Sleep – what is it good for (absolutely nothing?)

Sleep can often feel like a fall into annihilation and rebirth. One moment you have all the memories and aches of a long day behind you, the next you wake up from nothingness into the start of something new. Or: a rush from some fictional slumber world into an entirely different waking world. What is it that’s happening inside your head? Why this rest?

Generally the answer that we are given is some sort of spring cleaning and consolidation, a removal of cruft and a return to what is important (baseline, learned). There is certainly plenty of evidence that the brain is doing this while you rest. One of the most powerful of these ‘resetting’ mechanisms is homeostatic plasticity. Homeostatic plasticity often feels like an overlooked form of learning, despite the gorgeous work being done on it (Gina Turrigiano and Eve Marder’s papers have been some of my all-time favorite work for forever).

One simple experiment that you can do to understand homeostatic plasticity is to take a slice of a brain and dump TTX on it to block sodium channels and thus spiking. When you remove it days later, the neurons will be spiking like crazy. Slowly, they will return to their former firing rate. It seems like every neuron knows what its average spiking should be, and tries to reach it.

But when does it happen? I would naively think that it should happen while you are asleep, while your brain can sort out what happened during the day, reorganize, and get back where it wants to be. Let’s test that idea.

Take a rat and at a certain age, blind one eye. Then just watch how visual neurons change their overall firing rate. Like so:Screen Shot 2016-04-03 at 11.26.07 AMdarklight-homeostasis

At first the firing rate goes down. There is no input! Why should they be doing anything? Then, slowly but surely the neuron goes back to doing what it did before it was blinded. Same ol’, same ol’. Let’s look at what it’s doing when the firing rate is returning to its former life:

sleep-homeostasisThis is something of a WTF moment. Nothing during sleep, nothing at all? Only when it is awake and – mostly – behaving? What is going on here?

Here’s my (very, very) speculative possibility: something like efference copy. When an animal is asleep, it’s getting nothing new. It doesn’t know that anything is ‘wrong’. Homeostatic plasticity may be ‘returning to baseline’, but it may also be ‘responding to signals the same way on average’. And when it is asleep, what signals are there? But when it is moving – ah, that is when it gets new signals.

When the brain generates a motor signal, telling the body to move, it also sends signals back to the sensory areas of the brain to let it know what is going on. Makes it much easier to keep things stable when you already know that the world is going to move in a certain way. Perhaps – perhaps – when it is moving, it is getting the largest error signals from the brain, the largest listen to me signals, and that is exactly when the homeostatic plasticity should happen: when it knows what it has something to return to baseline in respect to.

Reference

Hengen, K., Torrado Pacheco, A., McGregor, J., Van Hooser, S., & Turrigiano, G. (2016). Neuronal Firing Rate Homeostasis Is Inhibited by Sleep and Promoted by Wake Cell, 165 (1), 180-191 DOI: 10.1016/j.cell.2016.01.046

The frontal cortex, freeing you from the straightjacket of genes

Robert Sapolsky – possibly the best neurobiologist science writer – has an article on teenagers and the krazy stuff they do:

Around the onset of adolescence, the frontal cortex is the only brain region that has not reached adult levels of grey matter, made up of neuronal cell bodies. It would seem logical that gray matter levels would increase thereafter. But no, over the course of adolescence, frontal cortical gray matter volume decreases.

These traits are exacerbated when adolescents are around peers. In one study, Laurence Steinberg of Temple University discovered that adolescents and adults, when left on their own, don’t differ in the risks they take in a driving simulator. Add peers egging them on and rates don’t budge in adults but become significantly higher in teens. When the study is carried out in a brain scanner, the presence of peers (egging on by intercom) lessens frontal cortical activity and enhances activity in the limbic dopamine system in adolescents, but not in adults….As has been said, the greatest crime-fighting tool available to society is a 30th birthday.

So what is the adaptive advantage of human brain development evolving this way? Potentially, there is no advantage…No, I think that the genetic program of brain development has evolved to help free the frontal cortex from the straightjacket of genes. If the frontal cortex is the last part of the brain to fully mature, it is by definition the brain region least shaped by that genome and most sculpted by experience. With each passing day, the frontal cortex is more the creation of what life has thrown at you, and thus who you become.

Frontal cortex, responsible for high cognitive functions and decisions, is like a ruthless lumberjack. The forest of neurons grows and, at the time of puberty, the lumberjack marches in and starts trimming anything that doesn’t seem useful. As Sapolsky suggests, what happens in adolescence is indelibly marked on your life. This suggests a curious possibility: does the age at which you go through puberty affect your future behavior? Children are going through puberty at a younger and younger age these days; and those who go through early or  late puberty will have vastly different experiences and cultural environments surrounding them. Since the age at which you go through puberty has some impact on your behavior – what are the differences in what you are learning during that time?

I couldn’t find any good references but if anyone knows anything, please let me know!

Update: I forgot to mention how this is a great example of genes putting you in a place where you have the opportunity to develop some phenotype. As in: perhaps early puberty does not cause children to be more wild than average, say, but it may more often put a child in an environment that makes the behavior more attractive.

