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Name of neurons affecting or being affected by a neuron?

Name of neurons affecting or being affected by a neuron?


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This question is about terminology:

By which established and catchy terms are the sets of neurons with respect to a given neuron $N$ called which

  1. directly affect $N$ synaptically

  2. are directly affected by $N$ synaptically?

These terms came to my mind, but I am not sure which ones are most commonly used:

  • the presynaptic/postsynaptic neurons of $N$
  • the afferent/efferent neighbours of $N$
  • the afferents/efferents of $N$
  • the incoming/outgoing neurons of $N$
  • the neurons projecting on/to $N$ vs. the neurons projected on/to by $N$

Or is there no such established term, and anything goes, depending on the context?


At the simplest level, the terms 'pre-synaptic'/'post-synaptic' neurons should cover what you wish to describe. 'Afferent'/'efferent' are usually used to refer to axonal projections, usually between functionally distinct areas, or nerves. However, I wouldn't be too surprised if someone used these terms to refer to synaptically connected populations although I would consider it unusual. Simply, 'connected neurons' is also a term frequently used.


I will now go into a tangent to give you some background that should help you formulate an informed view about the vocabulary being used:

In areas that display a high degree of local connectivity, like the cerebral cortex and the hippocampus, any given neuron is connected with many different neurons. Hebb's theory of cell assemblies posits that a group of highly connected neurons may form over time in order to subsist a specific function. Given the high degree of connectivity, any given neuron will be able to participate in many different assemblies. It is often thought that these assemblies constitute the substrate of engrams, the physiological correlates of memory, at the level of the neuronal network. Hence, the concept of cell assemblies is prominent in our thinking about groups of synaptically connected neurons.

Setting aside the questions of the definition of the engram, recent efforts have claimed to identify engram cells under this or that experimental paradigm, although the connectivity of those cells has not been established. While we know that functionally similar neurons display a higher degree of connectivity and that groups of cells with correlated activity patterns can be formed by artifically-induced synchrony, the direct link between increased connectivity and behaviourally relevance has not been established.

On the other hand, only a handful of studies have directly measured the connectivity between more than two neurons (Song et al., Perin et al.), revealing the intricate properties of neuronal network structure. Depending on the level of description they may refer to distributed cell assemblies, synaptic clusters of neurons, $n$-vertex cliques, and motifs.

All this is to say that as soon as you start considering more than a pair of neurons, it makes little sense to talk about neurons that are exclusively connected with each other. As every neuron is connected to almost all other neurons in its immediate neighbourhood we often focus on the functional implications of their connectivity. Under this light, the concept of cell assemblies is central in modern neuroscience.



I think the only way to reasonably answer this question is to say: "read some papers and follow their conventions" - it's much too hard to develop an exhaustive description of how all of those terms are used without creating an answer that's much too broad. I'll give some comments, though:

Presynaptic/postsynaptic is most often used in the context of synaptic transmission/plasticity/etc where the subject is a synapse. You would not typically say presynaptic neurons to N when you are really just talking about a network from A to N.

Afferent/efferent 'neighbors' I have never heard used, won't claim it never is but it seems completely wrong to me.

As for afferents/efferents these refer to the fibers primarily. Those terms are most appropriate when you are talking about something like the spinal cord, where afferent projections head toward the CNS/brain and efferent projections head toward the periphery; in the brain I find them often confusing or incorrectly used. This is because the terms afferent and efferent don't actually refer to inputs and outputs, but rather to inward versus outward direction. Defining "direction" in the brain gets pretty ambiguous if you aren't talking specifically about primary sensory or motor areas.

Incoming/outgoing I rarely if ever hear.

In the context of "projecting" usually the noun form is used: projections, as in "thalamocortical projections" or "projections from V1 to V2." I'd say this is the least ambiguous/best terminology if you are talking about connections between different brain areas. Within a local brain area (i.e., a given nucleus or a region of neocortex) you would be less likely to use the term "projection" though it wouldn't necessarily be wrong.


Scientists Say: Neuron

The cells here in green are individual neurons in the spinal cord. Each neuron has a long axon reaching out to pass on messages.

Lawrence Marnett and colleagues, Vanderbilt University Nature Chemical Biology

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Neuron (noun, “NUR-on”)

This is one of the main cell types of the nervous system — your brain, spinal cord and nerves. It’s also frequently called a nerve cell. Neurons help the body detect and respond to information. They do this by transmitting signals from one place in the body to another.

Every time you touch something, that touch starts an electrical signal in the very tip of a neuron near your skin. This neuron then carries the information to other neurons in the brain for processing. When you want to move, for instance, the brain sends electrical signals down neurons to contract the muscles in your arm or leg. There are around 86 billion neurons in the brain and another billion in the spinal cord.

The parts of a neuron are specialized to produce, receive and move electrical signals. Usually, a neuron receives signals on small branches called dendrites. These dendrites stick out from the main body of the cell. Electrical signals go down a long tail called an axon. At the end of the axon is another set of small branches, called the axon terminal.

Electrical signals move along the axon as waves of positively and negatively charged ions. These weave in and out of the cell’s axon, rippling to the terminal. There, the neuron passes the message to another cell using chemical signals.

Because neurons transmit signals from one body part to another, they can get very long. In fact, a single neuron from the base of the spinal cord to the big toe can be more than one meter (three feet) long.

In a sentence

In a count of neurons in the outer layers of the brain, dogs beat cats — and bears, too.

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Power Words

action potential: A brief change in the electrical potential on the surface of a cell, especially of a nerve or muscle cell. It happens when the cell is stimulated. This triggers the release of an electrical impulse.

axon: The long, tail-like extension of a neuron that conducts electrical signals away from the cell.

cell: The smallest structural and functional unit of an organism. Typically too small to see with the unaided eye, it consists of a watery fluid surrounded by a membrane or wall. Depending on their size, animals are made of anywhere from thousands to trillions of cells. Most organisms, such as yeasts, molds, bacteria and some algae, are composed of only one cell. (in telecommunications) A technology that relies on a large number of base stations to relay signals. Each base station covers only a small area, which is known as a cell. Phones that rely on this system are typically referred to as cell phones.

cell body: The compact section of a neuron where its nucleus is located.

chemical: A substance formed from two or more atoms that unite (bond) in a fixed proportion and structure. For example, water is a chemical made when two hydrogen atoms bond to one oxygen atom. Its chemical formula is H2O. Chemical also can be an adjective to describe properties of materials that are the result of various reactions between different compounds.

chemical messenger: (in physiology) Molecules that send signals from one place to another within the body. Messages pass from one neuron to another via chemical messengers. Hormones are also chemical messengers from one body part to another

chemical signal: A message made up of molecules that get sent from one place to another. Bacteria and some animals use these signals to communicate.

contract: To activate muscle by allowing filaments in the muscle cells to connect. The muscle becomes more rigid as a result. (in commerce) An agreement between two parties, such as to make a purchase or provide some service.

dendrites: Hair-like projections from the head (cell body) of a neuron. They sit ready to catch a neurotransmitter, a chemical signal, that has been released by a neighboring neuron.

