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

Papers of the week, 6/10 – 6/17

Joint attention, social-cognition, and recognition memory in adults

“The early emerging capacity for Joint Attention (JA), or socially coordinated visual attention, is thought to be integral to the development of social-cognition in childhood…We tested the validity of the differentiation of IJA [initiating joint attention] and RJA [responding to joint attention] in our paradigm in two studies of picture recognition memory in undergraduate students. Study 1 indicated that young adults correctly identified more pictures they had previously viewed in an IJA condition (67%) than in a RJA (58%) condition, η2 = 0.57. Study 2 controlled for IJA and RJA stimulus viewing time differences, and replicated the findings of Study 1.”

The biological bases of conformity

“We review the relevant literature considering the causation, function, history, and ontogeny of conformity, and describe a computer-based experiment on human subjects that we carried out in order to resolve ambiguities. We found that only when many demonstrators were available and subjects were uncertain was subject behavior conformist. A further analysis found that the underlying response to social information alone was generally conformist. Thus, our data are consistent with a conformist use of social information, but as subjects’ behavior is the result of both social and asocial influences, the resultant behavior may not be conformist.”

Effects of age, sex, and  neuropsychological performance on financial decision-making

“Results indicated that Older participants significantly outperformed Younger participants on a multiple-choice test of acquired financial knowledge. However, after controlling for such pre-existing knowledge, several age effects were observed. For example, Older participants were more likely to make immediate investment decisions, whereas Younger participants exhibited a preference for delaying decision-making pending additional information…In terms of sex differences, Older Males were more likely to pay credit card bills and utilize savings accounts than were Older Females. Multiple positive correlations were observed between Older participants’ financial decision-making ability and performance on neuropsychological measures of non-verbal intellect and executive functioning. Lastly, the ability to justify one’s financial decisions declined with age, among the Older participants.”

Efficient coding and the neural representation of value

“Although normative theories of choice have outlined the theoretical structure of these valuations, recent experiments have begun to reveal how value is instantiated in the activity of neurons and neural circuits. Here, we review the various forms of value coding that have been observed in different brain systems and examine the implications of these value representations for both neural circuits and behavior. In particular, we focus on emerging evidence that value coding in a number of brain areas is context dependent, varying as a function of both the current choice set and previously experienced values. Similar contextual modulation occurs widely in the sensory system, and efficient coding principles derived in the sensory domain suggest a new framework for understanding the neural coding of value.”  By Paul Glimcher, so of course you should read this.

Orbitofrontal cortical activity during repeated free choice

“OFC neurons encode important features of the choice behavior. These features include activity selective for exceptionally long runs of a given choice (stay selectivity) as well as activity selective for switches between choices (switch selectivity). These results suggest that OFC neural activity, in addition to encoding subjective values on a long timescale that is sensitive to satiety, also encodes a signal that fluctuates on a shorter timescale and thereby reflects some of the statistically improbable aspects of free-choice behavior.”

Physical competition increases testosterone among Amazonian forager-horticulturalists: a test of the ‘challenge-hypothesis’

“We tested whether the Tsimane, pathogenically stressed forager-horticulturalists of the Bolivian Amazon, would express acute T increases in response to physical competition…Linear mixed-effects models were used to establish that T increased significantly immediately following competition (β = 0.23, p < 0.001), remaining high 1 h later (β = 0.09, p = 0.007); equivalent to 30.1 and 15.5 per cent increases in T, respectively. We did not find larger increases in T among winners (p = 0.412), although T increases were positively associated with self-rated performance (β = 9.07, p = 0.004). These results suggest that despite lower levels of T than US males, Tsimane males exhibit acute increases in T at the same relative magnitude reported by studies in industrialized settings, with larger increases in T for those who report better individual performance.”  I covered this partly in my introduction to testosterone earlier in the week.

Individual plastic responses by males to rivals reveal mismatches between behavior and fitness outcomes

“Behaviour (mating duration) was remarkably sensitive to the level of competition and fully reversible, suggesting that substantial costs arise from the incorrect expression of even highly flexible behaviour. However, changes in mating duration matched fitness outcomes (offspring number) only in scenarios in which males experienced zero then high competition. Following the removal of competition, mating duration, but not offspring production, decreased to below control levels. This indicates that the benefit of increasing reproductive investment when encountering rivals may exceed that of decreasing investment when rivals disappear.”

The dynamics of coordinated group hunting and collective information transfer among schooling prey

“Predators were found to frequently form coordinated hunting groups, with up to five individuals attacking in line formation. Attacks were associated with increased fragmentation and irregularities in the spatial structure of prey groups, features that inhibit collective information transfer among prey. Prey group fragmentation, likely facilitated by predator line formation, increased (estimated) per capita risk of prey, provided prey schools were maintained below a threshold size of approximately 2 m2.”

Aging-related increases in behavioral variability: relations to losses of dopamine D1 receptors

“Increasing ISDs [intraindividual standard deviation] were associated with increasing age and diminished D1 binding in several brain regions (anterior cingulate gyrus, dorsolateral prefrontal cortex, and parietal cortex) for the interference, but not control, condition. Analyses of partial associations indicate that the association between age and IIV in the interference condition was linked to D1 receptor losses in task-relevant brain regions. These findings suggest that dysfunctional DA modulation may contribute to increased variability in cognitive performance among older adults.”