The Pentagon of Neuroscience — An Infographic/Listicle for Understanding the Neuroculture

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brainzNeuroscience has hit the big time. Every day, popular newspapers, websites and blogs offer up a heady stew of brain-related self-help (neuro-snake oil?) and gee wiz science reporting (neuro-wow?). Some scientists and journalists — perhaps caught up in the neuro-fervor — throw caution to the wind, promising imminent brain-based answers to the kinds of questions that probably predate civilization itself: What is the nature of mind? Why do we feel the way we do? Does each person have a fundamental essence? How can we avoid pain and suffering, and discover joy, creativity, and interpersonal harmony?

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Wave goodbye to the “unconscious mind”!

I came across a nice little presentation on certain problematic ways of thinking:

Let’s Wave Goodbye to the Unconscious Mind

Here’s are two excerpts:

(Lecturer stops waving hand) It’s gone, thank goodness. Now let’s get on with the talk.

This presentation concerns the problems of using nouns rather than verbs when referring to certain activities.

When we do so, we are sometimes inclined to ask the wrong questions.

Incidentally, I wonder where that wave went.

Where is it now?

Is it in my arm?

Is it stored somewhere?

This presentation concerns the problems and misunderstandings that arise when we nominalise and reify activities. We thereby create entities…..


But you might suppose that later today you may start to think about some of the ideas that I have been discussing. Surely you can only do this if there is some thing, some representation of this material – a memory – that exists in your mind and which you retrieve, when you decide to, as you would draw a file from a filing cabinet?

We can say that this is so ‘only in a manner of speaking’, but a more accurate and less misleading description, is to say that, as you are listening to me now biochemical changes are occurring in your brain that enable you, in the future, to engage in the activity of recalling this material.

But do not these observable neuronal properties constitute your memory of this information? Recall again the example of waving. An anatomist may perform a careful examination of a person’s arm and hand. From its macro-and micro-anatomical properties he will conclude that indeed the arm is designed to wave. Put energy into it and it cannot fail to wave. But nowhere in the arm will the anatomist locate a wave.

Likewise, perhaps it will eventually be possible for neuroanatomists to examine a neuronal network and conclude from its structure, properties and location that, put energy into it and it cannot help but engage in recalling recent activities. But what the neuroanatomists will not find is a thought, a memory or an image.

Read the whole thing here:

Let’s Wave Goodbye to the Unconscious Mind


What neuroscience too often neglects: Behavior

A Quora conversation led me to recent paper in Neuron that highlights a very important problem with a lot of neuroscience research: there is insufficient attention paid to the careful analysis of behavior. The paper is not quite a call to return to behaviorism, but it is an invitation to consider that the pendulum has swing too far in the opposite direction, towards ‘blind’ searches for neural correlates. The paper is a wonderful big picture critique, so I’d like to just share some excerpts.


“Neuroscience is replete with cases that illustrate the fundamental epistemological difficulty of deriving processes from processors. For example, in the case of the roundworm (Caenorhabditis elegans), we know the genome, the cell types, and the connectome—every cell and its connections (Bargmann, 1998; White et al., 1986). Despite this wealth of knowledge, our understanding of how all this structure maps onto the worm’s behavior remains frustratingly incomplete.”

“New technologies have enabled the acquisition of massive and intricate datasets, and the means to analyze them have become concomitantly more complex. This in turn has led to a need for experts in computation and data analysis, with a reduced emphasis on organismal-level thinkers who develop detailed functional analyses of behavior, its developmental trajectory,and its evolutionary basis. Deep and thorny questions like‘‘what would even count as an explanation in this context,’’ ‘‘what is a mechanism for the behavior we are trying to understand,’’and ‘‘what does it mean to understand the brain’’ get sidelined. The emphasis in neuroscience has transitioned fromthese larger scope questions to the development of technologies,model systems, and the approaches needed to analyze the deluge of data they produce. Technique-driven neuroscience could be considered an example of what is known as the substitution bias: ‘‘[.] when faced with a difficult question, weoften answer an easier one instead, usually without noticing the substitution’’ (Kahneman, 2011, p. 12).”