information: (as opposed to data) Facts provided or trends learned about something or someone, often as a result of studying data.

ion: An atom or molecule with an electric charge due to the loss or gain of one or more electrons.

membrane: A barrier which blocks the passage (or flow through) of some materials depending on their size or other features. Membranes are an integral part of filtration systems. Many serve that same function as the outer covering of cells or organs of a body.

muscle: A type of tissue that produces movement by contracting its cells, known as muscle fibers. Muscle is rich in protein, which is why predatory species seek prey containing lots of this tissue.

nerve: A long, delicate fiber that transmits signals across the body of an animal. An animal’s backbone contains many nerves, some of which control the movement of its legs or fins, and some of which convey sensations such as hot, cold or pain.

nervous system: The network of nerve cells and fibers that transmits signals between parts of the body.

neuron: The main cell type of the nervous system—the brain, spinal column and nerves. These specialized cells transmit information by producing, receiving and conducting electrical. Neurons also can transmit signals to other cells with chemical messengers.

spinal cord: A cylindrical bundle of nerve fibers and associated tissue. It is enclosed in the spine and connects nearly all parts of the body to the brain, with which it forms the central nervous system.

synapse: The junction between neurons that transmits chemical and electrical signals.

terminal: The end point or last station in some system, network or process. The end of the line.

transmit: (n. transmission) To send or pass along.

wave: A disturbance or variation that travels through space and matter in a regular, oscillating fashion.

About Bethany Brookshire

Bethany Brookshire was a longtime staff writer at Science News for Students. She has a Ph.D. in physiology and pharmacology and likes to write about neuroscience, biology, climate and more. She thinks Porgs are an invasive species.

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What's in the Story?

You shouldn’t take the cells in your brain for granted. They exist in a swirl of electrical and chemical messages, with information racing this way and that. In all creatures with a brain, brian cells work together to control their bodies and to make them behave in unique ways. But not all animals are able to complete the same challenges. So what are the differences between the brains of, say, a mouse and a human, that make them better or worse at certain tasks?

Mice are especially good at finding food in mazes because they can use their sense of smell. Image by Rama.

Well, we know they are very different in size and each has special abilities. For example, mice are very good at moving through mazes to find food because of their great sense of smell. Humans can’t use their noses as well but are able to find their way around by using maps or electronics.

Many scientists wondered what was the cause of these differences. For a while, some thought brain size was responsible. But it turns out this may not be the only reason. In the PLOS Biology article, “High Bandwidth Synaptic Communication and Frequency Tracking in Human Neocortex,” biologists studied whether brain cells in humans communicate in a more efficient way than those in rodents.


Know Your Neurons: The Discovery and Naming of the Neuron

Over the years, I have taught my copy of Microsoft Word a lot of neuroscience terminology: amygdala, corpus callosum, dendritic spines, voxel. But it always knew what neuron meant. I thought I did too.

Neurons—the electrically excitable cells that make up the brain and nervous system—first fascinated me in high school. In college, like so many other students studying the brain, I dutifully memorized the structure of the archetypal neuron. I also remember learning about a few different types of neurons with different shapes and functions: motor neurons that make muscles twitch, for example, and unique sensory neurons in the eyes and nose.

Only recently, however, have I begun to recognize and appreciate the extraordinary diversity of cells in the nervous system—cells that differ from one another more than the cells of any other organ. Some neurons send electrical signals along fibers that stretch several feet other neurons' branches extend only a few millimeters away from the cell body. Some neurons possess a fractal beauty similar to that of ferns and corals: Purkinje cells, for example, often sport finely branched nets, like a sea fan. But some of their neighbors look more like tangled tumbleweeds. One neuron might appear more or less round under the microscope—like a firework frozen in climax—whereas another might spider through the brain like a daddy longlegs. Neurons not only differ in shape—different types of neurons turn on different sets of genes and not all neurons use the same chemicals to communicate. Excitatory neurons mostly stimulate other cells inhibitory neurons prefer to stifle. Most neurons fire in patterns, but their tempos vary: some keep a steady beat, others remain largely silent except for the occasional burst of activity and still other cells continually fire like a trigger-happy toddler playing laser tag. To summarize: not all neurons are exactly alike. The brain contains multitudes.

The Know Your Neurons series will celebrate and explore the cellular diversity of the nervous system, which is a subject of active research today. In the last decade, for example, intriguing and ostensibly unique types of neurons—such as spindle neurons and mirror neurons—have soaked up the spotlight because these cells might be crucial for some of the brain's most sophisticated forms of intelligence. However, scientists have not yet reached consensus about just how special these cells really are. In decades to come, increasingly powerful imaging technologies will allow researchers to see neurons in greater detail than ever before, likely revealing previously hidden differences between brain regions and cell types. Close inspection is how the neuron was discovered in the first place. It took years of careful observations to convince the scientific community that neurons were true cells.

The Discovery and Naming of the Neuron

The word neuron, as we understand it today, did not exist before 1891.

By the middle of the 19th century, scientists had discovered that the tissues of plants, animals and all living things were made of discrete units called "cells," the same "small rooms" that 17th century English physicist Robert Hooke observed in a slice of cork under his microscope. However, one kind of living tissue appeared to be an exception to "cell theory"—the nervous system.

When the leading anatomists of the 19th century examined fragile nervous tissue with the best microscopes available to them, they identified cell bodies that sprouted many tangled projections. German histologist Joseph Gerlach's observations convinced him that the fibers emerging from different cell bodies fused to form a continuous network, a seamless web known as the "reticulum." His ideas were popular. Many researchers accepted that, unlike the heart or liver, the brain and nervous system could not be split up into distinct structural units.

In 1873, Italian physician Camillo Golgi discovered a chemical reaction that allowed him to examine nervous tissue in much greater detail than ever before. For some reason, hardening a piece of brain in potassium dichromate, and subsequently dousing it with silver nitrate, dyed only a few cell bodies and their respective projections in the tissue sample, revealing their complete structures and exact arrangement within the unstained tissue. If the reaction had stained all the neurons in a sample, Golgi would have been left with an unfathomable black blotch, as though someone had spilled a bottle of ink. Instead, his technique yielded neat black silhouettes against a translucent yellow background.

Golgi's "black reaction," combined with the painstaking work of Karl Deiters and others, clearly distinguished two kinds of projections from cell bodies in nervous tissue: a long slender cable that did not seem to branch much and a cluster of shorter branching fibers. Even though Golgi saw that one cell body's branching fibers did not fuse with another's, he did not reject Gerlach's idea of the reticulum—instead, he decided that the long slender cables probably connected to form one continuous network.

Fourteen years later, in 1887, Spanish neuroanatomist Santiago Ramón y Cajal learned about Golgi's black reaction from psychiatrist Luis Simarro Lacabra, who had managed to improve Golgi's original technique. Cajal, who was already obsessed with studying the structures of living tissues in minute detail, immediately recognized the black reaction as the most sophisticated way to investigate the nervous system and puzzled at why so few scientists apart from Golgi himself had tried out the staining procedure. Cajal further improved the black reaction and applied the technique to all kinds of nervous tissue from different animals and from people, producing beautiful and detailed sketches of what he saw under the microscope—drawings that scientists and educators still rely on today.

