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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Is there a ‘multi-dimensional universe’ in the brain? A case study in neurobabble

I was asked a question on Quora about a recent study that talked about high-dimensional ‘structures’ in the brain. It has been receiving an inordinate amount of hype, partly as a result of the journal’s own blog. Their headline reads:

‘Blue Brain Team Discovers a Multi-Dimensional Universe in Brain Networks’

As if the reference to a ‘universe’ weren’t bad enough, the last author, Henry Markram, says the following:

“We found a world that we had never imagined”.

The following passage in the blog post doubles-down on the conflation:

“If 4D worlds stretch our imagination, worlds with 5, 6 or more dimensions are too complex for most of us to comprehend.”

As will soon be clear, using words like ‘universe’ and ‘world’ in conjunction with the word ‘dimension’ creates a false impression that these researchers are dealing with spatial dimensions and/or how the brain represents them. This is simply not the case.

This is the question I was asked:

What exactly are the recently discovered multidimensional geometrical objects in the neuronal networks of the brain?

Here is what I wrote:

In this particular case the hype has gotten so out of control that the truth may already be irretrievably buried in mindless nonsense.

The key message is this: the word ‘dimension’ in this paper has nothing to do with the dimensions of space.

Here’s the paper that is receiving all the hype about higher dimensions:

Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function

It came out recently in the journal Frontiers in Computational Neuroscience. The authors employ somewhat complex ideas from graph theory to analyze connectivity among neurons in a small segment of rat neocortex.

This is where the authors of the paper talk about ‘dimension’:

“Networks are often analyzed in terms of groups of nodes that are all-to-all connected, known as cliques. The number of neurons in a clique determines its size, or more formally, its dimension.” [Italics in original.]

So dimension here just refers to the number of neurons that are connected in an all-to-all network. In the area of rat neocortex they studied, they found that 11-neuron cliques were common.

The other concept they talk about is ‘cavities’:

“The manner in which directed cliques bind together can be represented geometrically. When directed cliques bind appropriately by sharing neurons, and without forming a larger clique due to missing connections, they form cavities (“holes,” “voids”) in this geometric representation, with high-dimensional cavities forming when high-dimensional (large) cliques bind together.”

The word dimension has a variety of meanings in mathematics and science. The idea of spatial dimension is most common: when we refer to 3D movies, this is the kind of dimension we are thinking of. The space we are familiar with has 3 dimensions, which we can think of in terms of X, Y, and Z coordinates, or in terms of up-down, left-right, and front-back directions.

The dimension of a network has nothing to do with spatial dimension. Instead, it has more to do with the number of degrees of freedom in a system. In physics, the number of degrees of freedom is the number of independent parameters or quantities that uniquely define a system.

So really, this paper is just talking about the statistics of local neuronal connectivity. They describe their findings as surprising when compared to other statistical models. All this means is that certain connectivity patterns are more common that one might expect under certain ‘random’ models. That’s what they mean here when they compare their results to ‘null models’:

“The numbers of high-dimensional cliques and cavities found in the reconstruction are also far higher than in null models, even in those closely resembling the biology-based reconstructed microcircuit, but with some of the biological constraints released. We verified the existence of high-dimensional directed simplices in actual neocortical tissue.”

Outside of the narrow community of computational neuroscientists who use graph theory, these results are interesting but hardly ground-breaking. Moreover, as far as I can tell these findings have no definitive functional implications. (There are some implications for network synchrony, but in my opinion synchrony has itself not been clearly linked with higher-level concepts of function.)

Neural network modelers assume all kinds of connectivity patterns than deviate from pure ‘randomness’, so such findings aren’t particularly surprising.

So I am baffled by the hype this research is getting. It strikes me that extremely lazy science journalism has collided with opportunistic PR practices.

Given what I’ve explained, I hope it’s clear that these kinds of headlines are profoundly — almost maliciously— misleading:

In fact, given that the brain contains around 80–100 billion neurons, we might consider 11 dimensions to be rather low for sub-networks, if we remind ourselves that dimension in this case simply means the number of neurons that are connected to each other.

Could every neuron be genetically unique?

Years ago I asked what I thought might be a naive question, perhaps on Quora: how do we really know that every cell has the same genome? Was a random sampling of the body conducted? The impression I got was that such a sampling was not conducted, or at least not done regularly and systematically. The main argument for genetic identity was theoretical: the cell division process was well understood (apparently), and the error-correction mechanisms were robust. I was always suspicious of this way of thinking. As Yogi Berra said,

“In theory there is no difference between theory and practice. In practice there is.”

Since then I’ve tried to dive into the nitty-gritty of genetics, and the level of complexity is so staggering that the confidence of theoreticians seemed misplaced. (I wrote a 4 part series on biological information for 3QD, and explored the history of genetics in the process.)

So this article in Scientific American makes a lot of sense to me. Here are some excerpts:

“Accepted dogma holds that—although every cell in the body contains its own DNA—the genetic instructions in each cell nucleus are identical. But new research has now proved this assumption wrong. There are actually several sources of spontaneous mutation in somatic (nonsex) cells, resulting in every individual containing a multitude of genomes—a situation researchers term somatic mosaicism. “The idea is something that 10 years ago would have been science fiction,” says biochemist James Eberwine of the University of Pennsylvania. “We were taught that every cell has the same DNA, but that’s not true.” There are reasons to think somatic mosaicism may be particularly important in the brain, not least because neural genes are very active.”

“Mature neurons stop dividing and are among the longest-living cells in the body, so mutations will stick around in the brain. “In the skin or gut, cells turn over in a month or week so somatic mutations aren’t likely to hang around unless they form cancer,” McConnell says. “These mutations are going to be in your brain forever.” This could alter neural circuits, thereby contributing to the risk of developing neuropsychiatric disorders. ”

“The fact specific genes only explain a small proportion of cases may be because researchers have only been looking in the germ line (sex cells), McConnell says. “Maybe the person doesn’t have the mutation in their germ line, but some percentage of their neurons have it.” Somatic mosaicism may also contribute to neural diversity in general. “It might explain why everybody’s different—it’s not all about the environment or genome. There’s something else,” says neuroscientist Alysson Muotri of the University of California, San Diego, who is not part of the consortium. “As we understand more about somatic mosaicism, I think the contribution to individuality as well as the spectrum [of symptoms] you find in, for example, autism, will become clear.””

Scientists Surprised to Find No Two Neurons Are Genetically Alike


Ways of Knowing

Here is an excerpt from my latest 3QD essay:

“To attempt an understanding of understanding, I think it might make sense to situate our verbal forms of knowledge-generation in the wider world of knowing: a world that includes the forms that we share with animals and even plants. To this end, I’ve come up with a taxonomy of understanding, which, for reasons that should become apparent eventually, I will organize in a ring. At the very outset I must stress that in humans these ways of knowing are very rarely employed in isolation. Moreover, they are not fixed faculties: they influence each other and gradually modify each other. Finally, I must stress that this ‘systematization’ is a work in progress. With these caveats in mind, I’d like to treat each of the ways of knowing in order, starting at the bottom and working my way around in a clockwise direction.”

Read the rest at 3 Quarks Daily: Ways of Knowing

(I’ve collected links to all my 3QD essays here.)

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