This next excerpt raises an important issue with interpretations of mirror neuron studies. (I also have my own little rant about mirror neuron “theory”.)
“Interpretation then has the following logic: as neurons can be decoded for intention in the first person, and these same neurons decoded for the same intention in the third person, then activation of the mirror neurons can be interpreted as meaning that the primate has understood the intention of the primate it is watching. The problem with this attempt to posit an algorithm for ‘‘understanding’’ based on neuronal responses is that no independent behavioral experiment is done to show evidence that any kind of understanding is actually occurring, understanding that could then be correlated with the mirror neurons. This is a key error in our view: behavior is used to drive neuronal activity but no either/ or behavioral hypothesis is being tested per se. Thus, an interpretation is being mistaken for a result; namely, that the mirror neurons understand the other individual.”

Here the authors talk about the importance of emergence as a bridge between neurons and behavior:

“The phenomenon at issue here, when making a case for recording from populations of neurons or characterizing whole networks, is emergence—neurons in their aggregate organization cause effects that are not apparent in any single neuron. Following this logic, however, leads to the conclusion that behavior itself is emergent from aggregated neural circuits and therefore should also be studied in its own right. An example of an emergent behavior that can only be understood at the algorithmic level, which in turn can only be determined by studying the emergent behavior itself, is flocking in birds. First one has to observe the behavior and then one can begin to test simple rules that will lead to reproduction of the behavior, in this case best done through simulation. The rules are simple—for example, one of them is ‘‘steer to average heading of neighbors’’ (Reynolds, 1987). Clearly, observing or dissecting an individual bird, or even several birds could never derive such a rule. Substitute flocking with a behavior like reaching, and birds for neurons, and it becomes clear how adopting an overly reductionist approach can hinder understanding.”

Krakauer, John W., Asif A. Ghazanfar, Alex Gomez-Marin, Malcolm A. MacIver, and David Poeppel. “Neuroscience needs behavior: correcting a reductionist Bias.” Neuron 93, no. 3 (2017): 480-490. [Paywalled]

My main criticism of this paper might be that their proposed solution — a separation between analysis of behavior and analysis of neural data, with behavioral analysis ideally happening first — might be too rigid, and also might leave the behavioral analysis somewhat underconstrained. It is definitely important to have a clear behavioral hypothesis if you are running a behavioral study, even if is part of a larger study of neural data. But the actual process of understanding might require a lot more ‘cross-talk’. It may not always be useful to come up with a high-level analysis of behavior in isolation from neural data. There is no guarantee that the high-level analysis will be accurate: there may be multiple high level models that correspond to the same behavioral data. So we might need to add a third circle to their Figure 1: one for the space of behavioral explanations.

Could the brain be a radio for receiving consciousness?


bradio.pngHere’s an answer I wrote a while ago to the following question:

 Is there any conclusive proof that the brain produces consciousness? What rules out the case that brain acts as receptor antennae for consciousness?

This is actually a fun question! Taken in the right spirit, it can be a good way to learn about what science is, and also what the limitations of science are.

What would count as proof that the brain produces consciousness? In the future we might try an experiment like this: we build an artificial brain. Let’s say we can all agree that it exhibits consciousness (leaving aside for now the extremely tricky question of what the word “consciousness” even means). Would this prove that the brain “produced” consciousness? Maybe.

But maybe the brain-as-antenna crowd would claim that their favored hypothesis hasn’t been ruled out. After all, if consciousness is somehow floating in the ether, how could we be sure that our artificial brain wasn’t just tuned to the ‘consciousness frequency’, like a gooey pink radio?

We’d need to construct some kind of cosmic-consciousness-blocking material, and then line the walls of our laboratory with it. Then we’d be able to decide on the question one way or the other! If our artificial brain showed no signs of consciousness, the antenna crowd could claim victory, and say “See!, you need cosmic consciousness in order to get biological consciousness! Consciousness is like yogurt: if you have some you can always make more.”

Constructing an artificial brain is hard enough. We have no idea if we will ever have enough understanding of neuroscience to do so. But constructing a consciousness-shield is straight out of science fiction, and just sounds absurd.