Cajal's studies showed that, contrary to Golgi's suspicion, the long slender cables emerging from cell bodies did not fuse into one mesh. Although the many fibers in a tissue sample overlapped, they remained distinct physical structures, like interweaving branches of different trees in a crowded forest. There was no reticulum. The nervous system, like all other living tissue, was made up of discrete building blocks, or what Cajal called "absolutely autonomous unit[s]."

In October 1889, Cajal visited the Congress of the German Anatomical Society in Berlin to present his findings to the world's leading neuroanatomists. Although many scientists had mocked Cajal and his sketches, his presentation in Germany convinced the extremely influential Swiss histologist Rudolf Albert von Kölliker to abandon any notion of the reticulum. In 1891 German anatomist Wilhelm Waldeyer synthesized Cajal's groundbreaking research with the cell theory of the 1830s—adding ideas introduced by Swiss embryologist Wilhelm His and Swiss psychiatrist August Forel—to form the "neuron doctrine": the nervous system is made up of discrete cells, which Waldeyer dubbed neurons. In 1896, Rudolph Albert von Kolliker coined the term axon to describe the long slender cables that transmit signals away from cell bodies. In 1889, William His named the thin branching fibers that ferry signals toward the cell body dendrites. Based on his drawings of cellular circuits, Cajal had already inferred the direction in which signals moved through neurons.

Cajal's sketches remain of one the most detailed accounts of the structural diversity of the brain and nervous system. Today, we know that although brain cells are built from a common blueprint, they differ from one another structurally, functionally and genetically. One could even argue that, because each neuron links up with neighbors in its own way, every single cell in the brain is unique. Next on Know Your Neurons, we explore the different ways to classify brain cells and try to get a sense of just how many different types exist.

Bentivoglio, M. Life and Discoveries of Santiago Ramon y Cajal. Nobelprize.org. 1998. http://www.nobelprize.org/nobel_prizes/medicine/laureates/1906/cajal-article.html

Costandi, M. The discovery of the neuron. Neurophilosopy. 2006. http://neurophilosophy.wordpress.com/2006/08/29/the-discovery-of-the-neuron/

Kandel ER, Schwartz JH, Jessell TM 2000. Principles of Neural Science, 4th ed. McGraw-Hill, New York

Mazzarello, P. A unifying concept: the history of cell theory. Nature Cell Biology 1, E13 - E15 (1999) doi :10.1038/8964


Content: Alcohol Disrupts the Communication Between Neurons

Alcohol acts as a general depressant of the central nervous system. It “depresses” or inhibits the function of neurons by reducing their ability to transmit or “fire” electrical impulses. These electrical impulses carry information that is essential for normal brain function. The overall “inhibitory” effect of alcohol on the brain is very similar to that produced by other drugs that cause sedation and anesthesia.

Neurons communicate with each other through the transmission of electrical and chemical signals. Because electrical impulses can’t jump from one neuron to the next, electrical impulses are converted to chemical signals. The conversion of electrical to chemical signals occurs at the synapse, the connection between two neurons. The chemicals are called neurotransmitters —they diffuse across the synaptic space between neurons, and bind to specific proteins or receptors on the receiving neuron. This binding reaction triggers a new electrical impulse in the receiving cell, and the communication proceeds so that the brain can function normally. Alcohol inhibits the generation of the new electrical impulse.

Figure 2.4 A synapse is the connection between two neurons it is where all the communication takes place. Neurotransmitters released from neurons bind to receptors on the receiving neuron. This changes the electrical activity of the receiving cell. Alcohol and other drugs that affect the brain act at specific synapses.

Learn more about basic neuron structure and function and view a 3D animation


Taking control and leveraging our knowledge

So, what does this mean in an everyday setting?

When we are not consciously aware of how things are impacting us it can have all sorts of negative impacts. For example, unconscious imitation within the context of other complex social influences can encourage young people to join gangs, be violent, to acquire eating disorders or even to commit suicide.

At a more everyday, yet still significant, level we are hugely influenced by those closest to us in ways that we’re often unaware. For example, in 2007 a study published in the New England Journal of Medicine observing more than 12,000 participants for 30 years found that people are more likely to gain weight if those they interact with gain weight. The chances of gaining that weight increased by an astounding 171 per cent if a close friend had done so.

We almost absorb the behaviour of those around us, and we point our attention – particularly those we’re close to – by osmosis. And this isn’t just true of gaining weight it covers almost everything we do.

Read more about social influence:

If we can be more conscious of this, then we’re able to judge it rationally and make a decision on whether we take on behaviours and attitudes or not. Even better, if we can understand how this brain functionality impacts us, if we are more aware of how our daily interactions influence and affect the workings of our brain, then we can deepen our understanding of what we can take from it and how we can use it. And if we make the effort to direct our own and our children’s attention toward positive role-models it can make a massive difference to who we are and how we live our life.

If leveraged effectively this knowledge could also be used for hugely positive social change. There is evidence that role-modelling can have a positive impact across all sorts of societal issues: improving diversity and inclusion, campaigning to protect the climate, LGBTQ+ rights, Black Lives Matter and mental health. It can result in increasing involvement in sport, improving physical health, life outcomes, educational attainment and even employment opportunities.

It seems likely that the mirror neuron, one of the smallest units of the brain, plays a critical and perhaps dominant role in our individual lives, in our ability to model others both internally and externally and, therefore, in what it means to be human.


Principle (agonist)

Normally neurotransmitters cross the synaptic gap and bind with postsynaptic receptors, which are activated to open ion channels with the result that the post-synaptic neuron is either fired or inhibited. Drugs can act to replace neurotransmitters, binding with the postsynaptic receptors to complete the neuron-to-neuron communication.

Normal neurotransmitter activity still happens, resulting in a significantly increased chance of stimulating the post-sypnaptic neuron.

Nicotine works this way, docking with acetylcholine receptors to increase stimulation.


Artificial Neurons

Artificial Neurons – those used in artificial neural networks – are a beautiful reduction of biology. They are an abstraction of neural behavior which reduces the behavior into a few key features: (a) they integrate (sum together) signals over all incoming synapses, (b) they transform the integral signal according to a non-linear function:

We can write (a) formally as:

Where neuron $j$ is the pre-synaptic neuron, $i$ is the post-synaptic neuron, $v_j$ is the signal from $j$ to $i$ and $w_$ is the strength of the synapse.

where $f(I_i)$ is typically a sigmoid or linear rectifier function.

Pretty simple, but a little hokey. These are better called units rather than neurons, since they don’t spike. Nonetheless, the output from these units can be interpreted as the a firing rate. Despite their simplicity, these units are useful for exploring aspects of neural computation. Not only that but they are used in state-of-the-art AI algorithms. It has been shown that artificial neural networks with just one hidden layer are capable of approximating any function.

The strength of these kind of neurons stem from being differentiable — even when stacked in networks. This property minimize or maximize some goal to learn an arbitrary function. However, due to their simplicity, e.g. the lack of a spiking mechanism and persistent state and their simplicity, they aren’t realistic models of the biology.


