A brief critique of predictive coding

Predictive coding is becoming a popular theory in neuroscience (see for example Clark 2013). In a nutshell, the general idea is that brains encode predictions of their sensory inputs. This is an appealing idea because superficially, it makes a lot of sense: functionally, the only reason why you would want to process sensory information is if it might impact your future, so it makes sense to try to predict your sensory inputs.

There are substantial problems in the details of predictive coding theories, for example with the arbitrariness of the metric by which you judge that your prediction matches sensory inputs (what is important?), or the fact that predictive coding schemes encode both noise and signal. But I want to focus on the more fundamental problems. One has to with “coding”, the other with “predictive”.

It makes sense that brains anticipate. But does it make sense that brains code? Coding is a metaphor of a communication channel, and this is generally not a great metaphor for what the brain might do, unless you fully embrace dualism. I discuss this at length in a recent paper (Is coding a relevant metaphor for the brain?) so I won’t repeat the entire argument here. Predictive coding is a branch of efficient coding, so the same fallacy underlies its logic: 1) neurons encode sensory inputs; 2) living organisms are efficient; => brains must encode efficiently. (1) is trivially true in the sense that one can define a mapping from sensory inputs to neural activity. (2) is probably true to some extent (evolutionary arguments). So the conclusion follows. Critiques of efficient coding have focused on the “efficient” part: maybe the brain is not that efficient after all. But the error is elsewhere: living organisms are certainly efficient, but it doesn’t follow that they are efficient at coding. They might be efficient at surviving and reproducing, and it is not obvious that it entails coding efficiency (see the last part of the abovementioned paper for a counter-example). So the real strong assumption is there: the main function of the brain is to represent sensory inputs.

The second problem has to with “predictive”. It makes sense that an important function of brains, or in fact of any living organism, is to anticipate (see the great Anticipatory Systems by Robert Rosen). But to what extent do predictive coding schemes actually anticipate? First, in practice, those are generally not prediction schemes but compression schemes, in the sense that they do not tell us what will happen next but what happens now. This is at least the case of the classical Rao & Ballard (1999). Neurons encode the difference between expected input and actual input: this is compression, not prediction. It uses a sort of prediction in order to compress: other neurons (in higher layers) produce predictions of the inputs to those neurons, but the term prediction is used in the sense that the inputs are not known to the higher layer neurons, not that the “prediction” occurs before the inputs. Thus the term “predictive” is misleading because it is not used in a temporal sense.

However, it is relatively easy to imagine how predictive coding might be about temporal predictions, although the neural implementation is not straightforward (delays etc). So I want to make a deeper criticism. I started by claiming that it is useful to predict sensory inputs. I am taking this back (I can because I said it was superficial reasoning). It is not useful to know what will happen. What is useful is to know what might happen, depending on what you do. If there is nothing you can do about the future, what is the functional use of predicting it? So what is useful is to predict the future conditionally to a different set of potential actions. This is about manipulating models of the world, not representing the present.

The substrate of consciousness

Here I want to stir some ideas about the substrate of consciousness. Let us start with a few intuitive ideas: a human brain is conscious; an animal brain is probably conscious; a stone is not conscious; my stomach is not conscious; a single neuron or cell is not conscious; the brainstem or the visual cortex is not a separate conscious entity; two people do not form a single conscious entity.

Many of these ideas are in fact difficult to justify. Let us start with single cells. To see the problem, think first of organisms that consist of a single cell. For example, bacteria, or ciliates. In this video, an amoeba’s engulfs and then digests two paramecia. At some point, you can see the paramecia jumping all around as if they were panicking. Are these paramecia conscious, do they feel anything? If I did not know anything about their physiology or size, my first intuition would be that they do feel something close to fear. However, knowing that these are unicellular organisms and therefore do not have a nervous system, my intuition is rather that they are not actually conscious. But why?

Why do we think a nervous system is necessary for consciousness? One reason is that organisms to which we ascribe consciousness (humans and animals, or at least some animals) all have a nervous system. But it’s a circular argument, which has no logical validity. A more convincing reason is that in humans, the brain is necessary and sufficient for consciousness. A locked-in patient is still conscious. On the other hand, any large brain lesion has an impact on conscious experience, and specific experiences can be induced by electrical stimulation of the brain.

