Archives de l’auteur : romain

Rate vs. timing (XVII) Analog vs. digital

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It is sometimes stated that rate-based computing is like analog computing while spike-based computing is like digital computing. The analogy comes from the fact, of course, that spikes are discrete whereas rates are continuous. But as any analogy, it has its limits. First of all, spikes are not discrete in the way digital numbers are discrete. In digital computing, the input is a stream of binary digits, coming one after another in a cadenced sequence. The digits are gathered by blocks, say of 16 or 32, to form words that stand for instructions or numbers. Let us examine these two facts with respect to spikes. Spikes do not arrive in a cadenced sequence. Spikes arrive at irregular times, and time is continuous, not digital. What was meant by digital is presumably that there can be a spike or there can be no spike, but there is nothing in between. However, given that there is also a continuous timing associated to the occurrence of a spike, a spike is better described as a timed event rather than as a binary digit. But of course one could decide to divide the time axis into small time bins, and associate a digit 0 when there is no spike and 1 when there is a spike. This is certainly true, but as one performs this process as finely as possible to approximate the real spike train, it appears that there are very few 1s drowned in a sea of 0s. This is what is meant by “event”: information is carried by the occurrence of 1s at specific times rather than by the specific combinations of 0s and 1s, as in digital computing. So in this sense, spike-based computing is not very similar to digital computing.

The second aspect of the analogy is that digits are gathered in words (of say 32 digits), and these words are assigned a meaning in terms of either an instruction or a number. Transposed to spikes, these “words” could be the temporal pattern of spikes of a single neuron, or perhaps more meaningfully a pattern of spikes across neurons, as in synchrony-based schemes, or across neurons and time, as in polychronization. Now there are two ways of understanding the analogy. Either a spike pattern stands for a number, and in this case the analogy is not very interesting, since this is pretty much saying that spikes implement an underlying continuous value, in other words this is the rate-based view of neural computation. Or a spike pattern stands for a symbol. This case is more interesting, and it may apply to some proposed spike-based schemes (like polychronization). It emphasizes the idea that unlike rate-based theories, spike-based theories are not (necessarily) related to usual mathematical notions of calculus (e.g. adding numbers), but possibly to more symbolic manipulations.

However, this does not apply to all spike-based theories. For example, in Sophie Denève’s spike-based theory of inference (which I will describe in a future post), spike-based computation actually implements some form of calculus. But in her theory, analog signals are reconstructed from spikes, in the same way as the membrane potential results from the action of incoming spikes, rather than the other way around as in rate-based theories (i.e., a rate description is postulated, then spikes are randomly produced to implement that description). So in this case the theory describes some form of calculus, but based on timed events.

This brings me to the fact that neurons do not always interact with spikes. For example, in the retina, there are many neurons that do not spike. There are also gap junctions, in which the membrane potentials of several neurons directly interact. There are also ephaptic interactions (through the extracellular field potential). There is also evidence that the shape of action potentials can influence downstream synapses (see a recent review by Dominique Debanne). In these cases, we may speak of analog computation. But this does not bring us closer to rate-based theories. In fact, quite the opposite: rates are abstracted from spikes, and stereotypical spikes are an approximation of what really goes on, which may involve other physical quantities. The point here is that firing rate is not a physical quantity as the membrane potential, for example. It is an abstract variable. In this sense, spike-based theories, because they are based on actual biophysical quantities in neurons, might be closer to what we might call “analog descriptions” of computation than rate-based theories.

Complex representations and representations of complex things

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In a previous post, I noted that the concept of neural assembly is limited by the fact that it does not represent relations. But this means that it is not possible to represent in this way a complex thing such as a car or a face. This might seem odd since many authors claim that there are neurons or groups of neurons that code for faces (in IT). I believe there might be again some confusion between representation and information in Shannon’s sense. What is meant when it is stated that an assembly of neurons codes for a face is that its activity stands for the presence of a face in the visual field. So in this sense the complex thing, the face, is represented, but the representation itself is not complex. With such a concept of representation, complex things can only be represented by removing all complexity.

This is related to the problem of invariant representations. How is it that we can recognize a face under different viewpoints, lightning conditions, possibly changes in hair style and facial expressions? One answer is that there must be a representation that is invariant, i.e., a neural assembly that codes for the concept “Paul’s face” independently of the specific way it can appear. However, this is an incomplete answer, for when I see Paul’s face, I can recognize that it’s Paul, but I can also see that he smiles, that I’m looking at him from the side, that he tainted his hair in black. It’s not that by some process I have managed to remove all details that are not constituent of the identity of Paul’s face, but rather I am seeing everything that makes Paul’s face, both in the way it usually appears and in the specific way it appears this time. So the fact that we can recognize a complex thing in an invariant way does not mean that the complexity itself is discarded. In reality we can still register this complexity, and our mental representation of a complex thing is indeed complex. As I argued before, the concept of neural assembly is too crude to capture such complexity.

The concept of invariance is even more interesting when applied to categories of objects, for example a chair. In contrast with Paul’s face, different chairs are not just different viewpoints on the same physical object, they really are different physical objects. They can have different colors, widely different shapes and materials. They usually have four legs, but surely we would recognize a three-legged chair as such. What really makes a chair is that one can sit on it, have her back in contact with it. This is related to Gibson’s concept of “affordances”. Gibson argued that we perceive the affordances of things, i.e., the possibilities of interaction with things.

So now I could imagine that there is an assembly of neurons that codes for the category “chair”. This is fine, but this is only something that stands for the category, it does not describe what this category is. It is not the representation of an affordance. Representing it would involve representing the potential action that one could make with that object. I do not know what kind of neural representation would be adequate, but it would certainly be more complex (i.e., structured) than a neural assembly.

