Revisiting Gallistel's conjecture

I recently received two papers that explore Gallistel’s conjecture (see here for one discussion) concerning the locus of neuronal computation. The first (here) is a short paper that summarizes Randy’s arguments and suggests a novel view of synaptic plasticity. The second (here: http://www.nature.com/nature/journal/v538/n7626/full/nature20101.html)[1] accept Randy’s primary criticism of neural nets and couples a neural net architecture with a pretty standard external memory system. Let me say a word about each.

The first paper is by Patrick Trettenbrein (PT) and it appears in Frontiers in Systems Neuroscience. It does three things.

First, it reviews the evidence against the idea that brains store information in their “connectivity profiles” (2). This is the classical assumption that inter-neural connection strengths are the locus of information storage. The neurophysiological mechanisms for this are long term potentiation (LTP) and long term depression (LTD). LTP/D are the technical terms for whatever strengthens or weakens interneuron connections/linkages. I’ve discussed Gallistel and Matzel’s (G&M) critique of the LTP/D mechanisms before (see here). PT reviews these again and emphasizes G&M’s point that there is an intimate connection between this Hebbian “fire together wire together” LTP/D based conception of memory and associationist psychology. As PT puts it: “Crucially, it is only against this background of association learning that LTP and LTD seem to provide a neurobiologically as well as psychologically plausible mechanism for learning and memory” (88). This is why if you reject associationsim and endorse “classical cognitive science” and its “information processing approach to the study of the mind/brain” you will be inclined to find contemporary connectionist conceptions of the brain wanting (3).

Second, there is recent evidence that connection strength cannot be the whole story. PT reviews the main evidence. It revolves around retaining memory traces despite very significant alterations in connectivity profiles. So, for example, “memories appear to persist in cell bodies and can be restored after synapses have been eliminated” (3), which would be odd if memories lived in the synaptic connections. Similarly it has recently been shown that “changes in synaptic strength are not directly related to storage of new information in memory” (3). Finally, and I like this one the best (PT describes it as “the most challenging to the idea that the synapse is the locus of memory in the brain”), PT quotes a 2015 paper by Bizzi and Ajemian which makes the following point:

If we believe that memories are made of patterns of synaptic connections sculpted by experience, and if we know, behaviorally, that motor memories last a lifetime, then how can we explain the fact that individual synaptic spines are constantly turning over and that aggregate synaptic strengths are constantly fluctuating?

Third, PT offers a reconceptualization of the role these neural connections. Here’s an extended quote (5):

…it occurs to me that we should seriously consider the possibility that the observable changes in synaptic weights and connectivity might not so much constitute the very basis of learning as they are the result of learning.

This is to say that once we accept the conjecture of Gallistel and collaborators that the study of learning can and should be separated from the study of memory to a certain extent, we can reinterpret synaptic plasticity as the brain's way of ensuring a connectivity and activity pattern that is efficient and appropriate to environmental and internal requirements within physical and developmental constraints. Consequently, synaptic plasticity might be understood as a means of regulating behavior (i.e., activity and connectivity patterns) only after learning has already occurred. In other words, synaptic weights and connections are altered after relevant information has already been extracted from the environment and stored in memory.

This leaves a place for connectivity, but not as the mechanism of memory but as what allows memories to be efficiently exploited.[2] Memories live within the cell but putting these to good use requires connections to other parts of the brain where other cells store other memories. That’s the basic idea. Or as PT puts it (6):

The role of synaptic plasticity thus changes from providing the fundamental memory mechanism to providing the brain’s way of ensuring that its wiring diagram enables it to operate efficiently…

As PT notes, the Gallistel conjecture and his tentative proposal are speculative as theories of the relevant cell internal mechanisms don’t currently exist. That said, neuroiphsyiological (and computational, see below) evidence against the classical Hebbian view are mounting and the serious problems for storing memories in usable form in connections strengths (the bases of Gallistel’s critique) are becoming more and more well recognized.

This brings us to the second Nature paper noted above. It endorses the Gallistel critique of neural nets and recognizes that neural net architectures are poor ways of encoding memories. It adds a conventional RAM to a neural net and this combination allows the machine to “represent and manipulate complex data structures.”

Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data.

Note that the system is still “associationist” in that learning is largely data driven (and as such will necessarily run into PoS problems when applied to any interesting cognitive domain like language) but it at least recognizes that neural nets are not good for storing information. This latter is Randy’s point. The paper is significant for it comes from Google’s Deep Mind Project and this means that Randy’s general observations are making intellectual inroads with important groups. Good.

However, this said, these models are not cognitively realistic for they still don’t make room for the domain specific knowledge that we know characterizes (and structures) different domains. The main problem remains the associationism that the Google model puts at the center of the system. As we know that associationism is wrong and that real brains characterize knowledge independently of the “input,” we can be sure that this hybrid model will need serious revision if intended as a good cog-neuro model.

