Revues prédatrices : quel est le problème ?

Un récent article du Monde alerte sur un phénomène qui prend de l’ampleur dans l’édition scientifique : les revues prédatrices (voir aussi l’éditorial). Il s’agit d’éditeurs commerciaux qui publient des articles scientifiques en ligne, contre rémunération, sans aucune éthique scientifique, en particulier en acceptant tous les articles sans qu’ils soient revus par des pairs. De manière similaire, les fausses conférences se multiplient ; des entreprises organisent des conférences scientifiques dans un but purement commercial, sans se soucier de la qualité scientifique.

En réaction, certaines institutions commencent à monter des « listes blanches » de journaux à éviter. C’est compréhensible, puisque le phénomène a un coût important. Mais la réponse néglige le problème fondamental. Il faut se rendre à l’évidence : l’éthique commerciale (recherche du profit) n’est pas compatible avec l’éthique scientifique (recherche de la vérité). Les entreprises dont on parle ne sont pas illégales, à ma connaissance. Elles organisent des conférences qui sont réelles ; elles publient des journaux qui sont réels. Simplement, elles ne se soucient pas de la qualité scientifique, mais de leur profit. On considère cela comme immoral ; mais une entreprise commerciale n’a pas de dimension morale, il s’agit simplement d’une organisation dont le but est de générer du profit. On ne peut s’attendre à ce que les intérêts commerciaux correspondent comme par magie exactement aux intérêts scientifiques.

  1. Le problème de l’édition commerciale

Ceci est vrai aux deux extrémités du spectre de la publication académique : pour les journaux prédateurs comme pour les journaux prestigieux. L’article parle de « fausse science » ; mais la plupart des cas de fraude scientifique ont été révélés dans des journaux prestigieux, pas dans des journaux prédateurs – qui de toutes façons ne sont pas lus par la communauté scientifique (voir par exemple Brembs (2018) pour le lien entre qualité méthodologique et prestige du journal). Pour les journaux commerciaux prestigieux, la stratégie commerciale des éditeurs est non pas de maximiser le nombre d’articles publiés, mais de maximiser le prestige perçu de ces journaux, qui servent ensuite d’appâts pour vendre les collections de journaux de l’éditeur. Autrement dit, c’est une stratégie de marque. Cela passe notamment par une sélection drastique des articles soumis, opérée par des éditeurs professionnels, c’est-à-dire pas par des scientifiques professionnels, sur la base de l’importance perçue des résultats, poussant ainsi une génération de scientifiques à gonfler les prétentions de leurs articles. Cela passe par la promotion auprès des institutions publiques de métriques douteuses comme le facteur d’impact, et plus généralement la promotion d’une mythologie de la publication prestigieuse, à savoir l’idée fausse et dangereuse qu’un article doit être jugé par le prestige du journal dans lequel il est publié, plutôt que par sa valeur scientifique intrinsèque – qui elle est évaluée par la communauté scientifique, pas par un éditeur commercial, ni même par deux scientifiques anonymes. En proposant d’éditer des listes de mauvais journaux, on ne résout pas le problème car l’on adhère implicitement à cette logique perverse.

Il suffit de regarder les marges dégagées par les grandes multinationales de l’édition scientifique pour comprendre que le modèle commercial n’est pas adapté. Pour Elsevier par exemple, les marges sont de l’ordre de 40%. La simple lecture de ce chiffre devrait nous convaincre immédiatement que l’édition scientifique devrait être gérée par des institutions publiques, du moins non commerciales (par exemple des sociétés savantes, comme c’est le cas d’un certain nombre de journaux). Quel est la justification pour faire appel à un opérateur commercial pour gérer un service public, ou n’importe quel service ? La motivation est que la compétition permet de diminuer les coûts et d’améliorer la qualité. Or si les marges sont de 40%, c’est que visiblement la compétition n’opère pas. Pourquoi ? Simplement parce que lorsqu’un scientifique soumet un article, il ne choisit pas le journal en fonction du prix ni même du service rendu (qui est en réalité essentiellement rendu par des scientifiques bénévoles), mais en fonction de la visibilité et du prestige du journal. Il n’y a donc pas de compétition sur les prix. Le pire qui pourrait arriver pour un éditeur commercial est que les articles scientifiques soient jugés à leur valeur intrinsèque plutôt que par le journal dans lequel ils sont publiés, parce qu’alors ce modèle commercial unique s’effondrerait et les journaux seraient en compétition sur les prix et les services qu’ils doivent fournir, comme n’importe quelle autre entreprise commerciale. C’est le pire qui puisse arriver aux éditeurs commerciaux, et le mieux qui puisse arriver à la communauté scientifique. Voilà pourquoi les intérêts commerciaux et scientifiques sont divergents.