Part 2: Cracking the Circuits for Olfaction: Odors, Neurons, Genes and Behavior

00:00:00.00 Hi, I'm Cori Bargmann,
00:00:03.25 from the Rockefeller University in New York,
00:00:05.29 and the Howard Hughes Medical Institute.
00:00:08.02 And I'm going to talk today about work that we've been doing to try to crack circuits for olfaction,
00:00:13.27 to understand how you go from odors to neurons to genes to behavior.
00:00:19.24 Now, I'm going to talk about this in the context not of the noble human brain,
00:00:24.20 but of the noble brain of the nematode worm, Caenorhabditis elegans.
00:00:28.20 Why would we study a simple animal instead of studying humans?
00:00:31.29 The reason is that the human brain is almost unimaginably complex:
00:00:36.14 it has billions of neurons that are connected to each other by trillions of synapses.
00:00:42.03 By contrast, the nervous system of the nematode worm C. elegans has only 302 neurons
00:00:47.27 that are connected by 7000 synapses, and another 600 or so gap junctions.
00:00:54.15 Now, this much simpler nervous system nonetheless shares many components with the nervous system of a human.
00:01:01.19 So whereas humans have about 25,000 genes,
00:01:04.09 worms have about 20,000 genes,
00:01:06.09 many or which are shared between the species.
00:01:08.20 And when we look at the properties of the nervous system,
00:01:11.01 we find that many features of the nervous system are similar,
00:01:14.22 that worms use similar neurotransmitters, channels, and developmental genes, as humans.
00:01:20.07 Therefore, we think that some of the principles that underlie the function of the brain
00:01:24.08 and the function of brain circuits in behavior will also be similar between simpler animals like the worm
00:01:30.06 and complex animals like ourselves.
00:01:34.17 Now, with C. elegans, we also have, from the work of John White and his colleagues,
00:01:39.06 knowledge of how those 302 neurons communicate with each other, through a wiring diagram.
00:01:45.05 This wiring diagram contains only 6000 or 7000 connections,
00:01:48.26 but that's still too many, as you can see in this illustration,
00:01:52.24 to really understand the flow of information.
00:01:55.05 We need to directly test what the connections do,
00:01:57.28 we need to test what the neurons do, in order to understand behavior.
00:02:03.28 And the way that we try to understand behavior is using the behavior of the entire animal,
00:02:10.29 the functions of individual genes, and the functions of neurons,
00:02:14.18 and relate those to each other vertically, from the level of molecules
00:02:18.23 to the level of the entire organism.
00:02:21.07 Now, the starting point for this set of studies will be the fact that worms respond to odors
00:02:27.15 with robust behavioral responses,
00:02:29.24 that pose a set of questions we can ask about how behavior is generated.
00:02:33.24 So, if you put a lot of worms down in an environment where there's no odor,
00:02:36.27 they'll scatter around.
00:02:38.24 But if you them in an environment where there's a good odor on one side,
00:02:41.25 they'll quickly move to the source of that good odor and accumulate there.
00:02:46.29 Conversely, if you put them in an environment with a bad odor,
00:02:49.06 they'll go as far from it as they possibly can.
00:02:51.25 So we can see attraction, repulsion, or neutral responses in the behavior of the animal.
00:02:57.21 We can then ask: What parts of the worm brain are required for these different kinds of behaviors?
00:03:04.15 And we can ask this question through different kinds of approaches,
00:03:08.22 either loss-of-function approaches or gain-of-function approaches,
00:03:12.01 and both of those converge on the same answer,
00:03:15.03 which is that specific neurons detect odors and initiate behaviors in the animal,
00:03:20.20 and that the neurons that do this are reliably similar from worm to worm.
00:03:25.23 So, one way to determine that is to eliminate the functions of single neurons,
00:03:29.27 which we can do by killing them with a laser microbeam,
00:03:32.13 and when we do that, for example, for this neuron shown here in blue, the AWC neuron,
00:03:37.13 we find that the animals become defective in their ability to chemotax
00:03:40.26 to certain attractive odors and to search for food.
00:03:44.15 Now, if we kill the neuron right next to AWC, this red neuron, ASH,
00:03:48.20 there's no defect in odor chemotaxis and food search.
00:03:51.14 But now instead, there's a defect in nociception
00:03:55.16 and escape behavior that is triggered by noxious compounds that the worm hates.
00:04:00.20 So this tells us these neurons are required for different behaviors.
00:04:04.08 We can complement this loss-of-function analysis by gain-of-function analysis,
00:04:08.19 where we activate these neurons artificially and ask what behaviors the animal generates.
00:04:14.15 And the method that's used to do that currently in neuroscience
00:04:18.07 is to use a molecule called channelrhodopsin.
00:04:21.03 It's a light-activated ion channel from a unicellular organism.
00:04:25.23 The gene for channelrhodopsin can be introduced into different neurons in different animals,
00:04:30.25 and it will then make those neurons responsive to light,
00:04:33.15 so that when you shine light on them, the neurons become active.
00:04:36.15 You can then ask, in this gain-of-function configuration,
00:04:39.20 what happens when you activate one of these neurons?
00:04:42.26 And so for, example, as is shown in this movie here, when you activate the ASH
00:04:47.24 nociceptive neuron that mediates escape behaviors simply by turning a light on
00:04:52.26 and activating channelrhodopsin, the worm generates a reversal.
00:04:57.01 This is an escape behavior associated with a change of direction
00:05:00.16 that's exactly like what would happen if ASH detected one of its normal,
00:05:05.04 noxious stimuli that would also direct an escape behavior.
00:05:09.12 And so we can say here that ASH is both necessary and sufficient for generating escape behaviors.
00:05:18.13 Now, explaining escape behavior is pretty straightforward.
00:05:22.17 Escape behavior is deterministic
00:05:24.26 that means that, when a worm encounters a noxious substance,
00:05:28.09 as illustrated by this series of panels, every worm generates a reliable response
00:05:33.09 to that noxious substance, in a way that's quite predictable,
00:05:37.08 where it will back up, turn away, and move in a new direction.
00:05:41.04 But when we try to understand chemotaxis behavior, we see that it has different properties.
00:05:46.01 It's a probabilistic behavior,
00:05:48.08 and what I mean by that is that,
00:05:49.29 while all of the worms will eventually reach the odor,
00:05:53.12 they get to the odor by what seems to be an unpredictable path.
00:05:57.00 Every worm seems to follow a different path to reach the odor source.
00:06:01.09 How can we explain this more complex trajectory,
00:06:04.21 which doesn't look like the reflex or deterministic action?
00:06:07.26 What we need is some kind of a model that would explain
00:06:11.00 how animals can approach an odor.
00:06:13.29 And in fact, exactly such a model was developed by Shawn Lockery and colleagues,
00:06:19.05 and what they showed was that worms approach the odor using a strategy
00:06:24.01 called a "biased random walk," which is the same strategy that bacteria use
00:06:29.06 to detect attractive chemicals in their environment.
00:06:32.12 A biased random walk occurs through a fascinating strategy where
00:06:37.19 animals don't point their nose straight up toward the odor like a weather vane
00:06:42.11 instead, they simply move through their environment,
00:06:45.17 waiting to see whether conditions are changing, and if so,
00:06:51.05 whether they're getting better or worse.
00:06:53.18 And what the animals do is that they turn, changing directions,
00:06:56.29 at some constant rate in constant conditions.
00:06:59.25 But if conditions get better, if the odor increases,
00:07:05.23 then they make fewer turns.
00:07:08.04 If the conditions get worse, if the odor decreases,
00:07:11.12 they make more turns.
00:07:12.27 And the effect of this, is that animals will move in a good direction
00:07:17.00 where odors are increasing for a longer period of time,
00:07:21.04 and they'll move in a bad direction where odors are decreasing
00:07:24.00 for shorter periods of time.
00:07:25.20 And eventually, just changing direction at random,
00:07:28.15 this will lead them to accumulate at the odor through what appears to be a
00:07:32.13 more-or-less random path.
00:07:34.14 So the key feature of this strategy is that the animals aren't detecting the absolute levels of odors,
