One phrase that occasionally pops up when speaking of the goal of computational neuroscience is “reverse engineering the brain”. This is quite an interesting phrase from an epistemological point of view. The analogy is to see the brain as an engineered device, the “engineer” being evolution, of which we do not possess the design plans. We are supposed to understand it by opening it, and trying to guess what mechanisms are at play.

What is interesting is that observing and trying to understand the mechanisms is basically what science is about, not only neuroscience, so there must be something else in this analogy. For example, we would not describe the goal of astronomy as reverse engineering the planets. What is implied in the phrase is the notion that there is a plan, and that this plan is meant to achieve a function. It is a reference to the teleonomic nature of life in general, and of the nervous system in particular: the brain is not just a soup of neurons, these neurons coordinate their action so as to achieve some function (to survive, to reproduce, etc).

So the analogy is meaningful from this point of view, but as any analogy it has its limits. Is there no difference between a living being and an engineered artifact? This question points at what is life, which is a very broad question, but here I will just focus on two differences that I think are relevant for the present matter.

There is one very important specificity that was well explained by the philosopher Humberto Maturana (“The Organization of the living”, 1974). Engineered things have a structure that is designed so as to fulfill some function, that is, they are made of specific components that have to be arranged in a specific way, according to a plan. So all you need to understand is the structure, and its relation with the function. But as Maturana pointed out, living things have a structure (the body, the wiring of neurons, etc) but they also have an organization that produces that structure. The organization is a set of processes that produce the structure, which is itself responsible for the organization. But what defines the living being is its organization, not its structure, which can change. In the case of the nervous system, the wiring between neurons changes dramatically in the course of life, or even in the course of one hour, and the living being remains the same. The function of the organization is to maintain the conditions for its existence, and since it exists in a body interacting with an external environment, it is in fact necessary that the structure changes so as to maintain the organization. This is what is usually termed “plasticity” or “learning”. Therefore living things are defined by their organization, while engineered things are defined by their structure.

This is one aspect in which the engineering analogy is weak, because it misses this important distinction. Another one is that an engineered thing is made by an engineer, that is, by someone external to the object. Therefore the function is defined with respect to an external point of view. The plan would typically include elements that are defined in terms of physics, concepts that can only be grasped and measured by some external observer with appropriate tools. But a living organism only has its own senses and ways of interacting with the environment to make sense of the world. This is true of the nervous system as a whole, but also of individual cells: a cell has ways of interacting with other cells and possibly with the outside world, but it does not have a global picture of the organism. For example, an engineer plan would specify where each component should go, e.g. with Euclidian coordinates. But this is not how development can work in a living thing. Instead, the plan should come in the form of mechanisms that specify not “where” a thing is, but rather “how to get there”, or perhaps even when a component should transform into a new component – specific ways of interacting that end up in the desired result.

Therefore the nature of the “plan” is really quite different from the plan of an engineer. To make my point, I will draw an analogy with philosophy of knowledge. A plan is a form of knowledge, or at least it includes some knowledge. For example, if the plan includes the statement “part A should be placed at such coordinates”, then there is an implicit knowledge on part of the organism that executes the plan about Euclidian geometry. For an engineer, knowledge comes from physics, and is based on the use of specific tools to measure things in the world. But for a cell, knowledge about the world comes just from the interaction with the world: different ways to sense it (e.g. incoming spikes for a neuron), different ways to act on it (e.g. producing a spike, releasing some molecules in the extracellular medium). A plan can be specified in terms of physics if it is to be executed by an engineer, but it cannot be specified in these terms if it is to be executed by a cell: instead, it would be specified in terms of mechanisms that make sense given the ways the cell can interact with the world. Implicit knowledge about the world that is included in an engineer plan is what I could call “metaphysical knowledge”, in relationship with the corresponding notion in philosophy of science.

Science is made of universal statements, such as the law of gravitation. But not all statements are scientific, for example “there is a God”. In philosophy of science, Karl Popper proposed that a scientific statement is one that can potentially be falsified by an observation, whereas a metaphysical statement is a statement that cannot be falsified. For example, the statement “all penguins are black” is scientific, because I could imagine that one day I see a white penguin. On the other hand, the statement “there is a God” is metaphysical, because there is no way I can check. Closer to the matter of this text, the statement “the world is actually five-dimensional but we live in a three-dimensional subspace” is also metaphysical because independently of whether it is true or not, we have no way to confirm it or to falsify it given the way we interact with the world.

So what I call “metaphysical knowledge” in an engineer plan is knowledge that cannot be corroborated or falsified by the organism that executes the plan, given its senses and possibilities for action. For example, consider the following statement: neurons in the lateral geniculate nucleus project to the occipital region of the brain. This includes metaphysical knowledge about where that region is, which is specified from the point of view of an external observer. This cannot be a biological plan. Instead, a biological plan would rather have to specify what kind of interaction a growing axon should have with its environment in order to end up in the desired region.