However, this tends to prove that the brain is the substrate of my experience, but it says nothing about, say, the stomach. The stomach also has a nervous system, it receives sensory signals and controls muscles. If it were conscious, I could not experience it, by definition, since you can only experience your own consciousness. So it could also be, just as for the brain, that the stomach is sufficient and necessary for consciousness of the gut mind: perhaps if you stimulate it electrically, it triggers some specific experience. As ridiculous as it might sound, I cannot discard the idea that the stomach is conscious just because I don’t feel that it’s conscious; I will need arguments of a different kind.

I know I am conscious, but I do not know whether there are other conscious entities in my body. Unfortunately, this applies not just to the stomach, but more generally to any other component of my body, whether it has a nervous system or not. What tells me that the liver is not conscious? Imagine I am a conscious liver. From my perspective, removing one lung, or a foot, or a large part of the visual cortex, has no effect on my conscious experience. So the fact that the brain is necessary and sufficient for your conscious experience doesn’t rule out the fact that some other substrate is necessary and sufficient for the conscious experience of another entity in your body. Now I am not saying that the question of liver consciousness is undecidable, only that we will need more subtle arguments than those exposed so far (discussed later).

Let us come back to the single cell. Although I feel that a unicellular organism is not conscious because it doesn’t have a nervous system, so far I have no valid argument for this intuition. In addition, it turns out that Paramecium, as many other unicellular organism including (at least some) bacteria, is an excitable cell with voltage-gated channels, structurally very similar to a neuron. So perhaps it has some limited form of consciousness after all. If this is true, then I would be inclined to say that all unicellular organisms are also conscious, for example bacteria. But then what about a single cell (eg a neuron) in your body, is it conscious? One might object that a single cell in a multicellular organism is not an autonomous organism. To address this objection, I will go one level below the cell.

Eukaryotic cells (eg your cells) have little energy factories called mitochondria. It turns out that mitochondria are in fact bacteria which have been engulfed in cells a very long (evolutionary) time ago. They have their own DNA, but they now live and reproduce inside cells. This is a case of endosymbiosis. If mitochondria were conscious before they lived in cells, why would they have lost consciousness when they started living in cells? So if we think bacteria are conscious, then we must admit that we have trillions of conscious entities in the cells of our body – not counting the bacteria in our digestive system. The concept of an autonomous organism is an illusion: any living organism depends on interactions with an ecosystem, and that ecosystem might well be a cell or a multicellular organism.

By the same argument, if we think unicellular organisms are conscious, then single neurons should be conscious, as well as all single cells in our body. This is not exclusive of the brain being conscious as a distinct entity.

A plausible alternative, of course, is that single cells are not conscious, although I have not yet proposed a good argument for this alternative. Before we turn to a new question, I will let you contemplate the fact that bacteria can form populations that are tightly coupled by electrical communication. Does this make a bacteria colony conscious?

Let us now turn to another question. We can imagine that a cell is somehow minimally conscious, and that at the same time a brain forms a conscious entity of a different nature. Of course it might not be true, but there is a case for that argument. So now let us consider two people living their own life on opposite sides of the planet. Can this pair form a new conscious entity? Here, there are arguments to answer negatively. This is related to a concept called the unity of consciousness.

Suppose I see a red book. In the brain, some areas might respond to the color and some other areas might respond to the shape. It could be then that the color area experiences redness, and the shape area experience bookness. But I, as a single conscious unit, experiences a red book as a whole. Now if we consider two entities that do not interact, then there cannot be united experiences: somehow the redness and the bookness must be put together. So the substrate of a conscious entity cannot be made of parts that do not interact with the rest. Two separated people cannot form a conscious entity. But this does not rule out the possibility that two closely interacting people may not form a conscious superentity. Again, I do not believe this is the case, but we need to find new arguments to rule this out.

Now we finally have something a little substantial: a conscious entity must be made of components in interaction. From this idea follow a few remarks. First, consciousness is not a property of a substrate, but of the activity of a substrate (see a previous blog post on this idea). For example, if we freeze the brain in a particular state, it is not conscious. This rules out a number of inanimate objects (rocks) as conscious. Second, interactions take place in time. For example, it takes some time, up to a few tens of ms, for an action potential to travel from one neuron to another. This implies that a 1 ms time window cannot enclose a conscious experience. The “grain” of consciousness for a human brain should thus be no less than a few tens of milliseconds. In the same way, if a plant is conscious, then that consciousness cannot exist on a short timescale. This puts a constraint on the kind of experiences that can be ascribed to a particular substrate. Does consciousness require a nervous system? Maybe it doesn’t, but at least for large organisms, a nervous system is required to produce experiences on a short timescale.