What is computational neuroscience? (IX) The epistemological status of simulations

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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”.

The villainous monster recursion

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In O’Regan’s paper about the sensorimotor theory of perception (O’Regan and Noë, BBS 2001), he uses the analogy of the “villainous monster”. I quote it in full:

“Imagine a team of engineers operating a remote-controlled underwater vessel exploring the remains of the Titanic, and imagine a villainous aquatic monster that has interfered with the control cable by mixing up the connections to and from the underwater cameras, sonar equipment, robot arms, actuators, and sensors. What appears on the many screens, lights, and dials, no longer makes any sense, and the actuators no longer have their usual functions. What can the engineers do to save the situation? By observing the structure of the changes on the control panel that occur when they press various buttons and levers, the engineers should be able to deduce which buttons control which kind of motion of the vehicle, and which lights correspond to information deriving from the sensors mounted outside the vessel, which indicators correspond to sensors on the vessel’s tentacles, and so on.”

It is meant here that all knowledge must come from the sensors and the effect of actions on them, because there is just no other source of knowledge. This point of view changes the computational problem of perception from inferring objective things about the physical world from the senses to finding relations between actions and sensor data.

This remark is not specific to the brain. It would apply whether the perceptual system is made of neurons or not – for example it could be an engineered piece of software for a robot. So what in fact is specific about the brain? The question is perhaps too broad, but I can at least name one specificity. The brain is made of neurons, and each neuron is a separate entity (with a membrane) that interacts with other neurons, which are relatively elementary (compared to the entire organism) and essentially identical (in the great lines). Each entity has sensors (dendrites) and can act by sending spikes through their axons (and also in other ways, but on a slower timescale). So in fact we could think of the villainous monster concept at different levels. The higher level is the organism, with sensors (photoreceptors) and actuators (muscle contraction). At a lower level, we could consider a brain structure, for example the hippocampus, and see it as a system with sensors (spiking inputs to the hippocampus) and actuators (spiking outputs). What can be said about the relationship between actions and sensor inputs? In fact, we could arbitrarily define a system by doing at graph cut in the connectivity graph of the brain. At the final level of analysis, we might analyze the neuron as a perceptual system, with a set of sensors (dendrites) and one possible action (to produce a spike). At this level, it may also be possible to define the same neuron as a different perceptual system by redefining the set of sensors and actions. For example, sensors could be a number of state variables, such as membrane potential at different points along the dendritic tree, calcium concentration, etc; actions could be changes in channel densities, in synaptic weights, etc. This is not completely crazy because in a way, these sensed properties and the effect of cellular actions are all that the cell can ever know about the “outside world”.

One might call this conceptual framework the “villainous monster recursion”. I am not sure where it could lead, but it seems intriguing enough to think about it!

On the notion of information in neuroscience

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In a previous post, I criticized the notion of “neural code”. One of my main points was that information can only make sense in conjunction with a particular observer. I am certainly not the first one to make this remark: for example, it is presented in a highly cited review by deCharms and Zador (2000). More recently Buzsaki defended this point of view in a review (Neuron 2010), and from the notes in the supplemental material, it appears that he is clearly more philosophically lucid than the average neuroscientist on these issues (check the first note). I want to come back on this issue in more detail.

When one speaks of information or code in neuroscience, it is generally meant in the sense of Shannon. This is a very specific notion of information coming from communication theory. There is an emitter who wants to transmit some message to a receiver. The message is transmitted in an altered form called “code”, for example Morse code, which contains “information” insofar as it can be “decoded” by the observer into the original message. The metaphor is generally carried to neuroscience in the following form: there are things in the external world that are described in some way by the experimenter, for example bars with a variable orientation, and the activity of the nervous system is seen as a “code” for this description. It may carry “information” about the orientation of the bar insofar one can reconstruct the orientation from the neural activity.

It is important to realize how limited this metaphor is, and indeed that it is a metaphor. In a communication channel, the two ends agree upon a code, for example on the correspondence between letters and Morse code. For the receiving end, the fact that the message is information in the common sense of the word relies on two things: 1) that the correspondence is known, 2) that the initial message itself makes sense for the receiver. For example, imagine a few centuries ago, someone is given a papyrus with ancient Egyptian hieroglyphs. Probably it will represent very little information for that person because she has no way to make sense of it. The papyrus becomes informative with the Rosetta stone, where the same text is written in ancient Egyptian and in ancient Greek, so that the papyrus can be translated to ancient Greek. But of course this becomes information only if ancient Greek makes sense for the person that reads it!

So the metaphor of a “neural code”, understood in Shannon’s sense, is problematic in two ways: 1) the experimenter and the nervous system obviously do not agree upon a code, and 2) how the original “message” makes sense for the nervous system is left entirely unspecified. I will give another example to make it clearer. Imagine you have a vintage thermometer (non-digital), but that thermometer does not have any graduation. You could replace the thermometer by the activity of a temperature-sensitive neuron. From the point of view of information theory, there is just as much information about temperature in the liquid level than if temperature were given as a number of Celsius degrees. But clearly for an observer, there is very little information because one does not know the relationship between the level of the liquid and the physical temperature, so it is essentially useless. Perhaps one could say that the level says something relative about temperature, that is, whether a temperature is hotter than another one. But even this is not true, because it relies on the prior knowledge that the level of the liquid increases when the temperature increases, a physical law that is not obvious at all. So to make sense of the liquid level, one would actually rely on association with other sources of information that are not given by the thermometer, e.g. that for some level one feels cold and that for another level one feels hot. But now this means that the information in the liquid level is actually limited (and in fact defined) not by the “communication channel” (how accurate the thermometer is) but by the external source of knowledge that provides meaning to the liquid level. This limitation comes from the fact that at no moment in time is the true temperature in Kelvin given as an objective truth to the observer. The only way it gets information is through its own sensors. This is why Shannon’s information is highly misleading as a metaphor for information in biological systems: there can be no agreed code between the environment and the organism. The organism has to learn ancient Egyptian just with hieroglyphs.

