Tracing Signals In The Brain In the traditional view of how signals travel in the brain, a portion of a neuron known as a dendrite acts as an input for stimuli, while the neuron's axon serves as a signal output. Nelson Spruston, author of an article in the journal Nature Neuroscience, says that the reality may be more complicated.
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Tracing Signals In The Brain

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Tracing Signals In The Brain

Tracing Signals In The Brain

Tracing Signals In The Brain

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In the traditional view of how signals travel in the brain, a portion of a neuron known as a dendrite acts as an input for stimuli, while the neuron's axon serves as a signal output. Nelson Spruston, author of an article in the journal Nature Neuroscience, says that the reality may be more complicated.


This is SCIENCE FRIDAY. I'm Ira Flatow.

Later in the hour: your brain on cell phones. But first, speaking about your brain, think back to biology class, when you learned about the brain and nerve cells. Remember those cells had long, wire-like tentacles carrying electric signals to and from the cell, the axon?

Yeah, the axon carried the impulses away. You remember the A for axon and away, and signals coming into the neurons from the dendrites. Dendrites evaluate, and they evaluate whether to respond with that information, and if they do, a split-second later, the signal fires and an impulse goes down, away on the axon.

It sounds pretty straightforward, right? Basic Biology 101. But a recent study in the journal Nature Neuroscience says it may not be so simple, that some neurons may respond not right after a stimulation, but significantly later.

And what's more, in some cases, a signal can go the other way, up the axon, and axons can talk to other axons in some mysterious way. Wow.

Joining me now to talk about it is Nelson Spruston. He's one of the authors of that paper and a professor of neurobiology and physiology at Northwestern University in Illinois.


Professor NELSON SPRUSTON (Neurobiology and Physiology, Weinberg College of Arts and Sciences, Northwestern University): Thanks for having me, Ira.

FLATOW: Wow, interesting new kinds of stuff about the way neurons work.

Prof. SPRUSTON: Yeah, well, this really fascinated us when we made this discovery. My graduate student, Mark Sheffield, came to me one day - I still remember the day - and said: Look what's happening here. I don't understand this.

And it took us a while to figure it out, but it definitely did change the way that we think about how these particular neurons are functioning.

FLATOW: So he really discovered - he looked at something people had seen over the years and thought it was something wrong with the experiments.

Prof. SPRUSTON: That's right. We think that probably one of the reasons it's been overlooked is people thought that something was wrong with either the cell being unhealthy or the quality of the recording conditions not being right.

But Mark had the insight to realize that everything looked OK, and that if you stimulated the neuron the same way, over and over again, he would get the same kind of unusual response. And you wouldn't expect that from a cell that was unhealthy. It would start to deteriorate and not produce a reliable response.

FLATOW: So let's talk about these things as individual events and separate them out. First, give us one and what was surprising about it.

Prof. SPRUSTON: Well, there's two aspects of the findings that were really surprising to us, and one has to do with time, and one has to do with space.

So as you nicely described, neurons respond rapidly to incoming signals, and if you think about it from an evolutionary perspective, it makes sense that it would happen that way. If you're an animal that wants to escape from a predator, you don't want to wait around for a long time.

So neurons were built, really, to perform very rapid signaling, and this is the way we usually think about how things work. But the thing that we noticed about these responses is they generated a form of response that seemed to be sensitive to what was happening with stimulation on the time scale of a minute or two minutes.

So if you stimulated the neuron repeatedly for a couple of minutes, it would go into another state of firing.

FLATOW: You mean, it would be - there would be a big delay between the stimulation and the response?

Prof. SPRUSTON: Yeah, that's right. So a minute or two of stimulation, and then suddenly, with a very rapid onset, the neuron would begin responding in this very long fashion. So again, the output would last for a long time, about a minute or so, and the firing - when we talk about firing, we're talking about action potentials in axons. And those action potentials had some very unusual characteristics, as well, that led us to believe the neuron was in a different state than the conventional state where it's responding rapidly to a stimulus that's arriving.

FLATOW: And what about the idea that the pulses in the axons can go in different directions?

Prof. SPRUSTON: Yeah, so that's where the idea of space comes in. As you nicely described at the top of the program, the way we usually think about it is the dendrites are the receiving structure of the neuron, and the axon is the output structure.

In this case, because of the unusual nature of the signals that we recorded during this persistent firing state, we thought that the action potentials were likely coming from the axon. And it looked to us like the axon was actually integrating the stimulus over this period of a minute or two that leads up to the persistent firing.

So it looks like the axon, in this particular behavioral state of the neuron, is functioning both as an input structure and an output structure.

FLATOW: Wow. No wonder you always thought there was something wrong with the measurement, because no one - this is so unusual.

Prof. SPRUSTON: Yeah. So it took quite a bit of work, actually, to really understand what we were seeing. So we had some insights both from experiments that we had done in the past, as well as computational modeling.

And, you know, we do - one of my colleagues, Bill Kath and I, work together a lot on making computer models of neurons. And computer modeling of neurons, I think, is a really powerful approach, and it allows us to see things and recognize things as unusual that we might not otherwise pick up when we're doing the experiments.

FLATOW: Now, let's talk about this third aspect, which is - it appears that axons are talking to one another.

