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By Root 559 0

is called summation, and there are two types: (1) temporal summation, in which two EPSPs from a single neuron are enough for the receiving neuron to reach its critical point and fire; and (2) spatial summation, in which two EPSPs from two different neurons arrive at the same time and are enough for the receiving neuron to reach its critical point and fire. This electrochemical change of voltage spike and sodium permeability propagates down the axon sequentially from the cell body to the axon terminals, and this is called, appropriately enough, propagation. The speed of propagation depends on two conditions: (1) the diameter of the axon (the bigger the faster), and (2) the myelination of the axon (the more myelin sheath there is covering and insulating the axon the faster the propagation of the impulse down it).2

Note that if the critical point for the neuron to fire is not reached, then it does not fire; if the critical point is reached then the neuron does fire. It’s an on-or-off, all-or-nothing system. Neurons do not fire “soft” in response to a weak stimulus, nor do they fire “hard” in response to a strong stimulus. They either fire or they do not fire. Therefore, neurons communicate information in one of three ways: (1) firing frequency (the number of action potentials per second), (2) firing location (which neurons fire), and (3) firing number (how many neurons fire). In this way, it is said that neurons are binary in action, analogous to the binary digits of a computer—1 and 0—which correspond to an “on” or “off” signal being passed along a neural pathway or not. If we consider these neuronal on-or-off states as a type of mental state, with one neuron giving us two mental states (on or off), then there are 2 × 1015 possible choices available to the brain in processing information about the world and the body it is running. Since we take in only a tiny fraction of this number, the brain—for all intents and purposes—is an infinite information-processing machine.

How do individual neurons and their action potentials create complex thoughts and beliefs? It begins with something called neural binding. A “red circle” would be an example of two neural network inputs (“red” and “circle”) bound into one percept of a red circle. Downstream neural inputs, such as those closer to muscles and sensory organs, converge as they move upstream through convergence zones, which are brain regions that integrate information coming from various neural inputs (eyes, ears, touch, and so forth) so that what you end up experiencing is a whole object instead of countless fragments of an image. In the upside-down image of President Obama presented in chapter 4, we initially see the integrated face holistically, and only later do we begin to notice that there is something wrong with the eyes and mouth; as explained, this is due to two different neural networks operating at different speeds—the whole face percept first, then the parts of the face second.

Binding involves so much more than this, however. There may be hundreds of percepts streaming into the brain from the various senses that must be bound together for higher brain regions to make sense of it all. Large brain areas such as the cerebral cortex coordinate inputs from smaller brain areas such as the temporal lobes, which themselves collate neural events from still smaller brain modules such as the fusiform gyrus (for facial recognition). This reduction continues all the way down to the single neuron level, where highly selective neurons—sometimes described as “grandmother” neurons—fire only when subjects see someone they know. There are neurons that fire only when an object moves left to right across your visual field. There are other neurons that fire only when an object moves right to left across your visual field. And there are still other neurons that have an action potential only when they receive EPSP inputs from other neurons that fire in response to diagonal-moving objects in your visual field. And so on up the networks goes the binding process. There are even neurons that