00:07:40.01 they're detecting the change in an odor level.
00:07:42.27 are things getting better or are things getting worse?
00:07:46.03 They're looking at the change in concentration over time.
00:07:51.05 So, we would like to test this model.
00:07:53.14 How do you go about testing a model like this, about odor concentrations over time?
00:07:58.20 The way you have to test this model is to generate a temporal gradient,
00:08:03.12 an odor environment that changes only over time and not over space,
00:08:08.12 to test the predictions of this particular quantitative model.
00:08:12.13 And the way that this can be done is by generating small chambers
00:08:16.11 in which animals can be exposed to odors flowing past them rapidly,
00:08:20.13 and then examine for their different kinds of behavioral responses.
00:08:24.07 And a chamber to carry out this task was designed by Dirk Albrecht.
00:08:30.05 So, what Dirk did was to find a small environment in which he could provide pulses of odors
00:08:35.20 at a known concentration at a known schedule,
00:08:38.10 and examine the responses of the worms in these environments.
00:08:41.23 And as is seen in the movie here, when you watch worms moving through this chamber,
00:08:46.02 sometimes they move in straight lines, and sometimes they change directions,
00:08:49.11 generating different kinds of turns.
00:08:51.28 Now, this light color here are worms in the absence of an odor.
00:08:55.12 Some of them are turning, some of them are moving in straight lines.
00:08:58.07 When the dark color appears, that will signal the appearance of an attractive odor.
00:09:02.25 When the light colors appears, the odor will disappear.
00:09:05.25 And what you should be able to see is that,
00:09:07.17 when the odor appears, the worms move in long, straight lines,
00:09:11.08 and when the odor disappears, they turn, they change direction.
00:09:15.02 Again, attractive odor. long, straight lines.
00:09:18.28 Disappearance. turning.
00:09:21.13 This is exactly the behavior that is predicted in the biased random walk model:
00:09:26.22 An increase of turning when conditions are getting worse.
00:09:30.14 So here we can see that at a visual level.
00:09:33.05 But in order to understand behaviors, we need to quantify those behaviors,
00:09:37.08 not just look at them qualitatively.
00:09:40.16 And to do that, we can use methods to automatically analyze the turning behaviors
00:09:45.08 using computers to monitor the position of worms over time.
00:09:49.04 We can then assign to each of the worms a description of what it's doing at any particular time:
00:09:54.23 Is it moving forward, here in gray?
00:09:57.02 Is it pausing or reversing, here in black?
00:09:59.24 Or is it generating different kinds of turns, called pirouettes, here in red?
00:10:04.17 This analysis can be done for many hundreds of animals over different kinds of stimulus protocols,
00:10:10.20 leading to the kinds of data shown here, where animals are exposed to pulses of odors in blue,
00:10:17.25 and odor being removed (replaced by buffer) in white.
00:10:21.25 And then here, hundreds of animals are monitored for their behavior in response
00:10:26.04 to that sequence of odor and buffer pulses.
00:10:29.04 Now what you should be able to see is that there's a lot of red and black material in the presence of buffer,
00:10:34.26 but much less when odor is present.
00:10:38.04 These hundreds of traces can then be quantified to generate the one trace underneath,
00:10:42.29 which shows the probability of turning under different conditions.
00:10:47.12 And what you can see is that, when odor is present, as it is here,
00:10:51.08 the probability of turning is quite low, but it's not zero.
00:10:55.04 And when odor is removed, as is shown here,
00:10:57.17 the probability of turning shoots up, but it doesn't go up to 100%.
00:11:02.03 it eventually returns again to the basal probability of turning.
00:11:06.11 So from this we can say a couple of different things:
00:11:08.29 We can confirm the biased random walk model, we can say that, yes,
00:11:12.14 turning rates do change based on odor history,
00:11:16.01 whether odor has been added or removed.
00:11:19.02 And we can also notice that this is indeed a probabilistic behavior,
00:11:23.29 that the probability of turning changes, but it's never 0%, and it's never 100%.
00:11:29.19 To understand behavior, we have to think quantitatively and statistically
00:11:33.28 about what animals are doing at any given time.
00:11:39.17 So, using these kinds of assays and simpler assay that resemble these,
00:11:44.09 it's been possible to map out neurons that are required for odor chemotaxis and food search.
00:11:50.18 I told you that the AWC neuron, an olfactory neuron, is required for odor detection.
00:11:55.23 AWC forms synapses onto three different classes of interneurons,
00:12:00.16 neurons that collect information from a variety of sensory neurons,
00:12:04.23 and these neurons are connected to each other and with a fourth neuron.
00:12:09.04 All four of these neurons, that are one synapse away from the AWC neuron,
00:12:13.26 regulate turning probabilities.
00:12:16.15 Two of them, shown in blue,
00:12:18.15 act to increase the rate of turning when odor is removed, and two of them, show in red,
00:12:24.09 act to decrease the the rate of turning.
00:12:26.14 So they're both positive and negative signals in this circuit that are mediating odor information.
00:12:32.27 Now, once a turn is being generated,
00:12:36.08 the worm has to decide what kind of turn it's going to be.
00:12:39.00 The neurons shown here in gray at the bottom of the slide
00:12:42.02 are neurons that help interpret this turning frequency information and
00:12:45.19 turn it into different kinds of output motor behaviors.
00:12:48.22 I won't talk about those further in this talk.
00:12:51.02 I'll just concentrate on the first step:
00:12:53.08 How is the problem of detecting odor transformed through the neurons
00:12:57.11 that collect this information from the sensory neuron, to regulate turning rates?
00:13:04.28 So, one way to answer that question is to start to get a dynamic picture
00:13:09.18 of what the neurons are doing in response to odors.
00:13:13.10 We want to visualize what's happening in these neurons.
00:13:16.21 So what are the tools we can use to understand when neurons are active?
00:13:20.25 In C. elegans, one of the tools we like to use are genetically encoded calcium indicators.
00:13:27.23 These are fluorescent proteins based on the "green fluorescent protein"
00:13:32.05 that include within them a calcium-binding protein "calmodulin,"
00:13:35.29 as well as a peptide that will bind to calmodulin when calcium is present.
00:13:40.25 Through genetic engineering and biochemical studies,
00:13:43.13 Junichi Nakai and others have generated versions of these proteins that increase fluorescence
00:13:49.06 when they are bound to calcium, and are less fluorescent when they are not bound to calcium.
00:13:53.28 This is useful to us because calcium is a good reporter of when a neuron is active.
00:13:59.20 When neurons are depolarized, they open voltage-gated calcium channels,
00:14:04.07 leading to an increase of calcium within the cell.
00:14:07.03 And therefore, an increase in fluorescence of a protein associated with
00:14:11.07 an increase of calcium will tell you when a neuron is depolarized.
00:14:16.04 To monitor a specific neuron,
00:14:17.27 we then take advantage of the powerful transgenic tools in C. elegans
00:14:22.04 to express this genetically encoded fluorescent protein
00:14:25.02 only in a single kind of neuron of interest,
00:14:27.23 in this case, in the AWC neuron, to ask when that neuron is active.
00:14:35.20 Now there's a third component required to monitor the activity of these neurons,
00:14:39.22 and that is that we need to be able to hold the worm still and
00:14:42.29 deliver odors in precise patterns while monitoring the fluorescence intensity of the AWC neuron.
00:14:50.05 We do that by borrowing a technology back from the engineering,
00:14:54.05 from the silicon chip, industry, into biology, called microfabrication.
00:14:58.23 And we build special worm traps that are worm dimension,
00:15:02.21 that enable us to hold a worm in an optically transparent environment,
00:15:07.23 while restraining it in three dimensions, and then flowing different kinds of fluids
00:15:11.20 past the nose of the worm while monitoring fluorescence intensity.
00:15:15.12 This microfluidic chamber then permits us to combine the genetic tools
00:15:20.00 with chemical tools to monitor neural activity.
00:15:25.13 And that's exactly what's happening in this image here.