To finish with this example, imagine now that the thermometer is graduated, so you can read the temperature. Wouldn’t this provide the objective information that was previously missing? As a matter of fact, not really. For example, as a European, if I am given the temperature in Fahrenheit degrees, I have no idea whether it is hot or cold. So the situation is not different for me than previously. Of course if I am also given the correspondence between Fahrenheit and Celsius, then it will start making sense for me. But how can Celsius degrees make sense for me in the first place? Again these are just numbers with arbitrary units. Celsius degrees make sense because they can be related to physical processes linked with temperature: water freezes at 0° and boils at 100°. Presumably, the same thing applies to our perception of temperature: the body senses a change in firing rate of some temperature-sensitive neuron, and this becomes information about temperature because it can be associated with a number of biophysical processes linked with temperature, say sweating, and all these effects can be noticed. In fact, what this example shows is that the activity of the temperature-sensitive neuron does not provide information about physical temperature (number of Kelvin degrees), but rather about the occurrence of various other events that can be captured with other sensors. This set of relationships between events is, in a way, the definition of temperature for the organism, rather than some number in arbitrary units.

Let us summarize. In Shannon’s information theory, it is implicitly assumed that there are two ends in a communication channel, and that 1) both ends agree upon a code, i.e., a correspondence between descriptive elements of information on both ends, and that 2) the initial message on the emitter end makes sense for the observer at the other end. None of these two assumptions apply to a biological organism because there is only one end. All the information that it can ever get about the world comes from that end, and so in this context Shannon’s information only makes sense for an external observer who can see both ends. A typical error coming from the failure to realize this fact is to highly overestimate the information in neural activity about some experimental quantity. I discussed this specific point in detail in a recent paper. The overestimation comes simply from the fact that detailed knowledge about the experiment is implicitly assumed on behalf of the nervous system.

Followed to its logical conclusions, the information-processing line of reasoning leads to what Daniel Dennett called the “Cartesian theater”. If neural activity gives information about the world in Shannon’s sense, then this means that at some final point this neural activity has to be analyzed and related to the external world. Indeed if this does not happen, then we cannot be speaking about Shannon information, for there is no link with the initial message. So this means that there is some critical stage at which neural activity is interpreted in objective terms. As Dennett noted, this is conceptually not very far from the dualism of Descartes, who thought that there is a non-material mind that reads the activity of the nerves and interprets it in terms of the outside physical world. The “Cartesian theater” is the brain seen as a screen where the world is projected, that a homunculus (the mind) watches.

Most neuroscientists reject dualism, but if one is to reject dualism, then there must be no final stage at which the observer end of the communication channel (the senses) is put in relationship with the emitter end (the world). All information about the world must come from the senses, and the senses alone. Therefore, this “information” cannot be meant in Shannon’s sense.

This, I believe, is essentially what James Gibson meant when he criticized the information-processing view of cognition. It is also related to Hubert Dreyfus’s criticism of artificial intelligence. More recently, Kevin O’Regan made similar criticisms. In his most cited paper with Noë (O’Regan and Noë, BBS 2001), there is an illuminating analogy, the “villainous monster”. Imagine you are exploring the sea with an underwater vessel. But a villainous monster mixes all the cables and so all the sensors and actuators are now related to the external world in a new way. How can you know anything about the world? The only way is to analyze the structure of sensor data and their relationships with actions that you can perform. So if one rejects dualism, then this is the kind of information that is available to the nervous system. A salient feature of this notion of information is that, contrary to Shannon’s information, it is defined not as numbers but as relations or statements: if I do action A, then sensory property B happens; if sensory property A happens, then another property B will happen next; if I do action A in sensory context B, then C happens.

 

Philosophy of knowledge

We have concluded that, if dualism is to be rejected, then the right notion of information for a biological organism is in terms of statements. This makes the problem of perception quite similar to that 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 knowledge, 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 falsify it.

To come back to the matter of this text, I propose to qualify as metaphysical for an organism all knowledge that cannot be falsified, given the senses and possibilities for action. For example, in an experiment, one could relate the firing rate of a neuron with the orientation of a bar presented in front of the eyes. There is information in Shannon’s sense about the orientation in the firing rate. This means that we can “decode” the firing rate into the parameter “orientation”. However this decoding requires metaphysical knowledge because “orientation” is defined externally by the experimenter, it does not come out from the neuron’s activity itself. From the neuron’s point of view, there is no way to falsify the statement “10 Hz means horizontal bar”, because the notion of horizontal (or bar) is either defined in relation to something external to the neuron, or by its activity itself (horizontal is when the activity is 10 Hz) and in this latter case the statement is a tautology.

Therefore it appears that there can be very little information without metaphysical knowledge in the response of a single neuron, or in its input. Note that it is not completely empty, for there could be information about the future state of the neuron in the present state.