I want to end with a final question. We are asking what kind of substrate gives rise to consciousness. But does consciousness require a fixed substrate? After all, the brain is dynamic. Synapses appear and disappear all the time, all the proteins get renewed regularly. The brain is literally a different set of molecules and a different structure from one day to the next. But the conscious entity remains. Or at least it seems so. This is what Buddhists call the illusion of self: contrary to your intuition, you are not the same person today and ten years ago; the self has no objective permanent existence. However, we can say that there is a continuity in conscious experience. That continuity, however, does not rely on a fixed material basis but more likely on some continuity of the underlying activity. Imagine for example a fictional worm that is conscious, but the substrate of consciousness is local. At some point it is produced by the interaction of neurons at some particular place of the nervous system, then that activity travels along the worm’s spine. The conscious entity remains and doesn’t feel like it’s travelling, it is simply grounded on a dynamic substrate.

Now I don’t think that this is true of the brain (or of the worm), but rather that long-range synchronization has something to do with the generation of a global conscious entity. However, it is conceivable that different subsets of neurons, even though they might span the same global brain areas, are involved in conscious experience at different times. In fact, this is even plausible. Most neurons don’t fire much, perhaps a few Hz on average. But one can definitely have a definite conscious experience over a fraction of second, and that experience thus can only involve the interaction of a subset of all neurons. We must conclude that the substrate of consciousness is actually not fixed but involve dynamic sets of neurons.

A summary of these remarks. I certainly have raised more questions than I have answered. In particular, it is not clear whether a single cell or a component of the nervous system (stomach, brainstem) is conscious. However, I have argued that: 1) any conscious experience requires the interaction of the components that produce it, and this interaction takes place in time; 2) the set of components that are involved in any particular experience is dynamic, despite the continuity in conscious experience.

Tip for new PIs : always do administrative work at the last minute, or later

This is a tip that has taken me years to really grasp, and I still haven't fully internalized it. I don't like to work at the last minute. If I have something to do and I don't do it, then it stays in the back of my mind until I do it. So, especially if it's some boring task like administrative work, I like to get rid of it as soon as possible. That's a mistake. I'm speaking of my experience in France, so maybe it doesn't apply so much elsewhere. The reason it's a mistake is that what you are required to do changes all the time, so the latest you do it, the least work you will have to do.

Every new politician seems to want to add a new layer of bureaucracy, independently of their political origin, so the amount of administrative work you are required to do as a scientist keeps growing, and it doesn't seem to converge. But setting up new rules and reglementations in a complex bureaucratic monster is not easy, so the monster often outputs nonsensical forms and requirements. One example in France is the evaluation of labs (HCERES), whose role is unclear and changing. The amount of redundancy and the absurdity of some requirements is abysmal. For example, you are required to fill a SWOT diagram, to select and list 20 % of all your « outputs », but also to list each one of them in another form, etc. Because many of the requirements are vague and nonsensical, any organization that deals with them will take some time to converge to a clear set of rules issued to the labs. I have written my evaluation document about 4 times because of the changing instructions.

Another recent example is the new evaluation system set up by INSERM (national medical research institution). Someone there (external consulting company ?) apparently decided that having an online CV with fields to fill instead of uploading a text would be more convenient. So for example you have to insert, one by one in web forms, the list of all journals for which you have reviewed in your entire career, and how many papers you have reviewed for each of them. You need to insert the list of all students you have supervised in you entire carrier, with names and exact dates, etc, all one by one in separate fields. Imagine that for senior PIs. Guess what : one week before deadline, the requirement of filling that CV was lifted for most scientists because of many complaints (a bit too late for most of them, including me). About a quarter of them still have to, but the message says that the format of the CV will change next year since it was not good, so all the work will basically be for nothing.

So here is my conclusion and tip : bureaucracy is nonsense and don't assume otherwise ; just set yourself some time on the deadline to do the required work, whatever it might become at that time (and it might disappear).

