Connectome (44 page)

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millions of miles:
The brain is over a million cubic millimeters in volume, and a large fraction of that is cortex. According to Braitenberg and Schüz 1998, a cubic millimeter of cortex contains several miles of neurites.

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single axon, long and thin: This description holds for a very common type of neuron, the pyramidal neuron of the cortex. However, there are many other types of neurons, which have different appearances. The dendrite–axon distinction is not even valid for some types of neuron, especially in invertebrate nervous systems. For these neuron types, each neurite both sends and receives synapses.
This description holds for a very common type of neuron, the pyramidal neuron of the cortex. However, there are many other types of neurons, which have different appearances. The dendrite–axon distinction is not even valid for some types of neuron, especially in invertebrate nervous systems. For these neuron types, each neurite both sends and receives synapses.

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typical synapse is from:
But there are also synapses from axon to cell body, dendrite to dendrite, axon to axon, and pretty much any other variation you can think of.

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Figure 17:
This figure shows a brief segment of the voltage signal recorded from a neuron in the hippocampus of a rat exploring a maze. The experiment is described in Epsztein, Brecht, and Lee 2011.

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above the static:
After the telegraph, the telephone was invented for analog communication—that is, the transmission of voice signals without encoding them into pulses. But now the telephone system has become digital again, utilizing something like Morse code. The encoding and decoding are invisible to the user because they are done quickly and automatically by electronic circuits rather than human operators. Why would our sophisticated telephone systems return to the style of communication used in the primitive telegraph? One reason is that today's systems are designed to transmit information at the highest possible rate. This requires operating at the limits set by noise, so the best strategy is again digital.

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spike triggers secretion:
I say “passing” because synapses mostly occur at locations along the axon, so that spikes propagate past them. Some synapses are located at axonal dead ends, however, so that spikes terminate at them.

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a synapse converts: How receptors transform chemical signals into electrical ones will be explained in Chapter 6.
How receptors transform chemical signals into electrical ones will be explained in Chapter 6.

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toward the cell body:
This is known as the Law of Dynamic Polarization. Neuroscientists sometimes violate the law by using electrical stimulation to initiate a spike that travels backward along the axon toward the cell body. Such “antidromic” propagation is opposite the normal direction, proving that signal transmission along the axon is two-way.

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cells that support them:
The nervous system also contains non-neuronal cells, known as glia. These come in a number of types, and are absolutely essential for keeping the brain alive and functioning. I will take the traditional view that glial cells are like the crew, supporting the cast of neurons that star in the mental show. Neurons and glial cells are about equally numerous (Azevedo et al. 2009). Much more about glia can be found in Fields 2009.

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synapses onto muscle fibers:
These are called neuromuscular junctions, to contrast them with ordinary synapses between neurons.

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“To move things”: Sherrington 1924.
Sherrington 1924.

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190 stations:
Bradley 1920.

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synapses are weak:
Some contrarians believe that there are a small number of strong synapses, and these are the important ones for brain function.

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cannot typically relay a spike:
Even if synapses are weak, it's possible for a single neuron to drive another neuron to spike. The neurons need only be connected by a large number of synapses. However, this situation is apparently rare in practice.

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synapses made by the axon:
Actually, synapses behave stochastically. With every spike, some randomly fail to secrete neurotransmitter.

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all possible pathways:
For the snake, your eye communicates with your legs and not your salivary glands. For the steak, it's the other way around. In telecom networks, such selectivity is achieved through the operation of
routing.
Every message has an address, which is separate from the content of the message. This is most obvious when you mail a letter. You write the address on the outside of an envelope, the content on the paper within. Similarly, you enter the address of a telephone by punching in its number to request a call, but it's the ensuing conversation that contains the content. A node in the network routes an incoming message by looking at its address and relaying it to another node that is closer to the destination specified by the address. A message takes a pathway through the network determined by these routing decisions. These are made by human workers in the post office, and by devices called switches in the telephone network. Even if a single pathway could relay spikes, it's not obvious how the nervous system could route spikes through the right pathway to reach a specific destination. Axons aren't doing any routing; they just send spikes indiscriminately to all their synapses. Perhaps routing could be found elsewhere in the neuron, but there is a fundamental problem with the whole idea. Since a spike is merely a pulse, it's unclear how it could carry both the content and the address of a message. This is why telecom networks are probably not such a good metaphor for the brain. That being said, this theoretical argument cannot exclude the possibility that messages consist of sequences of spikes, that assemblies of neurons can function as routing devices, and that the brain is like a communication network when examined at a higher level of organization. In fact, some theorists still contend that the routing operation is helpful for understanding brain function (Olshausen, Anderson, and Van Essen 1993).

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If dendrites lack spikes:
As explained in Häusser et al. 2000 and Stuart et al. 2007, researchers have challenged the traditional conception that dendrites don't spike. Experiments on neurons kept alive in slices of brain have demonstrated spikes in dendrites. If this phenomenon also occurs in intact brains, it could be that each dendrite of a neuron takes a vote of its synapses, and then the cell body takes a vote of its dendrites. This would be analogous to the American presidential election, in which the people of each state vote in the general election, and then the states vote in the Electoral College. In principle, it's possible for a candidate to win this two-stage election without winning the popular vote.