 

The structure of information and “neural assemblies”

When information is understood as statements rather than numbers to be decoded, it appears that information to be represented by the brain is much richer than implied by the usual notion inspired by Shannon’s communication theory. In particular, the problem of perception is not just to relate a vector of numbers (e.g. firing rates) to a particular set of parameters representing an object in the world. What is to be perceived is much richer than that. For example, in a visual scene, there could be Paul, a person I know, wearing a new sweater, sitting in a car. What is important here is that a scene is not just a “bag of objects”: objects have relationships with each other, and there are many possible different relationships. For example there is a car and there is Paul, and Paul is in a specific relationship with the car, that of “sitting in it”.

Unfortunately this does not fit well with the concept of “neural assemblies”, which is the mainstream assumption about how things we perceive are represented in the brain. If it is true that any given object is represented by the firing of a given assembly of neurons, then several objects should be represented by the firing of a bigger assembly of neurons, the union of all assemblies, one for each object. Several authors have noted that this may lead to the “superposition catastrophe”, i.e., there may be different sets of objects whose representations are fused into the same big assembly. But let us assume that this problem has somehow been solved and that there is no possible confusion. Still, the representation of a scene can be nothing else than an unstructured “bag of objects”, there are no relationships between objects in the assembly representation. One way to save the assembly concept is to consider that there are combination assemblies, which code for specific combinations of things, perhaps in a particular relationship. But this cannot work if it is the first time I see Paul in that sweater. There is a fundamental problem with the concept of neural assembly, which is that there is representation of relations, only of things to be related. In analogy with language, there is no syntax in the concept of neural assemblies. This is actually the analogy chosen by Buzsaki in his recent Neuron review (2010).

This remark, mostly made in the context of the binding problem, has led authors such as von der Malsburg to postulate that synchrony is used to bind the features of an object, as represented by neural firing. This avoids the superposition catastrophe because at a given time, only one object is represented by neural firing. It also addresses the problem of composition: by defining different timescales for synchrony, one may build representations for objects composed of parts, possibly in a recursive manner. However, the analogy of language shows that this is not going to be enough, because only one type of relation can be represented in this way. But the same analogy also shows that it is conceptually possible to represent structures as complex as linguistic structure by using time, in analogy with the flow of a sentence. Just for the sake of argument, and I do not mean that this is a plausible proposition (although it could be), you could imagine that assemblies can code either things (Paul, a car, a jumper) or relations between things (sitting, wearing), that only one assembly would be active at a time, and that the order of activation indicate which things a relation applies to. Here not only synchrony is important, but also the order of spikes. This idea is quite similar to Buszaki’s “neural syntax” (based on oscillations), but I would like to emphasize a point that I believe has not been noticed: that assemblies must stand not only for things but also for relations between things (note that “things” can also be thought of relations, and in this case we are speaking of relations of different orders).

All this discussion, of course, is only meant to save the concept of neural assembly and perhaps one might simply consider that a completely different concept should be looked for. I do not discard this more radical possibility. However, I note that if it is admitted that neurons interact mostly with spikes, then somehow the spatio-temporal pattern of spikes is the only way that information can be carried. Unless, perhaps, we are completely misled by the notion of “information”.

Outline of an existentialist theory of evolution

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In previous posts, I pointed out that there is a critical epistemological difference between the study of biological things and of physical things, due to the fact that living things have a fundamental “teleonomic project”. I described this project as “reproductive invariance”, following the words of Jacques Monod, which is simply to say that what characterizes a living being is that it can reproduce itself. I believe many scientists would propose this definition. However, it is not very satisfying. For example, isn’t a sterile individual alive? One might amend the definition by proposing that life can only be defined at the level of species, but this does not seem very satisfying. Mules for example, are generally infertile, but certainly they are living beings. As a thought experiment, we may imagine a species of immortal humans that do not reproduce. Certainly we would consider them alive too.

So even though reproduction is a salient characteristic of life, it might not be the best way to define it. Here is another possible definition: a living being is something with the teleonomic project of living. That is, a living being is a set of self-sustaining processes, subject to the forces of nature (this is similar to Varela’s autopoiesis). Thus an important feature of living beings is that they could die, which distinguish them from other stable forms of existence such as rocks. The fact that they can die is critical, because it implies that there are constraints on their conditions of existence. For example, when removed from its natural environment (for example put in the void), a living being dies, so a living being is contingent on a given environment. So let us say that a living being is a stable form of existence that requires some energy, specific processes and environmental conditions to sustain itself. This could be called the existentialist view of life.

Many things could be said about this definition (such as the notion of multicellular organism), but this was just to introduce a discussion about evolution. Life is a stable form of existence, but evolution means change. The question of evolution, then, is what changes in forms of existence can occur while yielding stable forms (i.e., not dying). Here I will not discuss evolution from an empirical point of view, but simply develop the existentialist perspective.

Evolution is often presented (at least in popular science writing) as an improvement process. Genetic algorithms follow this view: there is an objective criterion to be maximized, and by mutations and selective reproduction, the criterion increases over generations. One obvious problem is that there is an objective criterion, independent of the organism itself, and defined externally. But in reality, there is no other objective criterion than the existence of the organism, in the environment. The criterion for survival is defined jointly by the organism and its environment, which itself is made partly of living beings. For example the survival of carnivores is contingent on the existence of herbivores (the notion of ecosystem). If carnivores exist, then a criterion for existence of an herbivore is its ability to escape carnivores. This is not an externally defined criterion. The existences of various organisms are interrelated, and the existence of a specific organism is determined by its sustainability as a stable form in the environment (stable at some timescale). Therefore the notion of “fixed point” is a better mathematical analogy than optimization. Potential changes, either external (environment) or internal (mutations), lead either to quick death, or to a new existential fixed point.