Project: Binaural cues and spatial hearing in ecological environments

I previously laid out a few ideas for future research on spatial hearing:

  1. The ecological situation and the computational problem.
  2. Tuning curves.
  3. The coding problem.

This year I wrote a grant that addresses the first point. My project was rather straightforward:

  1. To make binaural recordings with ear mics in real environments, with real sound sources (actual sounding objects) placed at predetermined positions. This way we obtain distributions of binaural cues conditioned on source direction, capturing the variability due to context.
  2. To measure human localization performance in those situations.
  3. To try to see if a Bayesian model can account for these results, and possibly previous psychophysical results.

The project was preselected but unfortunately not funded. I probably won't resubmit it next year, except perhaps with a collaboration. So here it is for everyone to read: my grant application, "Binaural cues and spatial hearing in ecological environments". If you like it, if you want to do these recordings and experiments, please do so. I am interested in the results, but I'm happy if someone else does it. Please contact me if you would like to set up a collaboration, or discuss the project. I am especially interested in the theoretical analysis (ie the third part of the project). Our experience in the lab is primarily on the theoretical side, but also in signal analysis, and we have done a number of binaural recordings too and some psychophysics.

Are journals necessary filters?

In my previous post, I argued that one reason why many people cling to the idea that papers should be formally peer-reviewed before they are published, cited and discussed, despite the fact that this system is a recent historical addition to the scientific enterprise, is a philosophical misunderstanding about the nature of scientific truth. That is, the characteristic of science, as opposed to religion, is that it is never validated; it can and must be criticized. Therefore, no amount of peer reviewing can ever be a stamp of approval for “proven facts”. Instead what we need is public discussion of the science, not stamps of approvals.

In response to that post came up another common reason why many people think it’s important to have journals that select papers after peer-review. The reason is that we are crowded with millions of papers and you can’t read everything, so you need some way to know which paper is important, based on peer-review. So here this is not just about peer-reviewing before publishing, but also about the hierarchy of journals. Journals must do an editorial selection so that you don’t have to waste your time reading low-quality papers, or uninteresting papers. What this means, quite literally, is that you only read papers from “top journals”.

Here I want to show that this argument is untenable, because selecting their readings based on journal names is not what scientists should do or actually do, and because the argument is logically inconsistent.

Why is it logically inconsistent? If the argument is correct, then those papers accepted in lower rank journals should not be read because they are not worth reading. But in that case, why publish them at all? There seems to be no reason for the existence of journals that people do not read because they do not have time to read bad papers. If we argue that those journals should exist because in some cases there are some papers worth reading there, for any sort of reason, then we must admit that we don’t actually use journals as filters, or that we should not use them as filters (see below).

Is it good scientific practice to use journal names as filters? What this implies is that you ignore any paper, including papers in your field and potentially relevant to your own studies, which are not published in “top journals”. So for example, you would not cite a relevant study if it’s not from a top journal. It also means that you don’t check that your own work overlaps other studies. So you potentially take credit for ideas that you were not the first to have. Is this a professional attitude?

If in fact you don’t totally ignore those lower journals, then you don’t actually use journal name as a filter, you actually do look at the content of papers independently of the journal they are published in. Which is my final point: to use journal names as filters is not the normal practice of scientists (or maybe I’m optimistic?). When you look for relevant papers on your topic of interest, you typically do a search (eg pubmed). Do you only consider papers from “top journals”, blindly discarding all others? Of course not. You first look at the titles to see if it might be relevant; then you read the abstract if they are; if the abstract is promising you might open the paper and skim through it, and possibly read it carefully if you think it is worth it. Then you will look at cited papers; or at papers that cite the interesting you just read; or you will read a review; maybe a colleague or your advisor will suggest a few readings. In brief: you do a proper bibliographical search. I cannot believe that any good scientist considers that doing a bibliographical search consists in browsing the table of contents of top journals.

The only case when you do use journal names to select papers to read is indeed when you read tables of contents every month for a few selected journals. How much of this accounts for the papers that you cite? You can get a rough idea of this by looking at the cited half-life of papers or journals. For Cell, it’s about 9 years. I personally also follow new papers on biorxiv using keywords, while most new papers in journals are irrelevant to me because they cover too many topics.

In summary: using journals as filters is not professional because it means poor scholarship and misattribution of credit. Fortunately it’s not what scientists normally do anyway.