In summary, although the phrase “reverse engineering” acknowledges the fact that, contrary to physical things of nature such as planets, living things have a function, it misses several important specificities of life. One is that living things are defined by their organization, rather than by the changing structure that the organization produces, while engineered things are defined by their structure. Another one is that the “plan”, which defines that organization, is of a very different nature than the plan made by and for an engineer, because in the latter case the function and the design are conceived from an external point of view, which generally includes “metaphysical knowledge”, i.e., knowledge that cannot be grasped from the perspective of the organism.

Computational neuroscience is not only about making theories. A large part of the field is also about simulations of neural models on a computer. In fact, there is little theoretical work in neuroscience that does not involve simulations at some stage. The epistemological status of simulations is quite interesting, and studies about it in philosophy of knowledge are quite recent. There is for example the work of Eric Winsberg, but I believe it mostly addresses questions related to physics. In particular, he starts one of his most cited papers (“Simulations, models and theories”, 2001) by stating: “I will be talking about the use of computers for modeling very complex physical phenomena for which there already exist good, well-understood theories of the processes underlying the phenomena in question”. This is an important distinction, and I will come back to it.

What is interesting about simulations from an epistemological viewpoint is that from a strictly Popperian viewpoint, simulation is useless. Indeed it looks like a sort of experiment, but there is no interaction with the world. It starts from a theory and a set of factual statements, and derives another set of factual statements. It is neither the making of a theory (no universal statement is produced), nor the test of a theory. So why is it that simulation is used so broadly?

In fact there are different types of simulation work. Broadly speaking, we may think of two categories: theory-driven simulations, and data-driven simulations.

I will start with theory-driven simulations. There are in fact two different motivations to use simulations in theoretical work. One is exploratory: simulations are used in the process of making theories, because the models are so complex so that it may be difficult to predict their behavior. This is a general problem with so-called complex systems. Simulations are then used for example to explore the effect of various parameters on the behavior of the model, or to see whether some property can appear given a set of rules, etc. Another motivation is to test a theory. Now this may seem odd since we are not speaking of an empirical test. First of all, this apparent oddity perhaps stems from the myth that theoretical work is essentially about making logical deductions from initial statements. But in reality, especially in biology where models can be very complex, theoretical work almost invariably involves some guess work, approximations, and sometimes vaguely justified intuitions. Therefore, it makes sense to check the validity of these approximations in a number of scenarios. For example, in my paper with Jonathan Platkiewicz about the spike threshold, we derived an equation for the spike threshold from the Hodgkin-Huxley equations. It involved approximations of the sodium current, and we also developed the theory in an isopotential neuron. Therefore in that paper, we checked the theory against the numerical simulation of a complex multicompartmental neuron model, and it was not obvious that it would work.

There is another motivation, which is more specific to computational neuroscience. Theories in this field are about how the interaction of neurons produces behavior, or in other words, about linking physiology, at the neuron level, and function, at the systems or organism level. But to speak of function, one needs an environment. This external element is not part of the neural model, yet it is critical to the relevance of the model. Theories generally do not include explicit models of the environment, or only simplistic versions. For example, in my paper about sound localization with Dan Goodman, we proposed a mechanism by which selective synchrony occurs when a sound is presented at specific locations, leading to a simple spiking model that can accurately estimation the location of a sound source in the presence of realistic diffraction properties. In principle it works perfectly, but of course in a real environment the acoustical signals are unknown, but not arbitrary, they may have a limited spectrum, there may be noise, diffraction properties are also unknown but not arbitrary, there may be ambiguities (e.g. the cones of confusion), etc. For this reason, the model needed to be implemented and its performance tested, which we did with recorded sounds, measured acoustical filters and acoustical noise. Thus it appears that even for theory-driven work, simulation is unavoidable because the theory applies to the interaction with an unknown, complex environment. In fact, ideally, models should be simulated, embodied (in a robot) and allowed to interact with a real (non simulated) environment. Since theories in computational neuroscience claim to link physiology and function, this would be the kind of empirical work required to substantiate such claims.

The other type of simulation work is data-driven. I believe this is usually what is meant by “simulation-based science”. In this kind work, there is little specific theory – that is, only established theories are used, such as cable equation theory. Instead, models are built based on measurements. The simulations are then truly used as a kind of experiment, to observe what might emerge from the complex interaction of neuron models. It is sometimes said that simulations are used to do “virtual experiments” when the actual experiments would be impractical. Another typical use is to test the behavior of a complex model when parameters are varied in a range that is considered plausible.