Let me put this another way. Classical cog sci rests on the assumption that representations are central to understanding cognition. Fodor and Pylyshyn and Marcus long ago agued convincingly that connectionism did not successfully accommodate representations (and, recall, that connectionist agreed that their theories dumped representations) and that this was a serious problem for connectionist/neural net architectures. Gallistel further argued that neural nets were poor models of the brain (i.e. and not only of the mind) because they embody a wrong concpetion of memory; one that that makes it hard to read/write/retrieve complex information (data structures) in usable form. This, Gallistel noted, starkly contrasts with more classical architectures. The combined Fodor-Pylyshyn-Marcus-Gallistel critique then is that connectionist/neural net theories were a wrong turn because they effectively eschewed representations and that this is a problem both from the cognitive and the neuro perspective. The Google Nature paper effectively concedes this point, recognizes that representations (i.e. “complex data structures) are critical  and resolves the problem by adding a classical RAM to a connectionist front end.

However, there is a second feature of most connectionist approaches that is also wrong. Most such architectures are associationist. They embody the idea that brains are entirely structured by the properties of the inputs to the system. As PT puts it (2):

Associationism has come in different flavors since the days of Skinner, but they all share the fundamental aversion toward internally adding structure to contingencies in the world (Gallistel and Matzel 2013).

Yes! Connectionists are weirdly attracted to associationism as well as rejecting representations. This is probably not that surprising. Once on thinks of representations then it quickly becomes clear that many of their properties are not reducible to statistical properties of the inputs. Representations have formal properties above and beyond what one finds in the input, which, once you look, are found to be causally efficacious. However, strictly speaking associationsim and anti-representationalism are independent dimensions. What makes Behaviorists distinctive among Empiricists is their rejection of representations. What unifies all Empiricists is their endorsement of associationism. Seen form this perspective, Gallistel and Fodor and Pylyshyn and Marcus have been arguing that representations are critical. The Google paper agrees. This still leaves associationism however, and position the Googlers embrace.[3]

So is this a step forward? Yes. It would be a big step forward if the information processing/representational model of the mind/brain became the accepted view of things, especially in the brain sciences. We could then concentrate (yet again) all of our fire on pernicious Empiricism so many Cog-neuro types embrace.[4] But, little steps my friends, little steps. This is a victory of sorts. Better to be arguing against Locke and Hume than Skinner![5]

That’s it. Take a look.

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[1] Thx to Chris Dyer for bringing the paper to my attention. I put in the URL up rather than link to the paper directly as the linking did not seem to work. Sorry.

[2] Redolent of a competence/performance distinction, isn’t it?  The physiological bases of memory should not be confused with the physical bases for the deployment of memory.

[3] I should add that it is not clear that the Googlers care much about the cog-neuro issues. Their concerns are largely technological, it seems to me. They live in a Big Data world, not one where PoS problems (are thought to) abound. IMO, even in a uuuuuuge data environment, PoS issues will arise, though finding them will take more cleverness. At any rate, my remarks apply to the Google model as if intended as a cog-neuro one.

[4] And remember, as Gallistel notes (and PT emphasizes) much of the connectionism one sees in the brain sciences rests on thinking that the physiology has a natural associationist interpretation psychologically. So, if we knock out one strut, the other may be easier to dislodge as well (I know that this is wishful thinking btw).

[5] As usual, my thinking on these issues was provoked by some comments by Bob Berwick. Thx.

The concise Gallistel on how brains compute

Jeff Lidz sent me this great little piece by Randy Gallistel on his favorite theme: how most neuroscientists have misunderstood how brains compute. I’ve discussed Randy’s stuff in various FoL posts (here, here, and here). Here in just four lucid pages, Randy makes his main point again. If he is right (and the form of his argument seems impeccable to me), then much of what goes on in neuroscience is just plain wrong. Indeed, if Randy is right, then current neo-connectionist/neural net assumptions about the brain are about as accurate as 1950s-60s behaviorist conceptions were about the mind. In other words, at best of tertiary interest and, more likely, deserving to be completely forgotten.[1] At any rate, Randy here makes four main points.

First, that there is recent evidence (discussed here) strongly pointing to the conclusion that information can be stored inside a single neuron (rather than in connections of many neurons).

Second, that there is scads of behavioral evidence showing that brains store number values and that there is no way of storing numbers this in connection weights, thus implying that any theory of the brain that limits itself to this kind of hardware must be at best incomplete and at worst wrong.

Third, that there is a close connection between neural net “plasticity” conceptions of the brain and traditional empiricist conceptions of the mind (especially learning). In fact, Randy argues that these are largely flip sides of the same coin.

Fourth, that brains already contain all the hardware that is required to function like classical computers, the latter being the perfect complements for the computational cognitive theories that replaced behaviorism.

And all in four pages.

There is one argument that Randy hints at but doesn’t stress that I would like to add to his four. It is a conceptual argument. Here it is.