One related argument that came out in the discussion of my previous post is that having papers reviewed post-publication could not work because that would be too much work, and consequently most papers would not be reviewed, while at least in the current system every paper is peer reviewed. That is wrong in several ways. First, you can have papers published then peer-reviewed formally and publicly (as in F1000 Research), without this being coupled to editorial selection. Second, if anything, having papers submitted a single time instead of many times to different journals implies that there will be less work for reviewers, not more. Third, what is exactly the advantage of having each paper peer-reviewed if it is argued that those papers should not be read or cited? In the logic where peer review in “good journals” serves as filters for important papers, it makes no difference whether the unimportant papers are peer reviewed or not, so this cannot count as a valid argument against post-publication review.

All this being said, there is still a case for editorial selection after publication, as one of the many ways to discover papers of interest, see for example my free journal of theoretical neuroscience.

The great misunderstanding about peer review and the nature of scientific facts

Last week I organized a workshop on the future of academic publication. My point was that our current system, based on private pre-publication peer review, is archaic. I noted that the way the peer review system is currently organized (where external reviewers judge both the quality of the science and the interest for the journal) represents just a few decades in the history of science. It can hardly qualify as the way science is or should be done. It is a historical feature. For example, only one of Einstein’s papers was formally peer-reviewed; Crick & Watson’s DNA paper was not formally peer-reviewed. Many journals introduced external peer review in the 1960s or 1970s to deal with the growth in the number and variety of submissions (see e.g. Baldwin, 2015); before that, editors would decide whether to publish the papers they received, depending on the number of pages they could print.

Given the possibilities that offers the internet, it seems that there is no reason anymore to couple the two current roles of peer review: editorial selection and scientific discussion. One could simply share their work online, get feedback from the community to discuss the work, and then let people recommend papers to their colleagues and compile all sorts of reader’s digests. No time wasted in multiple submissions, no prestige misattributed to publications in glamour journals, who do not do a better a job than any other journal at pointing errors and frauds. Just the science and the public discussion of science.

But there is a lot of resistance to this idea, namely the idea that papers should be formally approved by peer reviewers before they are published. Because otherwise, so many people claim, the scientific world would be polluted by all sorts of unverified claims. It would not be science anymore, just gossip. I have attributed this attitude to conservatism, first because as noted above this system is a rather recent addition to the scientific enterprise, and second because papers are published before peer review. We call those “preprints”, but really these are scientific papers made public, so by definition they are published. I follow the preprints in my field and I don’t see any particular loss in quality.

However, I think I was missing a key element. The more profound reason why many people, in particular experimental biologists, are so attached to peer review is in my view that they hold naive philosophical views about the notion of truth in science. A paper should be peer-reviewed because otherwise you can’t cite it as a true fact. Peer review validates science, thanks to experts who make sure that the claims of the authors are actually true. Of course it can go wrong and reviewers might miss something, but it is the purpose of peer review. This view is reflected in the tendency, especially in biology journals, to choose titles that look like established truths: “Hunger is controlled by HGRase”, instead of “The molecular control of hunger”. Scientists and journalists can then write revealed truths with a verse reference, such as “Hunger is controlled by HGRase (McDonald et al., 2017)”.

The great misunderstanding is that truth is a notion that applies to logical propositions (for example, mathematical theorems), not to empirical claims. This has been well argued by Popper, for example. Truth is by nature a theoretical concept. Everything said is said with words, and in this sense it always refers to theoretical concepts. One can only judge whether observations are congruent with the meaning attributed to the words, and that meaning necessarily has a theoretical nature. There is no such thing as an “established fact”. This is so even of what we might consider as direct observations. Take for example the claim “The resting potential of neurons is -70 mV”. This is a theoretical statement. Why? First, because to establish it, I have recorded a number of neurons. If you test it, it will be on a different neuron, which I have not measured. So I am making a theoretical claim. Probably, I also tested my neurons with a particular method (not mentioning a particular region and species). But my claim makes no reference to the method by which I have made the inference. That would be the “methods” part of my paper, not the conclusion, and when you cite my paper, you will cite it because of the conclusion, the “established fact”, you will not be referring to the methods, which you consider are the means to establish the fact. It is the role of the reviewers to check the methods, to check that they do establish the fact.