Mirror neurons?

The strongest claim about mirror neurons is that they are responsible for human uniqueness, because they turned us into a singularly social primate. “It is widely believed that hyper-sociality is what makes humans ‘special,’ the key to understanding why it is we, and not the members of any other species, who dominate the world with our language, artefacts and institutions,” wrote Heyes in her 2010 commentary. “Therefore, in the light of this ‘adaptation hypothesis,’ mirror neurons emerge as an evolutionary foundation of human uniqueness…If mirror neurons are an adaptation, and more ‘advanced’ in humans than in monkeys, they may well play a major role in explaining the evolutionary origins and online control of human social cognition,” she wrote. Indeed, that’s the same claim that Ramachandran makes…

But recent research casts doubt on the adaptation hypothesis. Increasing evidence indicates that the so-called “mirror effect” in brain cells can be enhanced, abolished, or even reversed due to the effects of learning. The mirror-neuron systems of dancers and musicians, for example, have different properties than those of others, and those tool-sensitive mirror neurons in monkeys only come about as a result of experience with tools…

That means that mirror neurons didn’t evolve, per se. What evolved is the mechanism that produces mirror neurons: associative learning, our ability to identify statistical patterns in the world, to associate one event with another, like the ringing of a bell with a tasty dinner. And associative learning is present in a wide variety of species, meaning that its mere presence can’t be the “evolutionary foundation of human uniqueness.”

Jason Goldman has a nice essay in Nautilus on the ubiquitous mirror neurons. Mirror neurons are fairly controversial, with questions as to whether they actually exist as a distinct population of cells.

Monday Open Thread: The Six Problems of Systems Neuroscience

I was brainstorming experiments and decided to make a list of what I think are the fundamental questions in systems neuroscience:

  1. Sensory: How do we represent the world?
  2. Motor: How do we create an action?
  3. Decision: How do we choose among competing alternatives?
  4. Learning: How do we remain plastic in changing environments?
  5. Computation: What are the underlying algorithms and computations?
  6. Modulation: How does internal state affect the nervous system?

Can anyone think of other broad questions in systems neuroscience? Should one of these not be here? Most other things I could think of belong here; for instance, “How do we deal with external and internal noise?” would probably be under Sensory or Learning. AYWNMBTTOF wrote a great post on what he considers the big questions of his field (taste) which I would subsume under Sensory.

I kind of hope this replaces the somewhat useless 23 Problems in Systems Neuroscience in terms of clarifying what we are studying.

Vision is for decision

Cholinergic learningWhen we typically think of how decision-making works in the brain, we think of new input coming in, perhaps through the eyes or ears, being processed in the relevant sensory areas, and then sent to the ‘decision-making’ areas (the basal ganglia, prefrontal cortex, or anterior cingulate cortex) where this information is used to make a decision.  Although useful and intuitive, this modular view ends up giving short shrift to some areas that do heavy lifting.

Sensory areas are not actually the ruthless calculating machines that we tend to think of, but are in fact quite plastic.  This ability of sensory cortex to modify its own responses allows it to participate in certain decisions: for instance, it can learn how long to wait in order to get a reward.  If a rat receives two visual cues that predict how long it will have to wait in order to receive a reward – either a short time or a long time – neurons in the initial part of visual cortex, V1, will maintain a heightened firing rate to match that duration.

This is accomplished through something like reinforcement learning.  When learning whether a visual cue is giving an animal information about how long it will have to wait for a reward, acetylcholine acts as a ‘reinforcement signal’.  The effect is to change encoding of the reward by modifying the strength of the synapses in the network.

Although we tend to think of certain ‘decision-making’ areas of the brain, in reality all of the brain is participating in every decision at some level or another.  In certain cases – perhaps when speed is of the essence or maybe when you want other areas of the brain to be involved in the computations and processing of that decision – even sensory portions of the brain are learning how to make decisions.  It is not always dopamine, the ‘rewarding’ or ‘motivational’ chemical in the brain that supports this decision-making: other neuromodulators like acetylcholine often play the very same role.

References

Chubykin, A., Roach, E., Bear, M., & Shuler, M. (2013). A Cholinergic Mechanism for Reward Timing within Primary Visual Cortex Neuron, 77 (4), 723-735 DOI: 10.1016/j.neuron.2012.12.039

The in-between of nature and nurture

There’s a debate that never seems to die down, and it’s one of nature versus nurture.  It’s a bit of a silly debate because the answer in every debate is (almost) always “both”, but it does seem to get a lot of play.  And it’s even sillier when you realize that one can ask the question about any behavior in our life, and we already know the answer.  Take, for example, what type of food you like.  There are certain foods that, innately, everyone likes, things that are required for survival: I imagine these are things like bacon and butter and otter pops.  But other foods, foods that are not as full of fat and grease and sugar, these things take some acclimation: brussel sprouts and pigs feet and rocky mountain oysters.  There’s always a genetic underpinning – for instance, there is a specific genetic mutation that determines whether we can taste certain bitter flavors – whose behavioral expression gets modified through the environment.  The question is though: when we learn to like these foods, what exactly are we learning?

