This month, we’re shifting our focus to look at the most intelligent, flexible, efficient, and complex supercomputer in the world: the human brain. Despite the tantalizing promises of science fiction, no one has yet been able to create an A.I. with comparable complexity—one that would not just execute commands but also think, feel, and understand the world the way that humans do. There is still so much we do not understand about the brain and how its physiology connects with our thoughts, feelings, and actions. How can a 3-pound blob of fatty neurons explain all of the weird, wonderful, scary, and stupid thoughts we have every day? And what aspect of the brain’s architecture can we blame when those thoughts and feelings go awry? Resolving this disconnect between the form and function of the brain is the first necessary step in reverse engineering intelligence. Like any complex system, the best way to understand the brain is to break it down to its constituent parts and put it back together again. So this week, we are focusing on the smallest unit of the brain’s architecture—the circuitry of neurons.
There are several different classes and forms of neurons in the brain and nervous system—which we will differentiate more next week when we start talking about the system as a whole—so there really isn’t a one-size-fits-all structure for neurons. Each neuron can vary in size, shape, and number of connections depending on what best serves its function. But there are a set of core elements that all neurons share that can help us form a pretty good picture of how a standard neuron works. The simplified form of a neuron includes a set of spiny dendrites extending off the cell body, which receives incoming signals, and a long, insulated axon tail, which transmits and packages signals for transmission to the next neuron. Signals jump from neuron to neuron across the synapse gap via chemical messengers called neurotransmitters. This basic structure is essential to the primary directives of any neuron: (1) receive information (from other neurons or the outside world), (2) process information to determine whether it should be transmitted, and (3) transmit information (to other neurons or body cells).
The structure of neurons is inextricably linked to their function as information conduits, so the best way to understand that structure is to follow along with the flow of information. First, let’s discuss what “information” means in this context. Like a computer circuit, neurons encode information using a sort of binary code. A neuron is either electrically excited or at rest, and the pattern of neuron excitation is what contains the information. When a neuron receives a chemical or electrical signal from some stimulus, either internal or external, it can either become excited or not. If it becomes excited, then it can either propagate that excitement to a nearby neuron or not. A single signal becomes a network of 1’s and 0’s, open and closed circuits, that define how the brain and body interpret and respond to that stimulus.
Now let’s talk about what “electrically excited” means for a neuron because you may not be used to thinking of body cells as electrically conductive (unless of course you’ve been struck by lightning). For a neuron, electrical charge across the cell membrane arises due to the relative levels of ions on either side—specifically sodium and potassium ions, which are both positively charged. When the neuron is at rest, it has a negative charge, meaning there are more of these positive ions outside than inside. Anytime there is a separation of charge, there is corresponding potential energy—the energy that could be gained by removing that separation. Neurons have resting potential energy of around -70 mV due to the higher concentration of ions outside the cell membrane. Selective ion channels and pumps control the relative levels of ions inside and outside the neuron to maintain this negatively charged gradient.
Information first comes to the neuron through electrical or chemical signals that cross the synapse gap from adjacent neurons, body cells, or the outside world. Electrical propagation from one neuron to another is much faster than the chemical route, but it is also less nuanced and modulated. If the electrical charge is strong enough to excite the neuron, then the signal will continue to propagate from neuron to neuron—like a simple circuit. This electrical signal can also be bidirectional, which is important in systems where many neurons need to fire at once. In an electrical synapse, the axon of the first neuron is very close to the dendritic membrane of the second neuron, so changes in the ion gradient surrounding the first neuron directly impact the electrical charge of the second neuron.
More commonly, neurons send chemical signals across the synapse in the form of neurotransmitters—like dopamine, serotonin, histamine, glutamate, and acetylcholine to name just a few. When these neurotransmitters bind to special receptors on the dendrites, they can excite the neuron or inhibit excitation by opening or closing ion channels. Whether the neurotransmitter excites or inhibits a neuron depends on both the type of neurotransmitter released and the presence of that particular receptor in the dendrite. Some neurotransmitters also act as neuromodulators, influencing several nearby neurons to modulate how they interact with other neurons and respond to signals. Overall, the diversity and flexibility of chemical signaling between neurons is what makes the nervous system so uniquely adaptable.
Once the neuron receives an electrical or chemical signal, it can either become excited or not. A neuron becomes excited when positive sodium ions start flowing into the cell, decreasing the separation of charge and depolarizing the neuron. If the neuron is depolarized enough, it will be excited into an action potential that travels down the axon. An action potential is an all-or-nothing affair, meaning that the electrical charge has to reach a certain threshold (usually around -55mV) for the neuron to fully depolarize. Once this threshold is reached, sodium ion channels start opening up, accelerating the depolarization. Sodium flows into the cell, driving the potential energy up to +40 mV. After an action potential, there’s a refractory period in which the neuron needs some time to reset. Sodium channels close and potassium channels open, allowing the membrane potential to repolarize to a resting state.
The action potential moves down the long arm of the neuron’s axon as depolarization in one segment triggers depolarization in the next. Many axons are also surrounded by a series of insulating myelin sheaths. Like any good insulator, myelin speeds up the current of electrical charge moving down the axon, which can be really important when signals need to move through the nervous system quickly. But there is a trade-off. Insulation prevents the axon from losing electric charge into its surroundings, but it also prevents the axon from gaining back the electric charge that it loses from resistance. To make sure that a strong signal can make it to the end of the axon, the axon is coated in several short myelin sheaths interspersed by nodes where the axon can interact with its surroundings and further depolarize.
At the end of the neuron, the axon starts branching off into axon terminals that connect the neuron with its neighbors or with other body cells. If the cells are connected by an electrical synapse, then the action potential passes directly to the next cell via depolarization in the synapse gap. But if the cells are connected by a chemical synapse, then depolarization at the axon terminal causes calcium channels to open. As calcium floods into the axon, it signals specialized synaptic vesicles filled with neurotransmitters to fuse with the membrane of the axon terminal and release their contents into the synapse. The neurotransmitters bind with the receptors on the next neuron’s dendrites, and the signal continues propagating.
The structure of neurons shares a lot of elements we can recognize from computer wiring and electronics, yet humans are still so fundamentally different than computers. We are capable of incredible feats of creativity and intelligence that robots can only dream of (presuming a robot could dream). We are also capable of a whole host of irrational behaviors, misperceptions, and mental breakdowns that a robot would find laughable (presuming a robot could laugh). Neurons themselves may seem like nothing more than simple circuits, but as we’ll see next week, the complexity and flexibility of the nervous system really comes down to the myriad ways in which those circuits interact.
Check out last month’s series on influential Black scientists of history. Comment on this post or email me at contact@anyonecanscience.com to let me know what you think about this week’s blog post and tell me what sorts of topics you want me to cover in the future. And subscribe below for weekly science posts sent straight to your email!