00:15:28.17 So this is a single AWC neuron expressing a genetically encoded calcium indicator,
00:15:33.20 and you will see when the movie starts, the neuron starts with a yellow level of fluorescence
00:15:39.05 and a relatively low level of fluorescence in the process of the neuron.
00:15:42.26 Ten seconds into the movie, a switch in odor stimuli will occur, and the neuron will become brighter.
00:15:49.22 The brighter color, the more intense color, the larger white color in the cell body of the neuron over here,
00:15:54.21 all reflect the fact that calcium has gone up, and the neuron has become active.
00:15:59.17 So, indeed, we can see that the AWC neuron responds to odors by changing its activity.
00:16:06.23 But it responds in a way that we did not expect,
00:16:10.06 because the AWC neurons are not activated when odors are presented to the worm.
00:16:15.26 In fact, when we look at the fluorescence intensity and graph it in the presence of odor,
00:16:20.05 it is, if anything, a little less intense than it would have been in the absence of odor.
00:16:26.27 Instead, the AWC neurons become active when odor is removed.
00:16:31.21 This leads to a large increase in the fluorescence intensity,
00:16:34.21 indicating depolarization and the presence of calcium.
00:16:38.08 So these neurons seem to work in reverse.
00:16:41.13 They are inhibited by odors, their natural stimuli.
00:16:44.28 They are active when odors are removed.
00:16:47.24 And I just want to remind you that the worm has to generate a behavior when odor is removed.
00:16:53.02 When odor is removed, the worm is going to start turning.
00:16:56.00 So the activity of the neuron is correlated with the behavioral output, not with the input stimulus.
00:17:05.18 So we can now say something about this first neuron that interacts with odors.
00:17:10.25 How does it communicate with the target neurons that then convert this information into behavior?
00:17:17.10 The way that we study this is by studying the process of synaptic transmission.
00:17:21.15 Neurons connect to each other at specialized structures called synapses,
00:17:25.08 where a presynaptic neuron, the upstream neuron, in this case AWC,
00:17:29.28 will release vesicles filled with a neurotransmitter, and these neurotransmitters
00:17:33.24 will interact with receptors on the postsynaptic neuron, here shown in gray.
00:17:39.01 One kind of neurotransmitter that neurons release is glutamate, an amino acid,
00:17:45.25 and glutamate is packaged into special synaptic vesicles by a molecule called the
00:17:49.25 "vesicular glutamate transporter," or EAT-4 in C. elegans.
00:17:54.25 We can use this EAT-4 molecule to probe the action of synapses in the AWC neuron.
00:18:02.27 We can do that by using mutants in EAT-4 to inactivate the transporter
00:18:07.26 and therefore the ability of AWC to release glutamate.
00:18:11.19 And we can ask then,
00:18:13.09 what kinds of behavior can the animal generate in the absence of this glutamate transmitter?
00:18:18.15 And remember that turning is a reflection of the response to odor removal,
00:18:23.22 an important component of chemotaxis behavior, and that we can quantify this.
00:18:26.27 So a high level here of "1" is a high level of turning.
00:18:31.13 In red here is an eat-4 mutant.
00:18:33.15 The eat-4 mutant does not turn efficiently when odor is removed,
00:18:37.21 indicating to us that glutamate is required as a neurotransmitter for this turning behavior.
00:18:43.04 And when we restore EAT-4 just in the AWC neurons using a specific transgene,
00:18:48.22 we restore most of the turning behavior.
00:18:51.01 And so we can say that glutamate from AWC promotes turning.
00:18:57.25 So we now have insight into the first step of how AWC communicates with its target:
00:19:03.10 It uses EAT-4 to package glutamate into vesicles, it releases glutamate,
00:19:08.06 and this must then act on target neurons.
00:19:10.23 How does it communicate with the target neurons?
00:19:12.26 How does it communicate with these three different neurons with which it forms connections?
00:19:17.00 Well, it has to do that through glutamate receptors,
00:19:20.04 proteins that are expressed on the target neurons that enable them to detect the released glutamate.
00:19:25.11 And we found that there are two classes of glutamate receptors
00:19:28.22 that are important for this particular behavior.
00:19:31.29 There's a glutamate-gated cation channel it's an excitatory receptor called GLR-1.
00:19:38.01 And there's also a glutamate-gated chloride channel,
00:19:41.05 an anion channel that is an inhibitory receptor called GLC-3.
00:19:45.14 These two glutamate receptors,
00:19:47.11 which can generate two different kinds of responses in target neurons,
00:19:50.20 are important for AWC's communcation with its targets.
00:19:56.26 We can demonstrate that both through quantitative behavioral assays
00:20:02.18 and through direct observation of the activity of target neurons,
00:20:06.18 which we do using genetically encoded calcium indicators.
00:20:10.15 Now, instead of expressing them in AWC, we express them in downstream neurons,
00:20:15.24 such as AIB.
00:20:17.18 AIB is one of the neurons that receives synapses from AWC,
00:20:21.16 and we see that AIB, like AWC, responds to odor removal by an increase in calcium.
00:20:29.06 This response disappears if the AWC neuron is killed,
00:20:33.21 and it also disappears in an animal that lacks the glutamate receptor GLR-1.
00:20:38.17 GLR-1 is required in AIB for AIB to sense the glutamate signal from AWC.
00:20:46.09 This excitatory glutamate receptor transmits an excitatory signal from sensory neuron to interneuron.
00:20:56.03 Next, we looked at the AIA and AIY interneurons.
00:21:01.09 These neurons also respond to odors,
00:21:04.05 but these neurons respond oppositely to AWC.
00:21:08.12 AIA and AIY respond with an increase in calcium to odor addition,
00:21:13.22 there's been a change in the sign of the signal between the sensory neuron and the interneuron.
00:21:18.21 They don't respond to odor removal.
00:21:21.19 Now this response to odor addition still requires AWC,
00:21:25.19 and it requires a glutamate receptor.
00:21:28.04 It requires GLC-3, the glutamate-gated chloride channel.
00:21:32.21 This inhibitory receptor serves to transmit a signal from an excited AWC
00:21:38.18 into a signal that will inhibit the downstream neurons,
00:21:42.06 so the downstream neurons AIA and AIY respond oppositely
00:21:47.00 to odors than the upstream neuron AWC.
00:21:52.24 So putting this information together, here on the left,
00:21:56.09 we can assemble a C. elegans odor circuit.
00:21:59.26 We can say that attractive odors inhibit the AWC olfactory neurons,
00:22:04.16 that the AWC olfactory neurons now release glutamate
00:22:08.03 onto two classes of downstream neurons through two classes of receptors.
00:22:12.16 They excite one class of neurons, the AIB neurons,
00:22:15.25 through an excitatory glutamate receptor.
00:22:18.14 They inhibit other classes of neurons, AIA and AIY neurons,
00:22:22.17 through an inhibitory glutamate receptor.
00:22:25.15 By splitting the information in this way,
00:22:27.15 the AWC neurons have now transformed information into two streams:
00:22:32.00 One signals the appearance of odor, an "odor ON" response
00:22:35.17 the second stream signals the disappearance of odor, an "odor OFF" response.
00:22:40.26 Remarkably, when we examine this circuit,
00:22:43.12 it looks similar to another sensory circuit that's been well characterized,
00:22:47.14 and that is the circuit that is used to collect light in the vertebrate retina,
00:22:51.18 in your own eye.
00:22:53.14 So in your eye, light is collected by the rod and cone photoreceptors.
00:22:58.15 Rods and cones are active in the dark
00:23:00.29 they are inhibited by light, their natural stimulus,
00:23:04.03 just as AWC neurons are inhibited by odors.
00:23:08.12 Rods and cones release glutamate to communicate with their targets,
00:23:12.04 and they have two major classes of target neurons.
00:23:14.28 The target neurons are called bipolar cells.
00:23:17.24 One connection is through an excitatory glutamate receptor, and therefore,
00:23:22.24 these neurons have the same pattern of activity as the photoreceptors.
00:23:27.01 They're what are called "OFF" bipolar cells they signal when lights go off.
00:23:31.26 The other class of neurons are connected through inhibitory glutamate receptors.
00:23:36.01 Therefore, these neurons are called "ON" bipolar cells they signal when lights come on.
00:23:43.05 So comparing these different neural circuits,
00:23:45.19 we can say that in a worm olfactory system and in a vertebrate visual system,