Let us start with a fixed, stable environment. Imagine there are only unicellular organisms, and they do not reproduce. It is possible that some of them die, because of radiations for example. Those that do not die are (by definition) robust to these radiations. These cells, perhaps, would live for a very long time – let us say they live eternally. But suppose now that, by some accident, one cell is given the ability to reproduce itself. When this happens, the cell initially multiplies exponentially. But in turn, this implies that the environment for each cell changes as the cells multiply. In particular, since each cell requires energy and the energy supply is limited, the population cannot grow indefinitely. At that saturation point, resources start to get scarce and some cells die. All cells are equally affected by this process, both reproductive and non-reproductive ones. When cells die, there are more resources again and so cells that can reproduce themselves occupy this space. Soon enough, the eternal non-reproductive cells are replaced by short-lived reproductive cells, where reproduction only compensates for deaths. This saturation point is reached extremely fast, because growth is exponential. Thus, the living world evolves from a stable fixed point, the small population of eternal non-reproducing cells, to a new stable fixed point, the short-lived reproducing cells that saturate the environment. This evolution is very quick, and perhaps it can be described as “punctuated equilibria”, as proposed by Stephen Jay Gould.

What is interesting in this example is that the new cells are not replaced by “better ones”, in a teleonomic sense. But simply, non-reproductive cells cannot co-exist with reproductive cells.

Let us consider now the notion of “adaptation”. Assume that sometimes, reproduction is imperfect and the new cell is slightly different from the original cell (a “mutation”). It could be that the cell dies, or cannot reproduce, and then that mutation does not yield a stable form of existence. Or it could be that the cell has a higher chance of survival, or reproduces itself at a higher rate than the other cells. If this is so, this cell and its descendants will quickly occupy (at an exponential rate) the entire environment. It could be said that the “species” adapts to its environment. This is essentially Darwinism, which is compatible with the existentialist view.

What kind of mutations would occur in this situation? At this stage, one may speculate that most mutations lead to death. Therefore, the mutations that are more likely to be reproduced in the long run are those that 1) reduce the rate of mutations, 2) fix all sorts or mutations that would otherwise yield to death or lower reproduction rate, 3) preserve the said mutations from subsequent mutations. Note that there may actually be a trade-off in the rate of mutations, because mutations are (at least initially) necessary to yield the repair mechanisms. Thus cells with a higher rate of mutations would be more fragile but also reach the self-repairing stage faster. These remarks suggest that, in such a stable environment, the population of cells should evolve sophisticated repair mechanisms. They are sophisticated for two reasons: they affect all mechanisms important for the existence and reproduction of the cell, and they are recursive (they affect the repair mechanisms themselves). Thus it could be said that the primary mechanisms developed by cells are homeostatic mechanisms. One very important corollary is that, at this stage, most mutations should now yield functional changes rather than death or dysfunction. This makes the mutation process creative (or without effect) rather than destructive.

An interesting point is that, even though living organisms are defined as fixed points, the evolution process has a direction. That is, it is not possible to evolve “backwards” towards non-reproductive cells. The fundamental reason is that evolution occurred because the two forms of existence were mutually exclusive. That is, reproductive cells and non-reproductive cells cannot co-exist, except for a transient time, because reproductive cells almost immediately occupy the whole environment and alter the conditions of existence of the non-reproductive cells. It is not that reproductive cells are “better” in some teleonomic sense, but simply that the other cells cannot simultaneously exist with them. Note that it is also possible that evolution occurs without the new form of life disappearing – otherwise there would be no unicellular organisms anymore.

It could be opposed that the co-existence of A and B necessarily yielding to the disappearing of A and the existence of B defines an order on the set of living things, and therefore it would be actually right to claim B is “better” than A, with respect to that order relation. But in fact, this is not an order relation in the mathematical sense. First of all, there are things that can co-exist, i.e., that cannot be ordered with respect to each other. Thus, if it is an order, then it is only a partial order. But more importantly, an order is transitive, which means that if B is better than A and C is better than B, then C is better than A. This is certainly not true in general for life. For example, imagine that bacteria B produces substances that are toxic for organism A, so B is “better” than A. But then imagine that some mutation of B can make a new bacteria C that is more efficient than B (e.g. reproduces at a higher rate) while producing different substances that are not toxic for A. Then C is “better” than B, yet C can co-exist with A, so C is not “better” than A. One may also imagine that A can be “better” than C in some way (e.g. eats something that is necessary for C). It follows that the dominance relation that I described above is not an order relation. It implies in particular that it is not possible to define a general external criterion that is maximized through evolution.

But this does not mean that there are no optimality principles in evolution. On the contrary, consider again the previous example of the reproductive cells. Such cells always live under conditions of sparse resources, since they grow as long as there are enough resources. This means that cells die because of the scarcity of resources. Therefore, any mutation that improves the efficiency with which resources are used quickly develops in the population. It follows that resource efficiency is optimal in a stable form of life. Here optimal should not be understood as “best” with respect to a specific criterion, but rather as something that cannot be improved through the evolutionary process – or, to remove any notion of goodness, something that is stable through the evolutionary process. So, processes tend to be “optimal” because non-optimal ones are not stable.

