, regardless of whether or not it has repeatedly fired as often as possible for a period of time before hand. I'm asking whether there's a mechanism that takes into account how many times the neuron has fired within x timeframes collectively to prevent further firings or weakens the output, sort of like a "resistance" (in which case it would be a mechanism having more to do with the synapse than the action potential / refactory period).

Do neurons feature a collective “resistance” to firing too often beyond the refactory period?

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  • $\begingroup$ My last question was deemed too broad and low effort, hopefully this one is soecific enough and demonstrates a reasonable understanding of the relevant concepts :) $\endgroup$ – Viziionary Mar 29 '17 at 20:39
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    $\begingroup$ I think this one could be answered, though again, it might be more broad than you even realize. Another benefit to doing some textbook study on your own is that you would learn some of the terminology to use which will help you search for answers on your own. A question like this should probably include terms like "plasticity" or "homeostasis" and the issues you talk about at the end are a completely different area of study than the issues you begin with. :) $\endgroup$ – Bryan Krause Mar 29 '17 at 20:46
  • $\begingroup$ @BryanKrause you're right, I struggle with lack of terminology in asking neuroscience questions, I'll have to study up. So far I've been winging it with a highschool Biology knowledge level plus some Wikipedia reading and Youtube videos lol. With that I've been able to create a neural simulation from scratch that teaches itself to play Super Mario Bros, but I'm having to go into deeper understandings to push the limits beyond that. $\endgroup$ – Viziionary Mar 29 '17 at 21:28
  • $\begingroup$ At some point you will have to decide if you want to get serious about learning neuroscience, or if you are only interested in machine learning. Although neuroscience is great for motivating machine learning, and there is crosstalk between the fields, they are really unique fields in their own right. There are a lot of things that biological neurons can do that no one would ever use in an artificial network unless they were specifically trying to model the biological one, and there are a lot of things that an artificial network can do that biology just doesn't. $\endgroup$ – Bryan Krause Mar 29 '17 at 21:30
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    $\begingroup$ Let us continue this discussion in chat. $\endgroup$ – Bryan Krause Mar 29 '17 at 21:39

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