Modified: 2020-06-09
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. If three pre-synaptic neurons simultaneously each deliver an excitatory message, that increases the likelihood that the post-synaptic neuron will fire.
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.