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quantifies the weight:
This is a simplification, as the notion of the “strength” of a synapse is more complex than can be summarized in a single number.

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“weighted voting model”:
Engineers call this the “linear threshold model” of a neuron, to contrast the summation in voting, which they call a “linear” operation, with thresholding,
a “nonlinear” operation:
Yet another name for the model is “simple perceptron.”

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ranging from milliseconds:
This is yet another dimension in which chemical synapses are more versatile than electrical synapses.

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Inhibitory synapses: More-direct evidence for the importance of synaptic inhibition comes from studies of movement. Muscles are generally organized in pairs with opposing effects. The biceps and triceps muscles, which are on either side of your upper arm, are one example. The biceps bends your elbow; the triceps extends it. Your nervous system is constantly sending spikes to both the biceps and the triceps. This is why your muscles are not completely relaxed at rest; they have some degree of “muscle tone.” When you bend your elbow, your nervous system sends more spikes to your biceps, causing it to contract, and simultaneously sends fewer spikes to your triceps, causing it to relax. One reason for this reduction is that the motor neurons controlling the triceps receive inhibition from synapses.
More-direct evidence for the importance of synaptic inhibition comes from studies of movement. Muscles are generally organized in pairs with opposing effects. The biceps and triceps muscles, which are on either side of your upper arm, are one example. The biceps bends your elbow; the triceps extends it. Your nervous system is constantly sending spikes to both the biceps and the triceps. This is why your muscles are not completely relaxed at rest; they have some degree of “muscle tone.” When you bend your elbow, your nervous system sends more spikes to your biceps, causing it to contract, and simultaneously sends fewer spikes to your triceps, causing it to relax. One reason for this reduction is that the motor neurons controlling the triceps receive inhibition from synapses.

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tends to “inhibit” spiking:
In a more accurate definition, excitatory versus inhibitory depends on whether the so-called reversal potential for the synapse is above or below the threshold voltage at which a neuron spikes.

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another kind of synapse:
An electrical synapse, or gap junction, consists of a cluster of molecules, each of which is a tiny tunnel connecting the interior of one neuron to the interior of the other.

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other limitations:
Electrical synapses are less versatile in many other ways. The duration of synaptic currents is fixed and short. Electrical current generally flows in both directions, though it may flow more readily in one of them. If two-way sounds superior to one-way, you might regard electrical synapses as more powerful than chemical synapses. But two-way communication between neurons can be established by two chemical synapses, one in each direction, while electrical synapses cannot establish one-way communication. Therefore two-way communication is actually a limitation. Electrical synapses are known to play an important role when a population of neurons needs to generate spikes simultaneously. Fast bidirectional communication makes sense for achieving such synchronicity. Electrical synapses exert only electrical effects, while chemical synapses can additionally trigger molecular signals within the receiving neuron. The extra steps in chemical transmission may slow it down, but they also allow for amplification, and modulation by other processes.

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how should our voting model be revised:
A simpler effect of inhibition on pathways almost goes without mentioning: A single pathway containing a mixture of inhibitory and excitatory synapses can't relay spikes, however strong the synapses may be.

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veto many excitatory synapses:
In 1943, the theoretical neuroscientists Warren McCulloch and Walter Pitts introduced the first voting model of a neuron. The McCulloch–Pitts model adhered to the slogan “One synapse, one vote,” but only for excitatory synapses. An inhibitory synapse was allowed to have complete veto power over many excitatory synapses. It can be shown that the McCulloch–Pitts model is a special case of the weighted voting model, just by giving the inhibitory synapse a very large weight.

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makes only excitatory synapses:
This follows from Dale's Principle, because a given neurotransmitter generally has the same electrical effect on any neuron, either always excitatory or always inhibitory. (The sign of the electrical current depends on the molecular machinery on the receiving side of the synaptic cleft.)

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A similar uniformity:
Also, the uniformity does not extend to strength; a neuron can make a strong synapse onto one neuron and a weak synapse onto another.

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most neurons are excitatory:
The split is 80–20 in the cortex.

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increases its selectivity:
Here's another way of thinking about the significance of selective spiking. Nature has gone to the trouble of preventing crosstalk between wires. Why do this when signals are mixed at every neuron by convergence and divergence? The answer is that selectivity is preserved because neurons often fail to spike.

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albeit a very different kind:
As computers have pervaded our everyday lives, we have lost sight of how strange they really are. A digital computer is a machine like no other, because of its universality. Like an infinitely versatile Swiss Army knife, a computer can perform any kind of computation if equipped with the right software. (This is an informal statement of the Church–Turing thesis, which is formulated for an abstract computing model known as a universal Turing machine. It's something like a modern digital computer with a hard disk of infinite capacity.) This is very different from your toolbox, which contains a hammer, a screwdriver, a saw, a wrench, and a drill, all of which are specialized for different functions. Since brain regions are specialized for particular functions, the brain is more like your toolbox than like a universal computer. Just as the structures of a saw and a hammer are closely related to their functions in carpentry, the structures of brain regions are likely to be closely related to their functions.

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deviate somewhat from the voting model:
The weighted voting model is only an approximation to a real neuron, which may be more complex. Bullock et al. 2005 briefly describes inaccuracies of the approximation, and Yuste 2010 is a book-length review of the properties of dendrites.