In physics, such computer simulations are also used, for example to simulate the explosion of a nuclear bomb. But as Winsberg noted, there is a very important epistemological distinction between simulations in physics and in biology: in the former, there is an extremely detailed knowledge of both the laws that govern the underlying processes and of the arrangement of the individual elements in the simulations. Note that even in this case, the value of such simulations is controversial. But in the case of biology and especially neuroscience, the situation is quite different. It is in fact acknowledged by the typical use cases mentioned above.

Consider the statement that a simulation is used to perform a “virtual experiment” when actual experiments are impractical. This seems similar to the simulation of a nuclear explosion. In that case, one is interested in the large scale behavior of the system, and at such a large scale the experiment is difficult to do. But in neuroscience, the situation is exactly the opposite. The experiment with a full organism is actually what is easy to do (or at least feasible), it is a behavioral experiment. So simulations are not used to observe how an animal behaves. They are used to observe the microstructure of the system. But then this means that this microstructure was not known at the time when the model was built, and so these properties that are to be observed are considered as sufficiently constrained by the initial set of measurements to be derived from them.

The second, and generally complementary, use case is to simulate the model while varying a number of parameters so as to find the viable region in which the model produces results consistent with some higher-order measurements (for example, local field potentials). If the parameters are varied, then this means they are actually not known with great certainty. Thus it is clear that biophysical models based on measurements are in fact much less reliable than physical models such as those of nuclear explosions.

One source of uncertainty is the values of parameters in the models, for example channel densities. This is already one great problem. Probably the biggest issue here is not so much the uncertainty about parameters, which is an issue in models of all fields, but the fact the parameters are most likely not independent, i.e., they covary in a given cell or between cells. This lack of independence comes from the fact that the model is of a living thing, and in a living thing all components and processes contribute to the function of the organism, which implies tight relations between them. The study of these relations is a defining part of biology as a field, but if a model does not explicitly include these relations, then it would seem extraordinary that proper function can arise without them, given that they are hidden under the uncertainty in the parameters. For example, consider action potential generation. Sodium channels are responsible for initiation, potassium channels for repolarization. There are a number of recent studies showing that their properties and densities are precisely tuned with respect to each other so that energy consumption is minimized: indeed energy is lost if they are simultaneously open because they have opposite effects. If this functional relation were unknown and only channel densities were known within some range, then the coordination would go unnoticed and a naive model simply using independent values from these distributions would display inefficient action potential generation, unlike real neurons.

I will try to summarize the above point. Such simulations are based on the assumption that the laws that govern the underlying processes are very well understood. This may well be true for the laws of neural electricity (cable equations, Hodgkin-Huxley equations). However, in biology in general and in neuroscience in particular, the relevant laws are also those that describe the relations between the different elements of the model. This is a completely different set of laws. For the example of action potential generation, the laws are related to the co-expression of channels, which is more related to the molecular machinery of the cell than to its electrical properties.

Now these laws, which relate to the molecular and genetic machinery, are certainly not so well known. And yet, they are more relevant to what defines a living thing than those describing the propagation of electrical activity, since indeed these are the laws that maintain the structure that maintain the cells alive. Thus, models based on measurements attempt to reproduce biological function without capturing the logics of the living, and this seems rather hopeful. There are also many examples in recent research that show that the knowledge we have of neural function is rather poor, compared to what is to be found. For example, glial cells (which make most of the cells in the brain) are now thought to play a much more important role in brain function than before, and these are generally ignored in models. Another example is in action potential initiation. Detailed biophysical models are based on morphological reconstructions of the axon, but in fact in the axon initial segment, there is also a scaffold that presumably alters the electrical properties along the axon (for example the axial resistivity should be higher).

All these remarks are meant to point out that in fact, it is illusory to think that there are, or will be in the near future, realistic models of neural networks based on measurements. What is worse, such models seem to miss a critical point in the study of living systems: these are not defined only by their structure (values of parameters, shape of cells) but by processes to maintain that structure and produce function. To quote Maturana (1974), there is a difference between the structure (channel densities etc) and the organization, which is the set of processes that set up that structure, and it is the organization, not the structure, that defines a living thing. Epistemologically speaking, the idea that things not accessible to experiment can be simulated based on measurements that constrain a model is induction. But the predictive power of induction is rather limited when there is such uncertainty.

I do not want to sound as if I were entirely dismissing data-driven simulations. Such simulations can still be useful, as an exploratory tool. For example, one may simulate a neuron using measured channel densities and test whether the results are consistent with what the actual cell does. If they are not, then we know we are missing some important property. But it is wrong to claim that such models are more realistic because they are based on measurements. On one hand, they are based on empirical measurements, on the other hand, they are dismissing mechanisms (or “principles”), which is another empirical aspect to be accounted for in living things. I will come back in a later post to the notion of “realistic model”.