But these are trivial remarks. It is not just that the method matters. The very notion of an observation always implicitly relies on a theoretical background. When I say that the resting potential is -70 mV, I mean that there is a potential difference of -70 mV across the membrane. But that’s not what I measure. I measure the difference in potential between some point outside the cell and the inside of a patch pipette whose solution is in contact with the cell’s inside. So I am assuming the potential is the same in all points of the cytosol, even though I have not tested it. I am also implicitly modeling the cytosol as a solution, even though the reality is more complex than that, given the mass of charged proteins in it. I am assuming that the extracellular potential is constant. I am assuming that my pipette solution reasonably matches the actual cytosol solution, given that “solution” is only a convenient model. I am implicitly making all sorts of theoretical assumptions, which have a lot of empirical support but are still of a theoretical nature.

I have tried with this example to show that even a very simple “fact” is actually a theoretical proposition, with many layers of assumptions. But of course in general, papers typically make claims that rely less firmly on accepted theoretical grounds, since they must be “novel”. So it is never the case that a paper definitely proves its conclusions. Because conclusions have a theoretical nature, all that can be checked is whether observations are consistent with the authors’ interpretation.

So the goal of peer review can’t be to establish the truth. If it were the case, then why would reviewers ever disagree? They disagree because they cannot actually judge whether a claim is true; they can only say whether they are personally convinced. This makes the current peer review system extremely poor, because all the information we get is: two anonymous people were convinced (and maybe others were not, but we’ll never find out). What would be more useful would be to have an open public discussion, with criticisms, qualifications and alternative interpretations fully disclosed for anyone to read and make their own opinion. In such a system, the notion of a stamp of approval on a paper would simply be absurd; why hide the disapprovals? There is the paper, and there is the scientific discussion of the paper, and that is all there needs to be.

There is some concern these days that peer reviewed research is unreliable. Well, science is unreliable. That is almost what defines it: it can be criticized and revised. Seeing peer review as the system that establishes the scientific truth is not only a historical error, it is a great philosophical error, and a dangerous bureaucratic view of science. We don’t need editorial decisions based on peer review. We need free publication (we have it) and we need open scientific discussion (it’s coming). That’s all we need.

What is computational neuroscience? (XXVII) The paradox of the efficient code and the neural Tower of Babel

A pervasive metaphor in neuroscience is the idea that neurons “encode” stuff: some neurons encode pain; others encode the location of a sound; maybe a population of neurons encode some other property of objects. What does this mean? In essence, that there is a correspondence between some objective property and neural activity: when I feel pain, this neuron spikes; or, the image I see is “represented” in the firing of visual cortical neurons. The mapping between the objective properties and neural activity is the “code”. How insightful is this metaphor?

An encoded message is understandable to the extent that the reader knows the code. But the problem with applying this metaphor to the brain is only the encoded message is communicated, not the code, and not the original message. Mathematically, original message = encoded message + code, but only one term is communicated. This could still work if there were a universal code that we could assume all neurons can read, the “language of neurons”, or if somehow some information about the code could be gathered from the encoded messages themselves. Unfortunately, this is in contradiction with the main paradigm in neural coding theory, “efficient coding”.

The efficient coding hypothesis stipulates that neurons encode signals into spike trains in an efficient way, that is, it uses a code such that all redundancy is removed from the original message while preserving information, in the sense that the encoded message can be mapped back to the original message (Barlow, 1961; Simoncelli, 2003). This implies that with a perfectly efficient code, encoded messages are undistinguishable from random. Since the code is determined on the statistics of the inputs and only the encoded messages are communicated, a code is efficient to the extent that it is not understandable by the receiver. This is the paradox of the efficient code.

In the neural coding metaphor, the code is private and specific to each neuron. If we follow this metaphor, this means that all neurons speak a different language, a language that allows expressing concepts very concisely but that no one else can understand. Thus, according to the coding metaphor, the brain is a Tower of Babel.

Can this work?

10 simple rules to format a preprint

Submitting papers to preprint servers (bioRxiv) is finally getting popular in biology. Unfortunately, many of these papers are formatted in a way that is very inconvenient to read, possibly because authors stick to the format asked by journals. Here are 10 simple rules to format your preprints:

  1. Format your preprint in the way you would like to read it. The next rules simply implement this first rule.
  2. Use single spacing. No one is going to write between the lines.
  3. Insert figures and their captions in the text, at the relevant place. It is really annoying when you have to continuously go back and forth between the text and the last pages. Putting figures at the end of the paper and captions yet at another place should be punished.
  4. Don’t forget the supplementary material.
  5. We don’t really need 10 rules. In fact the first rule is just fine.