Prof. SPRUSTON: Right. So this was perhaps the most surprising aspect of our work, was we did recordings from pairs of these particular types of neurons that exhibit persistent firing. And we found that in some cases, when we stimulated one neuron, one of the two that we were recording from, the other one, the unstimulated one, would go into this persistent firing state.

And there were no - none of the usual indications that the two neurons were connected in a conventional way by synaptic connections. And so we think we've identified an unusual form of communication that occurs between the axons of these neurons.

FLATOW: So how do they - spooky action at a distance, like the physicists would call it? I mean, how would they connect?

Prof. SPRUSTON: Well, there's got to be a physical substrate for the interaction, of course. One possibility is that the axons are connected by structures called gap junctions. That's an idea that has been proposed in the past.

Another possibility is that there are actually synaptic connections between the axons themselves, although we think that that's unlikely. We still need to do a lot more work to really understand this, but we think it's unlikely that there are synaptic connections.

The third possibility is that there's some sort of intermediate structure. So, for example, a non-neuronal cell, or a glial cell in the system is responding to stimulation of one cell and then generating a signal to which other cells in the immediate vicinity could respond.

FLATOW: So even after all these years of studying nerve cells and brain cells, there may still be structure there that we have not discovered?

Prof. SPRUSTON: Yeah, that's the - that's really the amazing thing. And I think it really highlights the need for this kind of research. I mean, we have to really understand how the component parts of the brain function if we want to understand how neural circuits function, how they control behavior and what goes wrong in various diseases. I think we really have to be doing basic research.

And sometimes findings like this really take you by surprise and make you realize: Wow, we really understand things a lot less than we may have thought.

FLATOW: Isn't science like that?

Prof. SPRUSTON: It is. It's what makes it exciting, actually. It's what makes it fun to do science.

FLATOW: I understand that, but the idea that there may be diseases connected to this, there may be a new mechanism for neuronal disease or brain disease or dysfunction that we never thought about might be somehow locked up in a structure we never knew about.

Prof. SPRUSTON: That's right. I mean, I think that when people go looking for causes of diseases or, let's say, neuronal manifestations of neurological and psychiatric disorders, you think about that in terms that you understand, in the way that we know how neurons function and how they communicate with each other.

For example, Alzheimer's disease is thought to be a disease that affects the synapses, the connections between neurons.

If there are unknown forms of communication, then we have really no way of identifying that those forms of communication might be involved in diseases. And there are lots of candidate diseases for this particular part of the brain that we're studying, and maybe this is a contributing factor.

FLATOW: What kinds of diseases might that...

Prof. SPRUSTON: So, the two diseases, neurological diseases, that affect the hippocampus the most or most commonly are epilepsy and Alzheimer's disease. There are also psychiatric diseases like schizophrenia, depression and anxiety.

The hippocampus has been implicated in being involved in all of those diseases. And again, if we want to understand how this structure, the hippocampus itself, is affected by disease, we need to understand in great detail how the individual neurons work and what the molecular mechanisms are for the various types of behavior that they exhibit.

FLATOW: You keep saying we need to understand. How do we proceed?

Prof. SPRUSTON: Yeah. Well, I think that, you know, there's a lot of different approaches in neuroscience. There's a lot of fancy, expensive equipment. There's new developments in technology that are truly fascinating.

And - but I think the most powerful approach that we have is collaboration. I think that the brain is such an incredibly complex organism that there's no single approach that is going to solve the problem.

And I think that bringing together smart people with different approaches to studying the brain - some studying its structural features, some studying its functional or physiological features, others studying its molecular composition - having those people collaborate together, I think, is the key to trying to understand how the brain works.

And I mentioned before the power of computational modeling. I think what we need to do in neuroscience is bring together facts that have been learned from experiments of different types and bring them together and integrate those findings in the form of computational models.

And, for example, you know, the NIH has supported the work that we've done in part through a program that is designed to bring together people doing experiments and people doing computational modeling. So it's a joint effort by the NIH and the NSF to try to really get people bringing together information from various types of experimental approaches and bringing them together specifically in computer models.

FLATOW: And a computer model, briefly, is trying to model how the brain works, but on a computer?

Prof. SPRUSTON: That's right. And you can do that on many different levels. So, for example, you could make a computer model of a large circuit of neurons. You could make a computer model of an individual neuron. You can even make computer models of small parts of neurons.

And the idea is to bring together knowledge in a very quantitative fashion. You know, you think you can understand that doing X causes Y in a neuron. Well, now do you have a system of quantitative equations that can really explain that behavior in a reliable fashion?

And as I mentioned before, sometimes because these models become very, very complicated, they sometimes lead to surprising behavior that's unexpected. And when you see that unexpected behavior, it allows you to identify new experiments that you might never have thought of otherwise.

FLATOW: Well, Dr. Spruston, thank you very much for taking time to talk with us today, and good luck to you.

Prof. SPRUSTON: I appreciate the opportunity to be here. Thank you.

FLATOW: You're welcome.

FLATOW: Nelson Spruston, professor of neurobiology and physiology at Northwestern University in Evanston, Illinois.

Take a quick break. We'll be coming back to talk more about the brain - this time, your cell phone on your brain.

Stay with us. We'll be right back.

(Soundbite of music)

FLATOW: I'm Ira Flatow. This is SCIENCE FRIDAY, from NPR.

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