In any case, there’s actually a much bigger problem facing any scientific approach to consciousness. No one has any idea what consciousness is. Sure, there’s plenty of philosophical speculation and mystical musing, but in my opinion there’s almost nothing solid from a scientific perspective.

Here’s why I think science cannot ever address the subject of consciousness: science studies objectively observable phenomena, whereas the most crucial aspect of consciousness is only subjectively observable. What are objectively observable phenomena? They’re the ones that more than one person can observe and communicate about. Through communication, they can agree on their properties. So the word “inter-subjective” is a pretty good synonym for “objective”. Objectivity is what can be agreed upon by multiple subjective perspectives.

So the sun is a pretty objective feature of reality. We can point to it, talk about it, and make measurements about it that can be corroborated by independent groups of people.

But consciousness is not objective in the same way that the sun is. I do not observe anyone else’s consciousness. All I observe are physical perceptions: the sights and sounds and smells and textures associated with bodies. From these perceptions I build up a picture of the behavior of an organism, and from the behavior I infer things about the organism’s state of mind or consciousness. The only consciousness I have direct experience of is my own. And even my own consciousness is mysterious. I do not necessarily observe my consciousness. I observe with my consciousness. Consciousness is the medium for observation, but it not necessarily a target of observation.

Clearly all the scientists who claim to study consciousness would disagree with my perspective. Their approach is to take some observable phenomenon — either behavior or some neural signal — and define it as the hallmark of consciousness. There’s nothing wrong with defining consciousness as you see fit, but you can never be completely sure if your explicit definition lines up with all your intuitions about the boundary between conscious and non-conscious.

For example, Information Integration Theory (IIT) proposes that there is a quantity called phi (which at the current historical juncture appears impossible to compute) that captures the degree of consciousness in a system. Armed with this kind of theory, it is possible to argue* that extended, abstract entities — such as the United States as a whole — are conscious. Some people like this generous approach. Why lock up consciousness in skulls? The proponents of IIT have gone so far as to claim that they are okay with panpsychism: the idea that everything from quarks to quasars is at least a little bit conscious.

If everything is conscious, then the question of whether the brain “produces” consciousness — or the universe “transmits” it — becomes moot. There is no ‘problem of consciousness’, since it’s already everywhere.

Neuroscientists like me will probably still have jobs even if society decides to bite the panpsychist bullet. We have other things to worry about beyond consciousness. In fact many of us are actively uninterested in talking about consciousness — we call it “the c-word”. We’re happy to just study behavior in all its objectively observable glory, and hope to understand how the brain produces that. Whether and where exactly consciousness arises during this process seems like a question we can leave unanswered for a generation or two (while enjoying the various after-work conversations about it, of course!). For now we can focus on how our gooey pink radios give rise to language, or memory, or emotion, or even the basic control of muscles.


* Philosopher Eric Schwitzgebel wrote a very interesting essay entitled ‘If Materialism Is True, the United States Is Probably Conscious’.

More on the dreaded c-word!

Here are some consciousness-related answers that may be of interest:

How does the brain create consciousness?

What percent chance is there that whole brain emulation or mind uploading to a neural prosthetic will be feasible by 2048? [I’ve posted this one on this blog too.]

What are some of the current neuroscientific theories of consciousness?

What do neuroscientists think of the philosopher David Chalmers?

Is anything real beyond our own perspective?

What is the currently best scientific answer to the psycho-physical (body-mind) question?

Why an organism is not a “machine”

I just came across a nice article explaining why the metaphor of organism as machine is misleading and unhelpful.

The machine conception of the organism in development and evolution: A critical analysis

This excerpt makes a key point:

“Although both organisms and machines operate towards the attainment of particular ends that is, both are purposive systems the former are intrinsically purposive whereas the latter are extrinsically purposive. A machine is extrinsically purposive in the sense that it works towards an end that is external to itself; that is, it does not serve its own interests but those of its maker or user. An organism, on the other hand, is intrinsically purposive in the sense that its activities are directed towards the maintenance of its own organization; that is, it acts on its own behalf.”