Technical draft for chapter 5, Propagation of action potentials

I have just uploaded a technical draft on chapter 5 of my book on action potentials: Propagation of action potentials. This draft introduces the cable equation, and how conduction velocity depends on axon diameter in unmyelinated and myelinated axons. There is also a short section on the extracellular potential. There are a few topics I want to add, including branching and determinants of conduction velocity (beyond diameter). There is also (almost) no figure at the moment. Finally, it is likely that the chapter is reorganized for clarity. I wanted to upload this chapter nonetheless so as to move on to the next chapter, on spike initiation with an initial segment.

What is computational neuroscience? (XXVI) Is optimization a good metaphor of evolution?

Is the brain the result of optimization, and if so, what is the optimization criterion? The popular argument in favor of the optimization view goes as follows. The brain is the result of Darwinian evolution, and therefore is optimally adapted to its environment, ensuring maximum survival and reproduction rates. In this view, to understand the brain is primarily to understand what “adapted” means for a brain, that is, what is the criterion to be optimized.

Previously, I have pointed out a few difficulties in optimality arguments used in neuroscience, in particular the problem of specification (what is being optimized) and the fact that evolution is a history-dependent process, unlike a global optimization procedure. An example of this history dependence is the fascinating case of mitochondria. Mitochondria are organelles in all eukaryotes cells that produce most of the cellular energy in the form of ATP. At this date, the main view is that these organelles are a case of symbiosis: mitochondria were once prokaryote cells that have been captured and farmed. This symbiosis has been selected and conserved through evolution, but optimization does not seem to be the most appropriate metaphor in this case.

Nonetheless, the optimization metaphor can be useful when applied to circumscribed problems that a biological organism might face, for example the energy consumption of action potential propagation. We can claim for example that, everything else being equal, an efficient axon is better than an inefficient one (with the caveat that in practice, not everything else can be made equal). But when applied at the scale of an entire organism, the optimization metaphor starts facing more serious difficulties, which I will discuss now.

When considering an entire organism, or perhaps an organ like the brain, then what criterion can we possibly choose? Recently, I started reading “Guitar Zero” by Gary Marcus. The author points out that learning music is difficult, and argues that the brain has evolved for language, not music. This statement is deeply problematic. What does it mean that the brain has evolved for language? Language does not preexist to speakers, so it cannot be that language was an evolutionary (“optimization”) criterion for the brain, unless we have a more religious view of evolution. Rather, evolutionary change can create opportunities, which might be beneficial for the survival of the species, but there is no predetermined optimization criterion.

Another example is the color visual system of bees (see for example Ways of coloring by Thompson et al.). A case can be made that the visual system of bees is adapted to the color of flowers they are interested in. But conversely, the color of flowers is adapted to the visual system of bees. This is a case of co-evolution, where the “optimization criterion” changes during the evolutionary process.

Thus, the optimization criterion does not preexist to the optimization process, and this makes the optimization metaphor weak.

A possible objection is that there is a preexisting optimization criterion, which is survival or reproduction rate. While this might be correct, it makes the optimization metaphor not very useful. In particular, it applies equally to all living species. The point is, there are species and they are different even though the optimization criterion is the same. Not all have a brain. Thus, optimization does not explain why we have a brain. Species that have a brain have different brains. The nervous system of a nematode is not the same as that of a human, even though they are all equally well adapted, and have evolved for exactly the same amount of time. Therefore, the optimization view does not explain why we speak and nematodes don’t, for example.

The problem is that “fitness” is a completely contextual notion, which depends both on the environment and on the species itself. In a previous post where I discussed an “existentialist” view of evolution, I proposed the following thought experiment. Imagine a very ancient Earth with a bunch of living organisms that do not reproduce but can survive for an indefinite amount of time. By definition, they are adapted since they exist. Then at some point, an accident occurs such that one organism starts multiplying. It multiplies until it occupies the entire Earth and resources become scarce. At this point of saturation, organisms start dying. The probability of dying being the same for both non-reproducing organisms and reproducing ones, at some point there will be only reproducing organisms. Thus in this new environment, reproducing organisms are adapted, whereas non-reproducing ones are not. If we look at the history of evolution, we note that the world of species constantly changes. Species do not appear to converge to some optimal state, because as they evolve, the environment changes and so does the notion of fitness.

In summary, the optimization criterion does not preexist to the optimization process, unless we consider a broad existentialist criterion such as survival, but then the optimization metaphor loses its usefulness.