“Authentic bio-gibberish”

It turns out that one of the 2017 Nobel Laureates is quite a character!

“Jeffrey Hall, a retired professor at Brandeis University, shared the 2017 Nobel Prize in medicine for discoveries elucidating how our internal body clock works. He was honored along with Michael Young and his close collaborator Michael Roshbash. Hall said in an interview from his home in rural Maine that he collaborated with Roshbash because they shared common interests in “sports, rock and roll, beautiful substances and stuff.”

“About half of Hall’s professional career, starting in the 1980s, was spent trying to unravel the mysteries of the biological clock. When he left science some 10 years ago, he was not in such a jolly mood. In a lengthy 2008 interview with the journal Current Biology, he brought up some serious issues with how research funding is allocated and how biases creep into scientific publications.”

I highly recommend watching this video, where he comes up with the term “authentic bio-gibberish” to describe the overly technical jargon used by scientists.


What does the frontal lobe have to do with planning and decision-making?

I was asked the following question on Quora:

Why is planning & decision making situated in the frontal lobe?

Here’s my answer:

Why not? 🙂

I suppose the most obvious answer is the fact that the motor cortex resides in the frontal cortex.

Now you may justifiably ask: what does the motor cortex have to do with planning and decision-making?

The connection is this: to a large extent, the purpose of the brain is to control the body. So planning and decision-making, at the simplest possible level, involves determining when and how to move the body.

From this perspective, it is interesting to speculate that all thoughts derive from the process of virtual or simulated movement. Thought arises in the ‘gap’ between perception and action.

The way an organism interacts with its environment can be understood in terms of the perception-action loop.

Stimuli from the outside world enter the brain through the sensory organs and percolate through the various brain regions, allowing the organism to form neural ‘representations’ or ‘maps’ of the world. Signals originating inside the body (such as from the stomach or the lungs) allow for similar maps of the inner world of the organism.

By using memory to compare past experience with present conditions, an organism can anticipate the future to meet its current needs: either by acting in the present, or by planning an action for some future time.

The signals that control our voluntary muscles emanate from the motor cortex: the neurons in this part of the brain are the ‘switches’, ‘levers’ and ‘buttons’ that allow us to change our body position and configuration.

So going back one more step, the signals that influence the motor cortex constitute the ‘proximal’ decision signal. Much of the input to motor cortex comes from premotor and prefrontal areas, which are nearby in the frontal lobe. The thalamus also sends important signals to motor cortex, as do the neuromodulatory systems (which include the dopamine, acetylcholine, norepinephrine and serotonin systems).

Ultimately you can keep going ‘back’ to see how every part of the brain influences the ultimate decision: sensation, memory and emotion all play a role. But the prefrontal and premotor areas constitute the most easily identifiable decision areas.

As to why these brain areas are located in the frontal lobe at all… this is a much more difficult question. The short answer is evolution by natural selection. But the long answer is still incomplete. Brains are soft tissue, so they don’t leave fossils.

Is the mind a machine?

My latest 3QD essay explores the “mind as machine” metaphor, and metaphors in general.

Putting the “cog” in “cognitive”: on the “mind as machine” metaphor

Here’s an excerpt:

People who study the mind and brain often confront the limits of metaphor. In the essay ‘Brain Metaphor and Brain Theory‘, the vision scientist John Daugman draws our attention to the fact that thinkers throughout history have used the latest material technology as a model for the mind and body. In the Katha Upanishad (which Daugman doesn’t mention), the body is a chariot and the mind is the reins. For the pre-Socratic Greeks, hydraulic metaphors for the psyche were popular: imbalances in the four humors produced particular moods and dispositions. By the 18th and 19th centuries, mechanical metaphors predominated in western thinking: the mind worked like clockwork. The machine metaphor has remained with us in some form or the other since the industrial revolution: for many contemporary scientists and philosophers, the only debate seems to be about what sort of machine the mind really is. Is it an electrical circuit? A cybernetic feedback device? A computing machine that manipulates abstract symbols? Some thinkers so convinced that the mind is a computer that they invite us to abandon the notion that the idea is a metaphor. Daugman quotes the cogntive scientist Zenon Pylyshyn, who claimed that “there is no reason why computation ought to be treated merely as a metaphor for cognition, as opposed to the literal nature of cognition”.

Daugman reacts to this Whiggish attitude with a confession of incredulity that many of us can relate to: “who among us finds any recognizable strand of their personhood or of their experience of others and of the world and its passions, to be significantly illuminated by, or distilled in, the metaphor of computation?.” He concludes his essay with the suggestion that “[w]e should remember than the enthusiastically embraced metaphors of each “new era” can become, like their predecessors, as much the prisonhouse of thought as they at first appeared to represent its liberation.”

Read the rest at 3 Quarks Daily:

Putting the “cog” in “cognitive”: on the “mind as machine” metaphor

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