Let us refine this notion of optimality. This is a pervasive notion in neuroscience, in particular. For example, it might be stated that wiring between neurons is optimal in some way. Two remarks are in order. First, this does not mean that, if one were to design a wiring plan from scratch, this is how it would like. Optimality must be understood in the sense of something that cannot be incrementally improved through the process of evolution. For example, there may be all sorts of oddities due to the specific evolutionary history of the species. Second, and this is perhaps more important, this does not mean that the organism has a plan (say, in its DNA), in the way an architect would have a plan, and that this plan is optimal. It only means that there are processes that develop the wiring, and that these processes cannot be improved. These processes do not need a “plan” in the sense of representation. They could take the form of rules of the kind “avoid such molecules” or “follow such gradient”.

This takes us to a refinement of the notion of optimality. Recall the previous remarks about homeostatic mechanisms. I noted that the first mechanisms developed by a cell should be those that minimize the bad impact of mutations, so that mutations are creative (or without effect) rather than destructive. Perhaps the same could be said of efficiency mechanisms. It seems that a general mechanism that regulates processes to make them maximally efficient in their present context (for example, in terms of metabolism) would be much more stable through evolution than specific mechanisms evolving towards maximum efficiency. Therefore, I postulate that there are early mechanisms, shared by all species, which regulate other processes of the organism to have to maximize their efficiency.

This idea could be taken one step further. Such regulation mechanisms can be seen as implementing the creative function of evolution in the time course of a single individual’s life. Clearly such meta-evolutionary mechanisms would be more stable than any particular mechanism (specific of a species) since, by construction, they are robust to all sorts of changes. There is at least one well-known example: the immune system. The immune system develops antibodies for specific foreign objects such bacteria, through what can be described as a Darwinian process (selection and cloning). Obviously this mechanism is much more stable through evolution than any mechanism targeted at a specific antigen. Note how this is “optimal” in the existential sense I discussed above: once it exists, any mechanism for a specific antigen loses its evolutionary value (although this may not be true of serious epidemic diseases). Thus it should be expected that there are such generic mechanisms in any stable form of life, rather than a “plan” for all specific actions that an organism must take. In the same way, it could be argued that the nervous system is such a generic system. In fact the analogy with the immune system was explicitly made by Edelman (a Nobel prize in immunology who then went to neuroscience).

These remarks suggest that some of the first and most universally shared mechanisms possessed by living beings should be meta-evolutionary or adaptive mechanisms by which the organism can adapt and maximize its efficiency in the face of changing conditions both in its environment (environmental change) and in the body itself (evolutionary change). This has an important consequence. When there is a discrepancy between empirical evidence and optimality principles, for example when some mechanism does not seem metabolically efficient, it is sometimes argued that it is not contradictory with the theory of evolution because there may be little evolutionary pressure for that specific mechanism. But implicitly, this argument assumes that evolutionary pressure actually applies to that specific mechanism, while I would argue that there may not be such specific mechanisms. Instead, it is more likely that there are generic efficiency-maximizing mechanisms, and for these mechanisms evolutionary pressure fully applies. Therefore, such discrepancy is more likely an indication that part of the interpretation is wrong, or pieces of the puzzle are missing.

To summarize, the conclusions of this existentialist reasoning about evolution is that all organisms, starting with unicellular organisms, must have developed 1) a large range of sophisticated homeostatic and repair mechanisms, starting with those that make mutations creative rather than destructive, 2) general-purpose regulating mechanisms that maximize the efficiency of other processes, 3) adapting or “meta-evolutionary” mechanisms that adapt the organism’s processes to changes, both external and internal, that occur within the time course of the individual’s life.

What is sound? (VIII) Sounds: objects or events?

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In my first post in this series, I described the differences between seeing and hearing. I noted that what characterizes sounds is that they are not persistent. One may say that sounds are “events”, as opposed to “objects”. I avoided this term because it is implied that an event has a definite start and end. Although this sometimes true (for example speech), many sounds actually do not have a definite end. For example, the sound produced when striking an object has a definite start (the impact) but not a definite end (energy decays exponentially). This is not to say that we hear these sounds as lasting forever, but simply that it is somewhat arbitrary to define a clear ending time. Worse, a number of sounds also have no start and no end. For example, the sound made by a river, or by wind. So what characterizes sounds is not exactly that they have a clear start and end, but rather that they are not persistent, they change through time. So, it could be said that sounds are events, but in the sense that they “happen”. When the sound is heard, the acoustical wave responsible for it is actually not here anymore (this is related to Husserl’s phenomenological description of time).

Now it could be argued that, if one could repeat the sound (with a recording for example, or less accurately by physically producing the sound several times), then perhaps it could qualify as an object. The notion of “repeatable object” is discussed by Jérôme Dokic (“Two ontologies of sound”), where there is an interesting remark about the notion of depiction. When seeing a painting, one sees both the content in the painting and the painting itself. But at first sight, it seems that sounds are not like this: the reproduction of a sound is like the original sound – possibly altered, but not a representation of the sound. But in fact there is an interesting auditory example: when a loud voice is heard through a phone and the volume is low, you actually hear a loud sound (the voice) inside a soft sound (the sound coming out of the phone).

