Integration: All-or-none and summation

Modified: 2026-08-05 2:53 PM CDST

• The all-or-none law refers to the activity of a single neuron.
  • A neuron will either fire or it will not.
  • When it does fire, it fires with the same intensity every time.
  • In other words, it always "says" the same thing.
• Consider the implications for communication in the nervous system.
  • Because the message is the same, any variability in the message must come from somewhere else.
  • That variability comes from the rate of nerve impulses.
• For example, suppose I had a single-cell recording device connected to a neuron in my arm.
  • Then, suppose that a mosquito landed on my arm right where that neuron could detect it.
  • Before the mosquito landed, that neuron would fire on occasion, just to fire.
  • All neurons do that; that rate is called their spontaneous rate of firing.
  • When the mosquito landed, the neuron might increase its rate somewhat.
  • If the mosquito bit me, then the rate might increase dramatically, causing me to swat it.
• The point here is that it is not the firing that conveys information; rather, it is the rate of the firing.
• Finally, no neuron can exceed a maximum, discrete rate.
  • The reasons for that are the refractory periods.
  • Remember, the axon has to repolarize itself before it can depolarize again.
• The second integrative principle is the phenomenon of excitation and inhibition.
  • Recall that a pre-synaptic neuron may cause the post-synaptic neuron not to fire.
  • Not firing can convey just as much information as firing.
  • For example, some neurons have a rapid spontaneous rate of conduction.
  • The mosquito landing on my arm could have reduced that rate; biting could have reduced it to the point of causing me to swat it.
• Think of the following example from a typical western movie.
  • Two cowpokes, surrounded by night, are listening to the distant drums.
  • Then one says to the other, "Listen".
  • The other replies, "I don't hear anything".
  • The first answers, "I know."
  • The drums have stopped, and that conveys information, does it not?
• So, excitation and inhibition have the property of vastly increasing the information capacity of the nervous system because a decrease or an increase in the rate of nerve conduction conveys information.

Earlier, we learned about refractory periods and that stronger than normal excitation could fire a neuron during the relative refractory period.
How do we get stronger than normal excitation?
There are two ways, and they can work singly, or in combination with each other.

• The first method is temporal summation.
  • The repeated excitatory firing of the pre-synaptic neuron makes it more likely for the post-synaptic one to fire.
  • Conversely, if the pre-synaptic neuron were delivering an inhibitory message, the post-synaptic neuron would be less likely to fire.
• The second method is spatial summation.
  • For example, if three pre-synaptic neurons simultaneously each deliver an excitatory message, that increases the likelihood that the post-synaptic neuron will fire.
  • In reality, a post-synaptic neuron might be analyzing input from many neurons that may be delivering EPSPs and IPSPs.
  • That neuron makes a "decision" to fire or not based on that input.
• Both temporal summation and spatial summation combined with excitation and inhibition can lead to immensely complex neural circuits with as little as 20 neurons.
  • Analyzing real neural circuits from the bottom up is an impossible task because of the vast number of neurons involved.
  • In fact, the creation of artificial neural networks on computers is now a hot research area.
  • But, analysis of those circuits must be done by other computers because of the great number of possibilities involved.