00:23:50.23 some of the same principles are used to process sensory information.
00:23:55.02 Differential signaling of the appearance and the disappearance of a stimulus,
00:23:59.16 differential signaling through different classes of glutamate receptors,
00:24:03.01 to split information through different circuits.
00:24:05.22 This kind of insight helps convince us that there may be principles
00:24:09.10 for neural circuits that apply across different systems,
00:24:12.16 that will help us understand information processing.
00:24:16.01 What I've told you is that AWC communicates with three downstream neurons,
00:24:20.19 using glutamate to send complex information about the input stimulus
00:24:24.25 to different downstream sets.
00:24:28.23 In addition, AWC has another way of communicating with its targets,
00:24:33.04 because AWC doesn't just release glutamate,
00:24:35.23 it releases a second transmitter, a neuropeptide neurotransmitter called NLP-1.
00:24:41.15 NLP-1 is related to neuropeptides called buccalin in other animals,
00:24:46.02 and NLP-1 signals through a G protein-coupled receptor, called NPR-11.
00:24:52.04 NPR-11 is expressed on some of the downstream neurons from AWC,
00:24:57.18 but not all, including the AIA neurons.
00:25:01.09 So glutamate is released from AWC onto several neurons, and in addition,
00:25:05.29 a neuropeptide is released from AWC onto a subset of those neurons.
00:25:12.29 What is the function of NLP-1?
00:25:15.18 We can ask that by examining animals that are mutant for the NLP-1 neuropeptide
00:25:21.00 or mutant for its receptor,
00:25:22.27 and then comparing their behaviors to the behaviors of wild-type animals.
00:25:27.13 And what we find is that the function of NLP-1 is to antagonize
00:25:32.17 the glutamate signal from the same AWC neuron.
00:25:36.17 So, this is illustrated here in the quantitative turning behaviors that measure AWC output.
00:25:42.11 So a wild-type animal, shown here in white,
00:25:44.29 will turn about once a minute in response to odor removal.
00:25:48.22 These turns are absolutely dependent on the glutamate signal from AWC.
00:25:52.29 There are simply no turns when AWC glutamate is absent, as shown by this mutant.
00:26:00.06 But when we look at the nlp-1 mutant, we see that there are turns.
00:26:03.25 In fact, there are more turns than there would be in a wild-type animal.
00:26:07.21 So AWC is both sending a signal to stimulate turning (the glutamate signal),
00:26:12.20 and it's sending a second signal that inhibits turning (the NLP-1 signal).
00:26:17.23 It's limiting its own output by generating these two antagonistic signals.
00:26:24.08 We next asked how this signal interacts with the circuit
00:26:29.12 to affect the activity of different neurons.
00:26:32.20 And here there was a large surprise.
00:26:35.14 So we examined the nlp-1 mutant, and mutants in its receptor NPR-11,
00:26:40.17 to see where activity in the circuit was changed compared to the activity of wild-type animals.
00:26:45.25 We saw changes in the activity of the neurons not just in downstream target neurons
00:26:51.16 we saw changes in AWC itself.
00:26:54.23 The olfactory neuron responds differently to odors
00:26:58.01 depending on the activity of this peptide system.
00:27:01.21 So we can see this here in calcium imaging experiments showing
00:27:05.08 the response of AWC neurons to odor removal.
00:27:08.23 In wild-type, they show a sharp, short response.
00:27:12.06 In animals that lack the NLP-1 peptide or its receptors,
00:27:16.27 we instead see a longer-lasting response and repeated responses,
00:27:20.21 indicating that the AWC neuron is staying active for longer after odor has been removed.
00:27:28.17 Now, AWC is releasing this signal, the receptor for this signal in on a downstream neuron.
00:27:34.23 How does that information come back to AWC?
00:27:38.15 The answer is that the downstream neuron releases another signal, a feedback signal,
00:27:44.20 that is an insulin-like peptide, that returns to the AWC neuron to modify its activity.
00:27:50.26 So, a signal from AWC talks to a target neuron,
00:27:54.07 the target neuron then sends a signal back to AWC,
00:27:57.13 and again, the use of that signal limits the activity of the AWC neuron.
00:28:02.12 The feedback keeps AWC from generating these longer
00:28:06.03 or repetitive responses to odor removal.
00:28:11.29 So, it seems curious that a neuron would be generating
00:28:14.21 both positive and negative responses.
00:28:16.29 What could be the purpose of generating a negative feedback signal?
00:28:21.13 To understand this, you should understand that,
00:28:24.02 in animals, odor preference is modified by its experience with odor.
00:28:28.10 And this can be illustrated in a variety of ways,
00:28:31.15 but one simple way is that, when animals are exposed to odor in the absence of food,
00:28:35.25 they slowly adapt to the odor, so that they are no longer attracted to it.
00:28:40.15 This causes animals to prefer new odors,
00:28:43.18 or odors that have been paired with food,
00:28:45.14 to odors that have been seen in the absence of food,
00:28:49.02 and it represents an obvious good behavioral strategy for finding odors
00:28:53.11 that might be predictive of food in the future.
00:28:56.00 This can be quantified here, where the attraction to odor, shown here in black,
00:28:59.22 drops after 60 minutes of seeing an odor without food,
00:29:03.04 and drops even further after two hours of seeing the odor without food.
00:29:09.03 This change in the odor-dependent activity requires the neuropeptide feedback loop
00:29:16.10 that limits AWC activity.
00:29:19.06 If you remove either NLP-1 or its receptor NPR-11
00:29:24.13 or the feedback signal INS-1 that converts that information back to AWC,
00:29:29.19 then animals that have been exposed to odor, adapted animals, as shown here,
00:29:34.05 continue to respond to odor even after a long time of pairing of odor at the absence of food,
00:29:40.07 where wild-type animals would lose their response.
00:29:44.09 Adaptation requires the function of NLP-1 in the AWC neurons
00:29:49.22 and the function of NPR-11 and of INS-1 (the feedback signal) in the AIA neurons.
00:29:56.04 And so we can map this particular negative feedback signal to a particular
00:30:01.09 negative feedback that must occur to drive a useful olfactory behavior:
00:30:06.10 olfactory adaptation.
00:30:09.14 The activity of this feedback loop is observed not only at the behavioral level,
00:30:13.23 but also at the level of neuronal responses,
00:30:17.01 because when we examine the activity of AWC neurons after a long time of exposure to high odor,
00:30:23.06 as shown here in black, they simply stop responding to the odor
00:30:27.17 if the odor was present in the absence of food.
00:30:30.26 And this suppression of their response is defective in animals
00:30:36.05 that lack the neuropeptide feedback signal, as shown here in red,
00:30:39.29 which continue to respond to the odor even when it no longer predicts the presence of food.
00:30:50.07 So the conclusion of this part of the talk is that neuropeptide feedback,
00:30:55.04 superimposed on the basic function of the circuit, shapes sensory dynamics:
00:31:01.06 That sensory neurons like AWC respond to odors not in one way,
00:31:05.17 but in different ways depending on the activity of a feedback circuit
00:31:09.20 that if that feedback circuit is lost, the sensory neurons respond for longer and with multiple stimuli
00:31:16.14 that if the feedback circuit is present, they respond with a short stimulus
00:31:20.07 and that if the feedback circuit is strongly activated through olfactory adaptation,
00:31:24.25 the sensory neurons stop responding,
00:31:26.23 allowing the animals to suppress the response to that odor, and to respond to new odors.
00:31:34.08 And the conclusion of this talk is that circuits change over time, that circuits are not fixed,
00:31:41.09 that they actively shape and transform sensory information.
00:31:45.05 They don't just passively receive that information.
00:31:48.03 And furthermore, circuits change their own properties
00:31:51.05 based on sensory information in real time.
00:31:55.12 This process, this dynamic and active interpretation of information,
00:32:00.18 allows circuits to perform complex computations and calculations.
00:32:05.14 If you take just what I told you about this small circuit of just a few C. elegans neurons,
00:32:11.06 you can realize that, if you multiply that by the billions of neurons in a human brain,
00:32:15.21 it can start to explain why a human brain can generate an
00:32:19.17 infinite number of perceptions, memories, and behaviors.
00:32:23.18 Thank you.