Quoi qu’il en soit, il faut se rendre à l’évidence : des marges aussi énormes signifient que le modèle commercial est inefficace. Il faut donc cesser immédiatement de faire appel à des journaux commerciaux. Ce n’est pas très difficile : les institutions publiques sont tout à fait capables de gérer des journaux scientifiques ; il en existe et depuis longtemps. Un exemple récent est eLife, un des journaux les plus innovants actuellement en biologie. Cela ne devrait pas être très étonnant : le cœur de l’activité des journaux, à savoir la relecture des articles, est déjà faite par des scientifiques, y compris chez les éditeurs commerciaux qui font appel à leurs services gratuitement. Cela ne veut pas dire que l’on ne peut pas faire appel à des entreprises privées pour fournir des services, par exemple héberger des serveurs, gérer les sites web, fournir de l’infrastructure. Mais les journaux ne doivent plus appartenir à des sociétés commerciales, dont l’intérêt est de gérer ces journaux comme des marques. L’éthique scientifique n’est pas compatible avec l’éthique commerciale.

Comment faire ? En réalité c'est assez évident. Il s’agit pour les pouvoirs publics d’annuler la totalité des abonnements aux éditeurs commerciaux et de cesser de payer des droits de publication à ces éditeurs. De nos jours, il n’est pas difficile d’avoir accès à la littérature scientifique sans passer par les journaux (par les prépublications ou ‘preprints’ ou simplement en écrivant aux auteurs qui sont généralement ravis que l’on s’intéresse à leurs travaux). L’argent économisé peut être réinvesti en partie dans l’édition scientifique non commerciale.

  1. Le mythe de la revue par les pairs

Je veux maintenant en venir à une question d’épistémologie plus subtile mais fondamentale. Quel est au fond le problème des revues prédatrices ? Clairement, il y a le gaspillage d’argent public. Mais l’article du Monde pointe également des problèmes scientifiques, à savoir le fait que de fausses informations sont propagées, sans avoir été vérifiées. L’éditorial parle en effet de ‘la sacro-sainte « revue par les pairs »’, qui n’est pas effectuée par ces revues. Mais est-ce vraiment le problème fondamental ?

L’idée que ce qui fait la valeur d’un article scientifique est qu’il a été validé par la relecture par les pairs avant publication est un mythe tenace mais néanmoins erroné. Cela est faux d’un point de vue empirique, et d’un point de vue théorique.

D’un point de vue empirique, à tout instant, il existe dans la littérature des conclusions contradictoires à propos d’un grand nombre de sujets, publiées dans des revues traditionnelles. Les cas de fraude récents concernent des articles qui ont pourtant subi une relecture par les pairs. Mais c’est le cas aussi d’une quantité beaucoup plus importantes d’articles non frauduleux, mais dont les conclusions ont été contestées par la suite. L’histoire des sciences est remplie de théories scientifiques contradictoires et coexistantes, d’âpres débats entre scientifiques. Ces débats ont lieu, justement, après publication, et le consensus scientifique se forme généralement assez lentement, pratiquement jamais sur la base d’un seul article (voir par exemple Imre Lakatos en philosophie des sciences, ou Thomas Kuhn). Par ailleurs, les résultats scientifiques sont également souvent diffusés dans la communauté scientifique avant publication formelle ; c’est le cas aujourd’hui avec les prépublications (« preprints » en ligne), mais c’était déjà partiellement le cas auparavant avec les conférences. L’article publié reste la référence parce qu’il fournit des détails précis, notamment méthodologiques, mais la contribution des relecteurs sollicités par les journaux n’est dans la plupart des cas pas essentielle, d’autant que celle-ci n’est généralement pas rendue publique.

