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Assume the following:

  • there are at least 10^11 neurons in the human brain
  • there are approximately 10^14 synaptic connections in the human brain (because on average each neuron gets inputs from approximately 1000 other neurons)
  • synaptic delay is approximately 1-2ms (but for the sake of it we can also assume an order of magnitude less, so 0.1ms)

A problem that revealed itself to me: It would "only" take 1000 (or 10000 if we use 0.1ms as synaptic delay) serial synaptic connections in order to generate a lag of 1s. Considering that there are 10^11 neurons in the brain, the number of neurons you need firing sequentially in order to generate significant lag (>1s) seems tiny compared to the overall amount of neurons in the brain.

My questions: Are there series of neurons (firing from the time of input to the time after processing) greater than 1000 (or 10000)? How are there series (not) greater than 1000 (or 10000) neurons?

P.S. I'm talking about chemical synapses which I assume to make up the bulk of neural transmission in this case

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    $\begingroup$ Welcome to the site. Where did you get your information? Particularly, where did you get the idea that all the neurons in the brain are connected in series? How on earth would the brain process information at all if there weren't millions of different short pathways that the "signal" could take? $\endgroup$
    – rotaredom
    Commented Jun 11, 2021 at 12:03
  • $\begingroup$ The information is from the Springer Handbook of Bio- and Neuroinformatics. I do not (!) think that the neurons in the brain are all connected in series. I'm just asking how come that there isn't significant lag as it would take only a series of 1000 or 10000 neurons in order to create a lag of 1s. My question is more like: how aren't there series of neurons that are greater than 1000 or 10000? I basically have an idea how to answer my question but I want to compare it with what people on here say. I'm not a neuroscientist and my reasoning doesn't suffice for me personally. $\endgroup$
    – JimPanSee
    Commented Jun 11, 2021 at 12:49
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    $\begingroup$ ...rarely would it be the effect of one neuron alone causing another neuron to fire; the whole idea behind processing is not to send information through the most processing centers possible; it's to take information from the most sources possible and integrate it into a single response. That said, some processing does take much longer than 1s if it has to go through lots of circuitry, sometimes multiple times - think of doing mental arithmetic - you have to store numbers and recover them, process them, etc. $\endgroup$
    – rotaredom
    Commented Jun 11, 2021 at 13:42
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    $\begingroup$ ...and some series of neurons are indefinitely long - for instance, you can close your eyes and start imagining a story, and technically there is no limit to the number of neurons in circuit which you can make fire. You could spend two hours imagining a story (or whatever) and there would be way more than 10000 neurons that fired dependant on others. Anyway not an answer; just food for thought which will hopefully help you clarify the question a bit $\endgroup$
    – rotaredom
    Commented Jun 11, 2021 at 13:46
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    $\begingroup$ Lags of 200-600 ms are pretty common. Have you ever read studies that measure reaction times? That said, total synaptic delays are typically much longer than 0.1 ms (that number is just for one step in the process). 1-10 ms is a better lower estimate. $\endgroup$
    – Bryan Krause ♦
    Commented Jun 11, 2021 at 14:10

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The brain is massively parallel. There are a lot of recurrent connections and feedback loops that are important for setting expectations, building a model of the world, and learning, but the pathway for stimulus to response is quite simple (see the diagrams in Grossberg & Pilly, and the latency profile in Schmolesky et al). It has to be, because if you are slow you die.

For example, a commonly used psychophysical task is to detect a motion stimulus and response with a saccade (eye movement). For this path you have a couple synapses in the retina, then the LGN, V1, MT, LIP, FEF, then a couple synapses in the brainstem and the muscle. For some of the cortical stops you might have responses that themselves depend on multiple synaptic steps. So let's say on the order of 10-30 synapses for that sort of rapid task with a response within a couple hundred ms. A good chunk of that is actually phototransduction in the retina which is quite slow (which is why you don't see still images a 24-30 fps movie).

DiCarlo, J. J., & Maunsell, J. H. (2005). Using neuronal latency to determine sensory–motor processing pathways in reaction time tasks. Journal of neurophysiology, 93(5), 2974-2986.

Gold, J. I., & Shadlen, M. N. (2007). The neural basis of decision making. Annu. Rev. Neurosci., 30, 535-574.

Grossberg, S., & Pilly, P. K. (2008). Temporal dynamics of decision-making during motion perception in the visual cortex. Vision research, 48(12), 1345-1373.

Schmolesky, M. T., Wang, Y., Hanes, D. P., Thompson, K. G., Leutgeb, S., Schall, J. D., & Leventhal, A. G. (1998). Signal timing across the macaque visual system. Journal of neurophysiology, 79(6), 3272-3278.

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  • $\begingroup$ Parallelity indeed solves the problem of massive difference in magnitude between overall neuron count in the brain and serial neuron count from input to result of process for me. However, do you have more insight or a source about counts of serial neurons? Your answer probably is enough to resolve my question, I'm just dumbfounded why neural transmission is so slow and long serial neural transmission has the potential to be excruciatingly slow. Our brains are amazing but a complex instruction in Assembly like ldw (loading a content) takes only 100 ns with simpler instructions being much faster $\endgroup$
    – JimPanSee
    Commented Jun 12, 2021 at 20:47
  • $\begingroup$ Brains are biology. I would recommend learning how they work from the ground up (get a neuroscience textbook) rather than trying to understand them like you would silico computing. The brain-computer analogy breaks down really fast. $\endgroup$
    – Bryan Krause ♦
    Commented Jun 12, 2021 at 20:52

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