  • Part 1: Genes, the Brain and Behavior

8 Ways to Express Your Gratitude

Given the benefits of gratitude for your brain and health, it’s well worth taking the time to focus on cultivating this emotion and trait in your life. Ready to amp up your daily gratitude? Try these practices:

  1. Keep a gratitude journal. Keep a small book on your bedside table and each evening write three things you were grateful for that day. You could also pop one in a desk drawer at work for a positive beginning to your day.
  2. Write a gratitude letter to a past mentor or teacher. It doesn’t matter if you are still in contact with the person you choose—they can be alive or no longer living. Write a letter, preferably by hand on nice paper, explaining what they did, how it affected you, how you felt, and why it is so important to you still. You can save it or send it.
  3. Count how many things you can find to be grateful for in each room of your home. See just how many things your kitchen has (like ice, running water, a beautiful view, sharp knives, etc.) that you can celebrate.
  4. Listen to a guided gratitude meditation, such as this one led by Deepak Chopra. You can also find guided meditations on apps such as Insight Timer, Calm, and Ten Percent Happier.
  5. Start business meetings with a “what went well” one-sentence reflection. When you prime your team by reviewing their recent accomplishments, it helps you to connect and keep going forward with enthusiasm.
  6. Savor receiving thanks. Notice if you are better at thanking than you are at being thanked (this applies to a lot of people). Work on receiving thanks with grace.
  7. Take a daily photo of something you are grateful for and post to Instagram or Facebook, tagging it with #365project.
  8. Try a gratitude jar or tree. Take a decorative mason jar or a small wooden tree and place it someplace you will see it every day, like the foyer or your kitchen counter. On a regular basis (daily or weekly) take a piece of paper and write: “I’m thankful for ______ today because ______.” Use recycled paint samples to add a splash of color. Then drop them in the jar or clip them to the tree. If you are feeling low, read your blessings to yourself.

When Deepak Chopra leads guided group meditations, he begins with a series of questions he calls the soul questions. One of these is “What am I grateful for?” Planting the seed of this question as you start your meditation is a way to start a dialogue with the universe about the daily gratitude you experience. By paying attention to the things you already have or are, you harness Hebb’s Law and strengthen your ability to see and experience more blessings in your life.

*Editor’s Note: The information in this article is intended for your educational use only and is not a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or other qualified health providers with any questions you may have regarding a medical condition and before undertaking any diet, supplement, fitness, or other health programs.


Watch the video: Μιά φορά κι έναν καιρό ήταν η ζωή - 10 Οι νευρώνες (September 2022).


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