Nevertheless, I think even in this case, describing the sound as a sort of “object” is misleading. An object is something that can be manipulated. For example, if you are looking at a box on the table, you can change your perspective on it, turn around it, see a face disappear behind an edge, etc. You can do this exploration because the object is persistent. In the same way, you could touch it, hold it, turn it, etc. So it makes sense to say that visual or tactile experience is about objects. But the same does not hold for sounds because they are transient, you cannot explore them. If you read my post on spatial hearing, you could oppose that you actually can: some of the properties of sound change when you move around the source. It is true, but precisely you do not hear these changes as changes in the sound, but in the localization of the sound. You feel the same sound, coming from some other direction. How about being able to repeat the sound with a recording? The point is that repeating is not manipulating. To manipulate, you need to change the perspective on the object, and this change of perspective tells you something about the object that you could not know before the manipulation (for example looking behind) – to be more precise, it can be said that visual shape is isomorphic to the relationship between viewing angle and visual field. If you repeat a recording exactly as in the original production, there is no manipulation at all. If you repeat it but, say, filter it in some way, you change it but it does not reveal anything about the sound, so it is not a change in perspective. You just produce a different sound, or possibly a depiction of the sound as in the phone example. The right visual analogy would be to insert a colored filter in front of your eyes, and this does not reveal anything about visual shape. Finally, it could be opposed that a sound could be repeatedly produced, for example by hitting the box several times, and the sound could be manipulated by hitting it with different strengths. But this is in fact not accurate: when the box is hit with a different strength, a different sound is produced, not a new perspective on the same sound. Here the object, what is persistent and can be manipulated, is not the sound: it is the material that produces the sound.

In fact, there is a well-known example in which the environment is probed using acoustical waves: the ultrasound hearing of bats. Bats produce loud ultrasound clicks or chirps and use the echoes to navigate in caves or localize small insects. In this case, acoustical waves are used to construct some sort of objects (the detailed shape of the cave), but I think this is really not what we usually mean by “hearing”, it seems rather closer to what we mean by seeing. I can of course only speculate about the phenomenological experience of bats, but I would guess that their experience is that of seeing, not of hearing.

To summarize: sounds are not like objects, which you can physically manipulate, i.e., have some control over the sensory inputs, in a way that is specific of the object. One possibility, perhaps, is to consider sounds as mental objects: things that you can manipulate in your mind, using your memory – but this is quite different from the notion of visual or tactile object.

On imitation

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How is it possible to learn by imitation? For example, consider a child learning to speak. She reproduces a word produced by an adult, for example “Mom”. How is this possible? At first sight, it seems like there is an obvious answer: the child tries to activate her muscles so that the sound produced is similar. But that’s the thing: the sound is not similar at all. A child is much smaller than an adult, which implies that: 1) the pitch is higher, 2) the whole spectrum of the sound is shifted towards higher frequencies (the “acoustic scale” is smaller). So if one were to compare the two acoustic waves, she would find little similarity (both in the time domain and in the spectral domain). Therefore, learning by imitation must be based on a notion of similarity that resides at a rather conceptual level – not at all the direct comparison of sensory signals. Note that the sensorimotor account of perception (in this case the motor theory of speech) does not really help here, because it still requires explaining why the two vastly different acoustic waves should relate to similar motor programs. To be more precise: the two acoustic waves actually do relate to similar motor programs, but the adult’s motor program cannot be observed by the child: the child has to relate the acoustic result of the adult’s motor program with her own motor program, when the latter does not produce the same acoustic result. Could there be something in the acoustic wave that directly suggests the motor program?

This was the easy problem of imitation. But here’s a harder one: how can you imitate a smile? In this case, you can only see the smile you want to imitate on the teacher’s face, but you cannot see your own smile. In addition, it seems unlikely that the ability is based on prior practicing in front of a mirror. Thus, somehow, there is something in the visual signals that suggests the motor program. These are two completely different physical signals, therefore the resemblance must lie somewhere in the higher-order structure of the signals. This means that the perceptual system is able to extract an amodal notion of structure, and compare two structures independently of their sensory origin.

Memory as an inside world

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A number of thinkers oppose the notion of pictorial representations, or even of any sort of representation, in the brain. In robotics, Rodney Brooks is often quoted for this famous statement: “the world is its own best model”. In a previous post, I commented on the fact that slime molds can solve complex spatial navigation problems without an internal representation of space – in fact, without a brain! It relies on using the world as a sort of outside memory: the slime mold leaves some extracellular trace on the floor, where it has previously been, so that it avoids being stuck in any one place.

This idea is also central in the sensorimotor theory of perception, and in fact Kevin O’Regan argued about “the world as an outside memory” in an early paper. This is related to a number of psychological findings about change blindness, but I will rephrase the argument from a more computational perspective. Imagine you are making a robot with a moveable eye that has a fovea. At any given moment, you only have a limited view of the world. You could obtain a detailed representation of the visual scene by scanning the scene with your eye and storing the images in memory. This memory would then be a highly detailed pictorial representation of the world. When you want to know some information about an object in any part of the visual scene, you can then look at the right place in the memory. But then why look at the memory if you can directly look at the scene? If moving the eye is very fast, which is the case for humans, then from an operational point of view, there is no difference between the two. It is then simply useless and inefficient to store the information in memory if the information is immediately available in the world. What might need to be stored, however, is some information about how to find the relevant information (what eye movements to produce), but this is not a pictorial representation of the visual scene.

Despite what the title of this post might suggest, I am not going to contradict this view. But we also know that visual memory exists: for example, we can remember a face, or we can remember what is behind us if we have seen it before (although it is not highly detailed). Now I am throwing an idea here, or perhaps an analogy, which might initially sound a bit crazy: how about if memory were like an inside world? In other words, how about interpreting the metaphor “looking at something in memory” in a literal way?

The idea of the world as an external memory implicitly relies on a boundary between mind and world that is put at the interface of our sensors (say, the retina). Certainly this is a conceptual boundary. Our brain interacts with the environment through an interface (sensors/muscles), but we could equally say that any part of the brain interacts with its environment, made of everything outside it, including other parts of the brain. So let us imagine for a while that we put the mind-world boundary in such a way that the hippocampus, which is involved in memory (working memory and spatial memory), is outside it. Then the mind can request information about the world from the sensory system (moving the eyes, observing the visual inputs), or in the same way from the hippocampus (making some form of action on the hippocampus, observing the hippocampal inputs).