D’un point de vue théorique, il n’y a aucune raison que la relecture par les pairs « valide » un résultat scientifique. Il n’y a rien de magique dans la revue par les pairs : simplement deux, parfois trois scientifiques donnent leur avis éclairé sur le manuscrit. Ces scientifiques ne sont pas plus experts que ceux qui vont lire l’article lorsqu’il sera publié (je parle bien sûr de la communauté scientifique et pas du grand public). Le fait qu’un article soit publié dans un journal ne dit pas grand chose en soi de la réception des résultats par la communauté ; lorsqu’un article est rejeté d’un journal, il est resoumis ailleurs. La publication finale n’atteste absolument pas d’un consensus scientifique. Par ailleurs, lorsqu’il s’agit d’études empiriques, les relecteurs n’ont pas en réalité la possibilité de vérifier les résultats, et notamment de vérifier s’il n’y a pas eu de fraude. Tout ce qu’ils peuvent faire, c’est vérifier que les méthodes employées semblent appropriées, et que les interprétations semblent sensées (deux points souvent sujets à débat). Pour valider les résultats (mais pas les interprétations), il faudrait au minimum pouvoir refaire les expériences en question, ce qui suppose le temps et l’équipement nécessaire. Ce travail indispensable est fait (ou tenté), mais il n’est pas fait au moment de la publication, ni commissionné par le journal. Il est fait après publication par la communauté scientifique. Le travail de « vérification » (mot inapproprié car il n’y a pas de vérité absolue en science, ce qui la distingue justement de la religion) est le travail de fond de la communauté scientifique, ce n’est pas le travail ponctuel du journal.

C’est cette idée reçue qu’il faut déconstruire : que le travail de revue interne au journal « valide » d’une certaine manière les résultats scientifiques. Ce n’est pas le cas, cela n’a jamais été le cas, et cela ne peut pas être le cas. La validation scientifique est la nature même de l’entreprise scientifique, qui est un travail collectif et de longue haleine. On ne peut pas lire un article et conclure « c’est vrai »; il faut pour cela l’intégrer dans un ensemble de connaissances scientifiques, confronter l’interprétation à des points de vue différents (car toute interprétation requiert un cadre théorique).

C’est justement cette idée reçue que les journaux prestigieux tentent au contraire de consolider. Il faut y résister. L’antidote est de rendre public et transparent le débat scientifique, qui actuellement reste souvent confiné aux couloirs des laboratoires et des conférences. On prétend que la relecture par les pairs valide les résultats scientifiques, mais ces rapports ne sont la plupart du temps pas publiés ; et quid des rapports non publiés lorsque l’article est rejeté par un journal ? Comment savoir alors ce qu’en pense la communauté ? Il faut au contraire rendre public le débat scientifique. C’est par exemple l’ambition de sites comme PubPeer, qui a mis à jour un certain nombre de fraudes, mais qui peut être utilisé simplement pour le débat scientifique de manière générale. Plutôt que de conditionner la publication à un accord confidentiel de scientifiques anonymes, il faut au contraire inverser ce système : publier l’article (c’est en fait déjà le cas par la prépublication), puis solliciter les avis de la communauté, qui seront également publiés, argumentés, discutés par les auteurs et le reste de la communauté. C’est ainsi que les scientifiques, mais également le plus grand public, pourront obtenir un vision plus juste de la valeur scientifique des articles publiés. La revue par les pairs est un principe fondamental de la science, oui, mais pas celle effectuée dans la confidence par les journaux, celle au contraire effectuée au grand jour et sans limite de temps par la communauté scientifique.

What is computational neuroscience? (XXXII) The problem of biological measurement (2)

In the previous post, I have pointed out differences between biological sensing and physical measurement. A direct consequence is that it is not so straightforward to apply the framework of control theory to biological systems. At the level of behavior, it seems clear that animal behavior involves control; it is quite documented in the case of motor control. But this is the perspective of an external observer: the target value, the actual value and the error criterion are identified with physical measurements by an external observer. But how does the organism achieve this control, from its own perspective?

What the organism does not do, at least not directly, is measure the physical dimension and compare it to a target value. Rather, the biological system is influenced by the physical signal and reacts in a way that makes the physical dimension closer to a target value. How? I do not have a definite answer to this question, but I will explore a few possibilities.

Let us first explore a conventional possibility. The sensory neuron encodes the sensory input (eg muscle stretch) in some way; the control system decodes it, and then compares it to a target value. So for example, let us say that the sensory neuron is an integrate-and-fire neuron. If the input is constant, then the interspike interval can be mapped back to the input value. If the input is not constant, it is more complicated but estimates are possible. There are various studies relevant to this problem (for example Lazar (2004); see also the work of Sophie Denève, e.g. 2013). But all these solutions require knowing quite precisely how the input has been encoded. Suppose for example that the sensory neuron adapts with some time constant. Then the decoder needs somehow to de-adapt. But to do it correctly, one needs to know the time constant accurately enough, otherwise biases are introduced. If we consider that the encoder itself learns, e.g. by adapting to signal statistics (as in the efficient coding hypothesis), then the properties of the encoder must be considered unknown by the decoder.

