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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The kind of brain training that actually works!

Just saw this on twitter:

Here’s the link to the (thus far unreviewed!) study:

How much does education improve intelligence? A meta-analysis

“… we found consistent evidence for beneficial effects of education on cognitive abilities, of approximately 1 to 5 IQ points for an additional year of education. Moderator analyses indicated that the effects persisted across the lifespan, and were present on all broad categories of cognitive ability studied. Education appears to be the most consistent, robust, and durable method yet to be identified for raising intelligence.”

Why human memory is not a bit like a computer’s

My latest 3QD essay is about the mystery of human memory, and why it is not at all like computer memory. I discuss the quirks of human memory formation and recall, and the concept of “content-addressable memory”.

3quarksdaily: Why human memory is not a bit like a computer’s

Here is an excerpt:

Decades of experience with electronics has led many people to think of memory as a matter of placing digital files in memory slots. It then seems natural to wonder about storage and deletion, capacity in bytes, and whether we can download information into the brain ‘directly’, as in the Matrix movies.

The computer metaphor may seem cutting edge, but its essence may be as old as civilization it is the latest iteration of the “inscription metaphor”. Plato, for example, described memory in terms of impressions on wax-tablets — the hard drives of the era. According to the inscription metaphor, when we remember something, we etch a representation of it in a physical medium — like carvings on rock or ink on paper. Each memory is then understood as a discrete entity with a specific location in space. In the case of human beings, this space is between the ears. Some memory researchers even use the term “engram” to refer to the neural counterpart of a specific memory, further reifying the engraving metaphor.

Before getting to the problems with the inscription metaphor, I should say that at a sufficiently fuzzy level of abstraction, it is not entirely useless. There is plenty of neuroscientific evidence that memories are tied to particular brain regions; damage to these regions can weaken or eliminate specific memories. So the general concepts of physical storage and localizability are the least controversial aspect of the inscription metaphor (at least at first glance).

The issue with the inscription metaphor is that it leaves out the aspects of human memory that are arguably the most interesting and mysterious — how we acquire memories and how we evoke them. When we look more closely at how humans form and recall memories, we may even find that the storage and localizability ideas need to be revised.

Read the whole piece here:

3quarksdaily: Why human memory is not a bit like a computer’s


“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.