In this section the author explains how the software/hardware idea found its way into developmental biology.

“The situation changed considerably in the mid-twentieth century with the advent of modern computing and the introduction of the conceptual distinction between software and hardware. This theoretical innovation enabled the construction of a new kind of machine, the computer, which contains algorithmic sequences of coded instructions or programs that are executed by a central processing unit. In a computer, the software is totally independent from the hardware that runs it. A program can be transferred from one computer and run in another. Moreover, the execution of a program is always carried out in exactly the same fashion, regardless of the number of times it is run and of the hardware that runs it. The computer is thus a machine with Cartesian and Laplacian overtones. It is Cartesian because the software/hardware distinction echoes the soul/body dualism: the computer has an immaterial ‘soul’ (the software) that governs the operations of a material ‘body’ (the hardware). And it is Laplacian because the execution of a program is completely deterministic and fully predictable, at least in principle. These and other features made the computer a very attractive theoretical model for those concerned with elucidating the role of genes in development in the early days of molecular biology.”

The machine conception of the organism in development and evolution: A critical analysis

I’ve actually criticized the genetic program metaphor myself, in the following 3QD essay:

3quarksdaily: How informative is the concept of biological information?


Image source: Digesting Duck – Wikipedia

“Conscious realism”: a new way to think about reality (or the lack thereof?)


Interesting interview in the Atlantic with cognitive scientist Donald D. Hoffman:

The Case Against Reality

“I call it conscious realism: Objective reality is just conscious agents, just points of view. Interestingly, I can take two conscious agents and have them interact, and the mathematical structure of that interaction also satisfies the definition of a conscious agent. This mathematics is telling me something. I can take two minds, and they can generate a new, unified single mind. Here’s a concrete example. We have two hemispheres in our brain. But when you do a split-brain operation, a complete transection of the corpus callosum, you get clear evidence of two separate consciousnesses. Before that slicing happened, it seemed there was a single unified consciousness. So it’s not implausible that there is a single conscious agent. And yet it’s also the case that there are two conscious agents there, and you can see that when they’re split. I didn’t expect that, the mathematics forced me to recognize this. It suggests that I can take separate observers, put them together and create new observers, and keep doing this ad infinitum. It’s conscious agents all the way down.”


Here’s the striking thing about that. I can pull the W out of the model and stick a conscious agent in its place and get a circuit of conscious agents. In fact, you can have whole networks of arbitrary complexity. And that’s the world.


“As a conscious realist, I am postulating conscious experiences as ontological primitives, the most basic ingredients of the world. I’m claiming that experiences are the real coin of the realm. The experiences of everyday life—my real feeling of a headache, my real taste of chocolate—that really is the ultimate nature of reality.”

I don’t agree with everything in the article (especially the quantum stuff) but I think many people interested in consciousness and metaphysics will find plenty of food for thought here:

The Case Against Reality

Also, the “conscious agents all the way down” is the exact position I was criticizing in a recent 3QD essay:

3quarksdaily: Persons all the way down: On viewing the scientific conception of the self from the inside out

The diagram above is from a science fiction story I was working on, back when I was a callow youth. It closely related to the idea of a network of conscious agents. Here’s another ‘version’ of it.


Not sure why I made it look so morbid. 🙂

What’s the deal with “brainwaves”?

I was asked this question on Quora recently:

The brain has neurons which approximately perform function evaluations on their inputs. How would this cause brainwaves of different frequencies? Why are they related to the level of arousal in the brain (sleep/meditation etc)?
Is there some sort of “controller” that synchronizes the firing?

Here’s my answer:

This is an excellent set of questions!

Short(ish) answers:

1. Why does the brain have waves?

There is no consensus on their functional role, but some researchers think oscillations facilitate flexible coordination and communication among neurons.

2. The brain has neurons which can approximately be understood as performing function evaluations on their inputs, though the actual mechanisms are more complex. But how would this cause brainwaves of different frequencies to exist?