Can the decoder learn to decode the sensory spikes? The problem is it does not have access to the original signal. The key question then is: what could the error criterion be? If the system has no access to the original signal but only streams of spikes, then how could it evaluate an error? One idea is to make an assumption about some properties of the original signal. One could for example assume that the original signal varies slowly, in contrast with the spike train, which is a highly fluctuating signal. Thus we may look for a slow reconstruction of the signal from the spike train; this is in essence the idea of slow feature analysis. But the original signal might not be slowly fluctuating, as it is influenced by the actions of the controller, so it is not clear that this criterion will work.

Thus it is not so easy to think of a control system which would decode the sensory neuron activity into the original signal so as to compare it to a target value. But beyond this technical issue (how to learn the decoder), there is a more fundamental question: why splitting the work into two units (encoder/decoder), if the function of the second one is essentially to undo the work of the first one?

An alternative is to examine the system as a whole. We consider the physical system (environment), the sensory neuron, the actuator, and the interneurons (corresponding to the control system). Instead of seeing the sensory neuron as involved in an act of measurement and communication and the interneurons as involved in an act of interpretation and command, we see the entire system as a distributed dynamical system with a number of structural parameters. In terms of dynamical systems (rather than control), the question becomes: is the target value for the physical dimension an attractive fixed point of this system, or more generally, is there such a fixed point? (as opposed to fluctuations) We can then derive complementary questions:

  • robustness: is the fixed point robust to perturbations, for example changes in properties of the sensor, actuator or environment?
  • optimality: are there ways to adjust the structure of the system so that the firing rate is minimized (for example)?
  • control: can we change the fixed point by an intervention on this system? (e.g. on the interneurons)

Thus, the problem becomes one of designing a spiking system that has an attractive fixed point in the physical dimension, with some desirable properties. Framing the problem in this way does not necessarily require that the physical dimension is explicitly extracted (“decoded”) from the activity of the sensory neuron. If we look at such a system, we might not be able to identify in any of the neurons a quantity that corresponds to the physical signal, or to the target value. Rather, physical signal and target value are to be found in the physical environment, and it is a property of the coupled dynamical system (neurons-environment) that the physical signal tends to approach the target value.

What is computational neuroscience? (XXX) Is the brain a computer?

It is sometimes stated as an obvious fact that the brain carries out computations. Computational neuroscientists sometimes see themselves as looking for the algorithms of the brain. Is it true that the brain implements algorithms? My point here is not to answer this question, but rather to show that the answer is not self-evident, and that it can only be true (if at all) at a fairly abstract level.

One line of argumentation is that models of the brain that we find in computational neuroscience (neural network models) are algorithmic in nature, since we simulate them on computers. And wouldn’t it be a sort of vitalistic claim that neural networks cannot be (in principle) simulated on computer?

There is an important confusion in this argument. At a low level, neural networks are modelled biophysically as dynamical systems, in which the temporality corresponds to the actual temporality of the real world (as opposed to the discrete temporality of algorithms). Mathematically, those are typically differential equations, possibly hybrid systems (i.e. coupled by timed pulses), in which time is a continuous variable. Those models can of course be simulated on computer using discretization schemes. For example, we choose a time step and compute the state of the network at time t+dt, from the state at time t. This algorithm, however, implements a simulation of the model; it is not the model that implements the algorithm. The discretization is nowhere to be found in the model. The model itself, being a continuous time dynamical system, is not algorithmic in nature. It is not described as a discrete sequence of operations; it is only the simulation of the model that is algorithmic, and different algorithms can simulate the same model.

If we put this confusion aside, then the claim that neural networks implement algorithms becomes not that obvious. It means that trajectories of the dynamical system can be mapped to the discrete flow of an algorithm. This requires: 1) to identify states with representations of some variables (for example stimulus properties, symbols); 2) to identify trajectories from one state to another as specific operations. In addition to that, for the algorithmic view to be of any use, there should be a sequence of operations, not just one operation (ie, describing the output as a function of the input is not an algorithmic description).