Perhaps this might seem somehow like a homunculus thinking exercise, but I think there is something interesting in this perspective. In particular, it puts memory and perception at the same level of description, in terms of sensorimotor interaction. This is interesting because from a phenomenological point of view, there is a similarity between memory and perception: the memory of an image feels (a bit) like an image, or one can say that she “hears a melody in her head”. At the same time, memory has distinct phenomenal properties, for example one cannot interact with memory in the same way as with the physical world, it is also less detailed, and finally there are no “events” in memory (something unpredictable happening).

In other words, this view may suggest a sensorimotor account of memory (where “sensorimotor” must be understood in a very broad sense).

Robots and jobs

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Are robots going to free us from the slavery of work, or are they going to steal people’s jobs?

As a computational neuroscientist, this is a question I sometimes think about. For a long time, I have followed a self-reassuring reasoning, which seems to make sense from a logical point of view, that having robots do the work for us means that either we get more products for the same amount of work or each person works less for the same quantity of products. So it has to be a good thing: ideally, robots would do the work we don’t want to do, and we would just do what we are interested in doing – maybe travel, write books, see our friends or play music.

This is a fine theoretical argument, but unfortunately it is also one that ignores the economy we live in. Maybe we could (or should) think of an economy that would make this work, but how about our current capitalist economy? Very concretely, if robots arrive on the market that are able to do the job that people previously did for cheaper, then these people would simply lose their job. If work can be outsourced to poorer countries, then in the same way it can also be outsourced to robots.

One counter-argument, of course, is that in a free market economy, people would temporarily lose their job but then they would be reassigned to other jobs and the whole economy would be more productive. This is a classical free-market fundamentalist argument. But there are at least two problems with this argument. The first is that it commits the mistake of thinking the economy as a quasi-static system: it changes, but it is always in equilibrium. It is implicitly assumed that it is easy to change job, that it has a negligible cost, that large scale changes in labor market has no significant impact on the rest of the economy (think for example of the effect on the financial system of thousands of sacked people being unable to pay their mortgage). Now if we think of a continuous progress, in which innovations regularly arrive and continuously change the structure of the labor market, then it is clear that the economy can never be in the ideal equilibrium state in which jobs are perfectly allocated. At any given time there would be a large fraction of the population that would be unemployed. In addition, anyone would then face a high risk of going through such a crisis in the course of their work life. This would then have major consequences for the financial system, as it would make loans and insurances riskier, and therefore more expensive. These additional costs to society (cost of unemployment and reconversion, financial risk, etc) are what economists call “externalities”: these are costs that have to be paid by society, but they are not supported by the ones that take the decisions that are responsible for these costs. For the company that replaces a human by a robot, the decision is based on the salary of the human vs. the cost of the robot, but it does not include the cost of the negative externalities. For this reason, it is possible that companies take decisions that seem beneficial for each one of them, and yet that have a negative impact on the global economy (not even considering the human factor).

A second problem is that the argument neglects a critical aspect of capitalist systems, which is the division between capital and work. When a human is replaced by a robot, what was previously the product of work is now the product of capital (investment in buying the robot) – see this blog post by Paul Krugman. Very concretely, this means that a larger part of the wealth goes to the owners rather than to the workers. As a thought experiment, we could imagine that the workforce is completely replaced by robots, and that the owner would only buy the robots and then get the money from customers without doing anything. Wealth would then be distributed according to how many robots one owns. This might seem far-fetched, but if you think about it, this is pretty much how real estate works.

So concretely, introducing robots in a capitalist economy means increasing productivity, but it also means that owners get an increasingly bigger part of the pie. In such an economy, the ideal robotic world is a dystopia in which wealth is distributed exclusively in proportion of what people own.

This thought is very bothering for scientists like me, who are more or less trying to make this ideal robotic world happen, with the utopia of the no-forced-work society in mind. How could one avoid the dystopian nightmare? I do not think that it is possible to just stop working on robots. I could personally decide not to work on robots, and maybe I would feel morally right and good about myself, having no responsibility in what happens next, but that would just be burying my head in the sand. The only way it will not happen is if all scientists in the world, in all countries, would stop working on robots or any sort of automation that would increase productivity (internet?). We don’t even seem to be able to stabilize our production of carbon dioxide even when we agree on the consequences, so I don’t think this is very realistic.

So if we can’t stop the scientific progress from happening, then the only other way is to adapt our economy to it. Imagine a society with robots doing all the work, entirely. Since there is no work at all in such a society, then in an unregulated free market economy wealth can only be distributed according to the amount of capital people have. There is simply no other way it could be distributed. Such an economy is bound to lead to the robotic nightmare.

Therefore, society has to take global measures to regulate the economy, and make the distribution of wealth fairer. I don’t have any magical answer, but we could throw a few ideas. For example, one could get rid of inheritance (certainly not easy in practice), and transmit capital from the deceased to the newborn in equal proportion. Certainly some people would get richer than others by the end of their lives, but it would be limited. As a transition policy, one could allow the replacement of people by robots, but the fired worker would own part of the robot. Alternatively, robots could only be owned by people and not by companies. A robot could then replace a worker only when a worker buys the robot and rents it to the company. Another alternative is that robot-making companies belong to the State and can only rent the robots to companies. The wealth would then be shared among citizens.

Certainly all these ideas come with difficulties, none of them is ideal, but one has to keep in mind that not implementing any regulation of this type can only lead to the robotic dystopia.