To understand how this type of learning works, we can turn to the hawkmoth Manduca sexta.  As caterpillars they feed on tobacco and tomato plants, but when they become adult moths they flap about in the dark, feeding on the nectar from night-blooming flowers.  These plants exist in a symbiotic relationship with the hawkmoths – without the moths, the flowers would have trouble getting pollinated.  Perhaps that is why the flowers that the hawkmoths prefer to visit all release a very similar chemical bouquet; other flowers, even genetically related flowers, have different scents, and these flowers do not get pollinated by the hawkmoths.

Now if  you go in and stick an electrode into the antennal lobe, the area where odors are first received by the hawkmoth, what do you see?  Recording from the projection neurons, the neurons most responsible for sending this odor information back to other parts of the hawkmoth brain, you will see what appear to be two different types of responses.  The hawkmoth odor neurons will respond to the attractive odors – the odors that were taken from flowers that the hawkmoths prefer to pollinate – and these responses all look pretty similar.  They are sudden, strong neuronal responses.  But if you now spray the hawkmoth with odors that aren’t particularly attractive, you get much weaker responses that look very little like the responses to attractive odors.

There’s the nature part of your story: there are some odors that even a naive, laboratory-raised hawkmoth will love, and others that it won’t care about.  But that’s clearly not the end of the story.  Just like humans can take that first sip of beer and spit it out because it’s disgusting only to find themselves savoring a good porter years later, hawkmoths can learn that a new, unknown odor might signal something delicious.  And it’s quick: with just three tastes of an odor paired with some nectar, the hawkmoths learn to like the odor.  It’s not just anywhere that the moths learn to like the odor, either.  It could be that the odor becomes more attractive somewhere deep in the brain, where reward neurons respond when they see the responses corresponding to this specific odor.  What actually happens is that it is the olfactory projection neurons themselves that change how they respond, to look like the responses to other attractive odors.  Even though there are odors that are genetically programmed to be attractive to the hawkmoth, interaction with the environment can directly modify how an odor is sensed to make it more or less attractive: nature, and nurture.

Screen shot 2013-01-14 at 10.29.16 AM

References

Riffell, J., Lei, H., Abrell, L., & Hildebrand, J. (2012). Neural Basis of a Pollinator’s Buffet: Olfactory Specialization and Learning in Manduca sexta Science, 339 (6116), 200-204 DOI: 10.1126/science.1225483
Photo from…I realize this is a different species of Hawkmoth, but work with me here!  There’s only so many decent pictures of Hawkmoths under the Creative Commons license at flickr.

Learning: positive and negative

Reward and punishment operate through two very different pathways in the human brain.  The general idea is that these two types of learning – positive and negative – operate through different unique types of dopamine receptors.  The D1 receptors (D1R) are generally ‘positive’ receptors, while the D2 receptors (D2R) are ‘negative’.  Specifically, D1Rs generally tend to increase the concentration of CamKII and D2Rs decrease it; this means that they are going to have opposite effects on downstream pathways such as receptor plasticity, intrinsic voltage channels, etc.

How are the D1 and D2 pathways distinct in terms of learning?  The hypothesis has been that in striatal projection neurons, D1R expressing medium spiny neurons (dMSNs) mediate reinforcement and D2R expressing indirect pathway neurons (iMSNs) mediate punishment.  Kravitz et al expressed channelrhodopsin selectively in dMSNs and iMSNs so they could use light to activate only one type of neuron at a time.  They figured that the striatum would be a good place to start looking for the effects of these neurons.  After all, it is a primary site of reinforcement and action selection (also, they probably tried a few other places and didn’t get great results…?).  These transgenic mice were then placed in a box with two triggers, one of which would stimulate the light and the other would do nothing.  So the mice are in this box, and able to turn on and off their neurons if they want to.  I wonder how that feels?

When the mice were able to activate their D1R (positively-reinforcing) neurons, they were much more likely to keep pressing the trigger.  The D2R (negatively-reinforcing) mice were more likely to press the other trigger.  But that’s not all!  By the third day, the effects of activating the D2R pathway had worn off – they no longer cared about the effect.  You can see this on the graph to the left, where 50% is chance.  The preference for the D1R pathway persisted, however.  Even on short time scales of 15 – 30 seconds, the mice kept their preference for stimulating D1R reward cells over D2R aversion cells.  In the figure to the right, this is seen with YFP being a control (it should have no effect); whereas activating the dMSN pathway over the first 30 seconds always is different than activating YFP, the iMSN pathway only shows a (statistical) different over the first 15 seconds.

The authors conclude by saying that that the dMSN pathway is sufficient for persistent reinforcement, while iMSNs are sufficient for transient punishment.  This is a nice finding; that the D1R pathway really is doing some positive reinforcement and that the D2R pathway is doing negative reinforcement, and one is more effective in the long-term than the other.  Remember this when raising your kids!

References
Kravitz AV, Tye LD, & Kreitzer AC (2012). Distinct roles for direct and indirect pathway striatal neurons in reinforcement. Nature neuroscience PMID: 22544310