There are several possible mechanisms that can cause the input-output transformation of neurons to lead to oscillations when the neurons are connected in networks. Furthermore, neurons themselves often have intrinsic oscillatory properties (other than basic spiking), such as rebound excitation following inhibition.

3. Is there some sort of “controller” that synchronizes the firing?

There is no single controller causing synchronization or oscillation — there are actually several local and meso-scale mechanisms, often involving inhibitory interneurons*. (Note that synchronization and oscillation are distinct phenomena. You can have synchronized non-oscillatory processes, and oscillations that are not synch-ed.)

Long answers:

What are brain waves?

Even though we use the generic term ‘brain waves’, there are actually a variety of distinct mechanisms at work that lead to rhythmic behavior in different frequency bands. And in many cases we still don’t know what the mechanism is that causes a particular brain rhythm, or can’t decide among multiple plausible mechanisms.

Even though I’m a computational neuroscientist, I find it hard to conceptually integrate all the different perspectives on neural activity. But I’ve been thinking about this a lot lately, so here goes!

The most fine-grained perspective involves measuring the voltage of an individual neuron. This voltage can change in various ways. The most well known is the spike, or action potential. Spikes travel efficiently down the axon, and typically cause vesicle release in the synapse, which allows neurotransmitters to affect the post-synaptic neuron. But spiking is not the only kind of voltage fluctuation in a neuron. There are also ‘sub-threshold’ fluctuations, which are often oscillatory. These oscillations may represent oscillatory and/or synchronized inputs to the neuron that are insufficient to cause spiking. Single-electrode and multi-electrode recordings of cell activities can pick up both the sub-threshold fluctuations and the supra-threshold spiking.

Brain waves were first discovered through electroencephalography (EEG), which has been around for a century or so. Unlike electrode-based recording, EEG is non-invasive (meaning we don’t have to poke any sharp objects into anyone). But the main drawback of EEG is that it is a coarse-grained measure of neural activity. It measures the cumulative electrical activity of very large numbers of neurons.

It’s also important to realize that EEG essentially measures the synchronized inputs to a brain area, rather than the firing outputs. This has to do with the biophysics of the technique, which you can read about in more detail in my answer to the question “Are EEG voltages related to the average action potential firing rate of the cortical neurons near the electrode or are the voltages the average of low frequency voltage oscillations of the neurons?” EEG effectively measures the degree of synchronization of neurons that send inputs to the brain region directly underneath the EEG electrode.

Other techniques that can pick up oscillatory activity include magnetoencephalography (MEG) and Electrocorticography (ECoG).

Brain rhythms tend to be grouped into frequency bands. The most well-studied bands have been assigned Greek letters that reflect the order of their discovery. Here’s a list of the bands, along with their frequency ranges in hertz (Hz). I’ve linked to their Wikipedia pages, when available.

  • Slow 3: 0.025-0.067
  • Slow 2: 0.200-0.500
  • Slow 1: 0.500-1.429
  • Delta: 1 – 4
  • Theta: 4 – 8
  • Mu and SMR: 7.5 – 12.5
  • Alpha: 9 – 13
  • Beta: 14 – 30
  • Gamma: 30 – 80
  • Fast: 80 – 200
  • Ultrafast: 200 – 600.000

What are the mechanisms that cause brain waves?

Through a combination of experimental techniques and theoretical approaches, neuroscientists have come up with several mechanisms that can explain oscillations in various frequency bands. In many cases it is not clear which mechanism is in fact at work.

I can’t really go though all the theoretical mechanisms that have been proposed, but I can talk about one that is very intuitive to understand: the PING model of Gamma activity. This model, developed by Nancy Kopell, has been widely corroborated by experimental work. (There may be different types of Gamma, however, and not all of them are covered by this model.)

PING stands for pyramidal-interneuronal network gamma, and involves interaction between excitatory cells and inhibitory cells, resulting in the creation of a nonlinear oscillator. The following diagram [1] illustrates the mechanism quite nicely:

The E(xcitatory) cells excite the I(nhibitory) cells, which in turn inhibit the same excitatory cells. This results in an oscillation whose frequency is determined by the rate of integration of the I-cells. Fast I-cells can produce fast rhythms.

