I am making a break before I continue on my review of spike-based theories, and I want to comment on the notion of “neural code”. These are keywords that are used in a large part of the neuroscience literature, and I think they are highly misleading. For example, you could say that neurons in V1 “code for orientation”. But what this statement refers to, in reality, is simply that if we record the response of such a neuron to an oriented bar, then we observe that its firing rate is modulated by the orientation, peaking at an orientation that is then called the “preferred orientation”. First of all, the notion of a “preferred orientation” is just a definition tied to the specific experimental protocol (the same is true for the notion of best delay in sound localization). Empirically speaking, it is an empty statement. In particular, by itself it does not mean that the cell actually “prefers” some orientations to others in any way, because a preferred orientation can be defined in any case and could be different for different protocols – it is just the stimulus parameter giving maximum response. So the only empirical statement associated to the claim “the neuron codes for orientation” is in fact: the neuron’s firing rate varies with orientation. Therefore, using the word “codes” is just a more appealing way of saying “varies”, but the empirical content is actually no more than “varies”.
In what sense then can we say that the neuron “codes” for orientation? Coding means presenting some information in a way that can be decoded. That is, the neuron codes for an orientation with its firing rate in the sense that from its firing rate it is possible to infer the orientation. Here we get to the first big problem with the notion of a “neural code”. If the firing rate varies with orientation and one knows exactly how (quantitatively), then of course it is possible to infer some information about orientation from the firing rate. The way you would decode a particular firing rate into an estimated orientation is by looking at the tuning curve, obtained by the experimental protocol, and look for the orientation that gives the best matching firing rate. But this means that the decoding process, and therefore the code, is meant from the experimenter’s point of view, not from the organism’s point of view. The organism does not know the experimental protocol, so it cannot make the required inference. If all the organism can use to decode the orientation is a number of spikes, then clearly this task is nearly impossible, because without additional knowledge, a tremendous number of stimuli could produce that same number of spikes (e.g. by varying contrast or simply presenting something else than a bar). Thus the first point is that the notion of a code is experimenter-centric, so talking about a “neural code” in this sense is highly misleading, as the reader of this code is not neurons but the experimenter.
So the first point is that, if the notion of a neural code is to make any sense at all, it should be refined so as to remove any reference to the experimental protocol. One clear implication is that the idea that a single neuron can code for anything is highly questionable: is it possible to infer anything meaningful about the world from a single number (spike count), and no a priori knowledge? Perhaps the joint activity of a set of neurons may make more sense. This reduces the interest of “tuning curves” in terms of coding – it may still be informative about what neurons “care about”, but not about how they represent information, if there is such a thing. Secondly, removing any reference to the experimental protocol means that one can speak of a neural code for orientation only if it does not depend on other aspects, e.g. contrast. Indeed if the responses were sensitive to orientation but also to everything else in the stimulus, how could one claim that the neuron codes for orientation? Finally, thinking of a code with a neural observer in mind means that, perhaps, not all codes make sense. Indeed, is the function of V1 to “represent” the maximum amount of visual information? This view, and the search for “optimal codes” in general, seems very odd from the organism’s point of view: why devote so many neurons and so much energy to represent exactly the same amount of information that is already present in the retina? If a representation has any use, then this representation must be different in nature from the original presentation, and not just in content. So the point is not about how much information there is, but in what form it is represented. This means that codes cannot be envisaged independently of a potential decoder, i.e., a specific way in which neurons use the information.
I now come to a deeper criticism of the notion of neural code. I started by showing that the notion is often meant in the sense of a code for the observer, not for the brain. But let us say that we have fixed that error and we are now looking for neural codes, with a neural-centric view rather than an experimenter-centric view. But still, the methodology is: looking at neural responses (rates or spike timings) and trying to find how much information there is and under what form. Clearly then, the notion that neurons code for things is not an empirical finding: it is an underlying assumption of the methodology. It starts, not ends, by assuming that neurons fire so that the rest of the brain can observe it and take the information it sees in this firing. It is postulated, not observed, that what neurons do is produce some form of representation for the rest of the brain to see. This appears to be very centered on the way we, external observers, acquire knowledge about the brain, and it has a strong flavor of the homunculus fallacy.
I suggest we consider another perspective on what neurons do. Neurons are cells that continuously change in many aspects, molecular and electrical. Even though we may want to describe some properties of their responses, spikes are transient signals, there is nothing persistent in them in the same way as a painting. So neurons do not represent the world in the same way as a painter would represent the world. Second, spikes are not things that a neuron leaves there for observers to see, like the pigments on a painting. On the contrary, a neuron produces a spike and actively sends it to target neurons, where changes will occur because of this spike. This is much more like an action than like a representation. Thus it is wrong to say that the postsynaptic neuron “observes” the activity of presynaptic neurons. Rather, it is influenced by it. So neural activity is not a representation, it is rather an action.
To briefly summarize this post: neurons do not code. This is a view that can only be adopted by an external observer, but it is not very meaningful to describe what neurons do. Perhaps it is more relevant to say that neurons compute. But the best description, probably, is to say that neurons act on other neurons by means of their electrical activity. To connect with the general theme of this series, these observations emphasize the fact that the basis of neural computation is truly spikes, and that rates are an external observer-centric description of neural activity.