A key difficulty in this identification is temporality: the state of the dynamical system changes continuously, so how can this be mapped to discrete operations? A typical approach is neuroscience is to consider not states but properties of trajectories. For example, one would consider the average firing rate in a population of neurons in a given time window, and the rate of another population in another time window. The relation between these two rates in the context of an experiment would define an operation. As stated above, a sequence of such relations should be identified in order to qualify as an algorithm. But this mapping seems only possible within a feedforward flow; coupling poses a greater challenge for an algorithmic description. No known nervous system, however, has a feedforward connectome.

I am not claiming here that the function of the brain (or mind) cannot possibly be described algorithmically. Probably some of it can be. My point is rather that a dynamical system is not generically algorithmic. A control system, for example, is typically not algorithmic (see the detailed example of Tim van Gelder, What might cognition be if not computation?). Thus a neural dynamical system can only be seen as an algorithm at a fairly abstract level, which can probably address only a restricted subset of its function. It could be that control, which also attaches function to dynamical systems, is a more adequate metaphor of brain function than computation. Is the brain a computer? Given the rather narrow application of the algorithmic view, the reasonable answer should be: quite clearly not (maybe part of cognition could be seen as computation, but not brain function generally).

Draft of chapter 6, Spike initiation with an initial segment

I have just uploaded an incomplete draft of chapter 6, "Spike initiation with an initial segment". This chapter deals with how spikes are initiated in most vertebrate neurons (and also some invertebrate neurons), where there is a hotspot of excitability close to a large soma. This situation has a number of interesting implications which make spike initiation quite different from the situation investigated by Hodgkin and Huxley, that of stimulating the middle of an axon. Most of the chapter describes the theory that I have developed to analyze this situation, called "resistive coupling theory" because the axonal hotspot is resistively coupled to the soma.

The chapter is currently unfinished, because a few points require a little more research, which we have not finished. The presentation is also a bit more technical than I would like, so this is really a draft. I wanted nonetheless to release it now, as I have not uploaded a chapter for a while and it could be some time before the chapter is finished.

What is computational neuroscience? (XXVIII)The Bayesian brain

Our sensors give us an incomplete, noisy, and indirect information about the world. For example, estimating the location of a sound source is difficult because in natural contexts, the sound of interest is corrupted by other sound sources, reflections, etc. Thus it is not possible to know the position of the source with certainty. The ‘Bayesian coding hypothesis’ (Knill & Pouget, 2014) postulates that the brain represents not the most likely position, but the entire probability distribution of the position. It then uses those distributions to do Bayesian inference, for example, when combining different sources of information (say, auditory and visual). This would allow the brain to optimally infer the most likely position. There is indeed some evidence for optimal inference in psychophysical experiments – although there is also some contradicting evidence (Rahnev & Denison, 2018).

The idea has some appeal. The problem is that, by framing perception as a statistical inference problem, it focuses on the most trivial type of uncertainty, statistical uncertainty. It is illustrated by the following quote: “The fundamental concept behind the Bayesian approach to perceptual computations is that the information provided by a set of sensory data about the world is represented by a conditional probability density function over the set of unknown variables”. Implicit in this representation is a particular model, for which variables are defined. Typically, one model describes a particular experimental situation. For example, the model would describe the distribution of auditory cues associated with the position of the sound source. Another situation would be described by a different model, for example one with two sound sources would require a model with two variables. Or if the listening environment is a room and the size of that room might vary, then we would need a model with the dimensions of the room as variables. In any of these cases where we have identified and fixed parametric sources of variation, then the Bayesian approach works fine, because we are indeed facing a problem of statistical inference. But that framework doesn’t fit any real life situation. In real life, perceptual scenes have variable structure, which corresponds to the model in statistical inference (there is one source, or two sources, we are in a room, the second source comes from the window, etc). The perceptual problem is therefore not just to infer the parameters of the model (dimensions of the room etc), but also the model itself, its structure. Thus, it is not possible in general to represent an auditory scene by a probability distribution on a set of parameters, because the very notion of a parameter already assumes that the structure of the scene is known and fixed.

Inferring parameters for a known statistical model is relatively easy. What is really difficult, and is still challenging for machine learning algorithms today, is to identify the structure of a perceptual scene, what constitutes an object (object formation), how objects are related to each other (scene analysis). These fundamental perceptual processes do not exist in the Bayesian brain. This touches on two very different types of uncertainty: statistical uncertainty, variations that can be interpreted and expected in the framework of a model; and epistemic uncertainty,  the model is unknown (the difference has been famously explained by Donald Rumsfeld).