There are plenty of other mechanisms for oscillations, but the PING model will give you a flavor of how they can be constructed. Arousal levels can by incorporated into models by incorporating factors such as neurotransmitter level fluctuations, and the effects of such fluctuation on the sub-threshold and/or firing properties of neurons. Changes in behavioral state can also change the inputs to various brain areas from the body and from the outside world, which in turn will affect the network activity mode. This is a vast topic for theory and computational modeling.

What is the purpose of brain waves?

This is actually still a very contentious issue. The field of neuroscience can be broadly divided into researchers who care about oscillations, and researchers who don’t. People who don’t care about oscillations are more interested in the firing activities and the input-output transformations of neurons and networks. Some of these researcher even go so far as to claim that oscillations are ‘epiphenomena’ — mere side-effects of the ‘main’ neural processes, such as integration, contrast enhancement, switching, resetting, and so on. I was broadly in the ‘skeptics’ category for many years, but I’ve started to realized that oscillations can’t be ignored. The power in various frequency bands often correlates strongly with behavioral measures, so oscillations are at the very least telling us something important about how the brain works.

One theory of brain waves that is becoming popular is the idea of coordination, or “communication through coherence”. The idea is that neurons which are in the same sub-threshold oscillatory state are more likely to be able to communicate spikes with each other. This is shown diagrammatically below [2]. The black neuron is out of phase with the blue neuron, so it communicates with the blue neuron less effectively that the red neuron.

As I mentioned earlier, synchrony and rhythmicity are completely distinct. Presumably you can get coherence without any rhythms, just by synchronizing groups of neurons. But maybe rhythmic behavior is more easy to control.

I’ve only scratched the surface of this topic. There is definitely a lot more to the story of brain waves, and in the coming decades hopefully researchers will work towards an integrated theory.

Images from:

[1] Cortical enlightenment: are attentional gamma oscillations driven by ING or PING? | pdf

[2] A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. | pdf

Further reading

* What do inhibitory neurons do in the brain? by Yohan John on Neurologism

Are EEG voltages related to the average action potential firing rate of the cortical neurons near the electrode or are the voltages the average of low frequency voltage oscillations of the neurons?

What changes occur in the brain when we close our eyes?

Is working memory associated with synchronization between oscillations in the prefrontal cortex and oscillations elsewhere in the brain? (e.g. parietal cortex)?

Medical Imaging: How strong is the correlation between a fMRI and an EEG?

Does the membrane time constant of neurons put a constraint on the frequencies seen in neuronal brain waves?

The Emotional Gatekeeper — a computational model of emotional attention

My paper is finally out in PLoS Computational Biology. It’s an open access journal, so everyone can read it:

The Emotional Gatekeeper: A Computational Model of Attentional Selection and Suppression through the Pathway from the Amygdala to the Inhibitory Thalamic Reticular Nucleus

Here’s the Author Summary, which is a simplified version of the abstract:

“Emotional experiences grab our attention. Information about the emotional significance of events helps individuals weigh opportunities and dangers to guide goal-directed behavior, but may also lead to irrational decisions when the stakes are perceived to be high. Which neural circuits underlie these contrasting outcomes? A recently discovered pathway links the amygdala—a key center of the emotional system—with the inhibitory thalamic reticular nucleus (TRN) that filters information between the thalamus and cortex. We developed a neural network model—the Emotional Gatekeeper—that demonstrates how the newly discovered pathway from the amygdala to TRN highlights relevant information to help assess threats and opportunities. The model also shows how the amygdala-TRN pathway can lead normal individuals to discount neutral but useful information in highly charged emotional situations, and predicts that disruption of specific nodes in this circuit underlies distinct psychiatric disorders.”


Here’s the full citation:

John YJ, Zikopoulos B, Bullock D, Barbas H (2016) The Emotional Gatekeeper: A Computational Model of Attentional Selection and Suppression through the Pathway from the Amygdala to the Inhibitory Thalamic Reticular Nucleus. PLoS Comput Biol 12(2): e1004722. doi:10.1371/journal.pcbi.1004722