Thus, the “Bayesian brain” idea addresses an interesting problem (statistical inference), but it trivializes the problem of perception, by missing the fact that the real challenge is epistemic uncertainty (building a perceptual model), not statistical uncertainty (tuning the parameters): the world is not noisy, it is complex.

What does Gödel's theorem mean ?

Gödel's theorem is a result in mathematical logic, which is often stated as showing that « there are true things that cannot proved ». It is sometimes used to comment on the limits of science, or the superiority of human intuition. Here I want to clarify what this theorem means and what the epistemological implications are.

First, this phrasing is rather misleading. It makes the result sound almost mystical. If you phrase the result differently, by avoiding the potentially confusing reference to truth, the result is not that mystical anymore. Here is how I would phrase it : you can always add an independent axiom to a finite system of axioms. This is not an obvious mathematical result, but I wouldn't think it defies intuition.

Why is this equivalent to the first phrasing ? If the additional axiom is independent of the set of axioms, then it cannot be proved from them (by definition). Yet as a logical proposition it has to be either true or not true. So it is true, or its negation is true, but it cannot be proved. What is misleading in the first phrasing is that the statement « there are true things » is contextual. I can start from a set of axioms and add one, and that new one will be true (since it's an axiom). Instead I could add its negation, and then that one will be true. That the proposition is true is not a universal truth, as it would seem with the phrasing « there are true things ». It is true in a particular mathematical world, and you can consider another one where it is not true. Famous examples are Euclidean and non-Euclidean geometries, which are mutually inconsistent sets of axioms.

So, what Gödel's theorem says is simply that no finite system of axioms is complete, in the sense that you can always add one without making the system inconsistent.

What are the epistemological implications ? It does not mean that there are things that science cannot prove. Laws of physics are not proved by deduction anyway. They are hypothesized and empirically tested, and all laws are provisory. Nevertheless, it does raise some deep philosophical questions, which have to do with reductionism. I am generally critical of reductionism, but more specifically of methodological reductionism, the idea that a system can be understood by understanding the elements that compose it. For example : understand neurons and you will understand the brain. I think this view is wrong, because it is the relations between neurons, at the scale of the organism, which make a brain. The right approach is systemic rather than reductionist. Many scientists frown at criticisms of reductionism, but this is only because they confuse methodological and ontological reductionism. Ontological reductionism means that reality can be reduced to a small number of types of things (eg atoms) and laws, and everything can be understood in these terms. For example, the mind can in principle be understood in terms of interactions of atoms that constitute the brain. Most scientists seem to believe in ontological reductionism.

Let us go back now to Gödel's theorem. An interesting remark made by theoretical biologist Robert Rosen is that Gödel's theorem makes ontological reductionism implausible to him. Why ? The theorem says that, whatever system of axioms you choose, it will always be possible to add one which is independent. Let us say we have agreed on a small set of fundamental physical laws, with strong empirical support. To establish each law, we postulate it and test it empirically. At a macroscopic level, scientists postulate and test all sorts of laws. How can we claim that any macroscopic law necessarily derives from the small set of fundamental laws ? Gödel's theorem says that there are laws that you can express but that are independent of the fundamental laws. This means that there are laws that can only be established empirically, not formally, in fact just like the set of fundamental laws. Of course it could be the case that most of what matters to us is captured by a small of set of laws. But maybe not.

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).

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.

New chapter : Excitability of an isopotential membrane

I have just uploaded a new chapter of my book on the theory of action potentials: Excitability of an isopotential membrane. In this chapter, I look mostly at the concept of spike threshold: the different ways to define it, its quantitative relation to different biophysical parameters (eg properties of sodium channels), and the conditions for its existence (eg a sufficient number of channels). This is closely related to my previous work on the threshold equation (Platkiewicz and Brette, 2010). It also contains some unpublished work (in particular updates of the threshold equation).

I am planning to extend this chapter with:

  • A few Brian notebooks.
  • A section on excitability types (Hodgkin classification).
  • Some experimental confirmations of the threshold equation that are under way (you will see in section 4.4.2 that current published experimental data do not allow precise testing of the theory).

I am now planning to work on the chapter on action potential propagation.

All comments are welcome.