Whatever one thinks of cognition, it is clear that animals use large molecules like DNA and RNA for information processing. Indeed, this is now standard biological dogma. As Gallistel and King (here) illustrates, this system has all the capacities of a classical computer (addresses, read-write memory, variables, binding etc.). So here’s the conceptual argument: imagine that you had an animal with the wherewithal to classically compute hereditary information but instead of repurposing (exapting) this system for cognitive ends it developed an entirely different additional system for this purpose. In other words, it had all it needed sitting there but ignored these resources and embodied cognition in a completely different way. Does this seem plausible? Is this the way evolution typically works? Isn’t opportunism the main mover in the evolution game? And if it is, doesn’t this suggest that Randy’s conjecture must be right? In fact, wouldn’t it be weird if large chunks of cognition did not exploit that computational machinery already sitting there in DNA/RNA and other large molecules? In fact, wouldn’t the contrary assumption bear a huge burden of proof? Well, you know what I think!

Why is this not the common perception? Why is Randy’s position considered exotic? Here’s the one word answer: Empiricism! In the cog-neuro world this is the default view. There is little to empirically support this conception (see here for a review of the pas de deux between unsupported empiricism in psychology and tendentious reasoning in neural net neuroscience). Indeed, it largely flourishes when we know next to nothing about some domain of inquiry. However, it is the default conception of the mind. What Randy is pointing out (and has repeatedly pointed out and is right to point out) is that it is fatally flawed, not only as a theory of mind but also as a theory of the brain. And its flaws are conceptual as well as empirical. I can’t wait for the day that this becomes the conventional wisdom, though given the methodological dualism characteristic of the cog-neuro-sciences, I suspect that this day is not just around the corner. Too bad.

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[1] Note that I say “deserving” of amnesia. This concedes the sad fact that neo-behaviorism is making a vigorous comeback within cognition. Yet another indication of the collapse of civilization.

More on intra-nuron computation

I here linked to a Youtube video of a talk that Randy Gallistel gave in Boston that goes over his argument for an intra-neuron based conception of brain computation. This recapitulates much of what he argues for in his paper that I posted (here), although it only goes over one of three recent experiments that supports this view in detail (btw, the other two are also pretty neat).  It is well worth watching for the presentation is very clear and easy to follow.

It also has an added bonus: a skeptical commentary by John Lisman from Brandeis. Lisman argues that the results that Randy points to are not inconsistent with a more classical inter-neuron circuit based conception of brain computation. He reviews some of his own work in this regard, which is interesting. 

I personally found a few other things interesting as well. First, Lisman has two main arguments, both promisory. The first is that though he agrees with Randy that the LTP/D evidence does not support the required timing for learning that it has been pressed to serve, this does not entail that some other version of the theory might not be serviceable. The second is that he has recently shown how to block/erase LTP/D accretions and hopes to run an experiment showing that this suffices to also erase a memory. He notes that should he be able to do this it would provide evidence for the view that the inter-neuron LTP/D mechanism underlies memory.  This experiment has not yet been run, hence my description as ‘promisory.’

Randy notes in his talk and paper that the LTP/D theory of memory as strengthened connections among neurons in a net is a venerable theory. It’s been around for a very long time. Lisman seems to agree. I thus found it interesting that Lisman’s retort did not proceed by citing chapter and verse of results in favor of the view but was largely defensive: a “that this is wrong does not mean that some version may be right” strategy for defending it. Second, I found it interesting that Lisman did not seem to understand Randy’s request to outlined how numerical information could be stored in a synapse in a way that it could be used. Randy wanted a description of a general mechanism for doing this independently of how this information was then put to use. Lisman kept retorting how this or that particular phenomenon might be modeled. In Randy’s view memory is the capacity to code information for storage that can later be retrieved. There is no mention as to how this information will be used. Presumably stored information can be used in multiple ways. For Lisman memory is not something that is use neutral but a link between one behavior and another. Lisman, in effect, does not seem to understand what Randy is asking him to provide, or, to be more charitable (as I should be) he rejects the idea that memory is disconnected from the uses it is put to.

I mention this for it reflects a point that is a central feature of Empiricism: identifying what something is as what that thing does. I’ve discussed this before (here), but I liked this particular example of the difference in action.  Let me say a bit more.

Many (me included) have tended to take the salient property of Empiricism to be its penchant for associationism. Randy points to this in his paper and lecture and he is, of course, correct to note the strong relationship. However, I now think that the deeper feature of Empiricism is its identification of the “powers/nature” of a thing (these are Cartwright’s terms, see above link) with what it does. Rationalists reject this. To specify the powers or nature of something is to provide an abstract description of its properties. What it does is a complex interaction of these properties with other things.  So when Randy asks for a physical basis for memory he wants an account of how to store information in a retrievable form independent of how this information might be later used and manifested. The mechanism is general. The occasion of use is just one manifestation of it. For Empiricists what something is just is a summary (perhaps statistical) of its effects. Not so for the Rationalist.[1]

Last point: Lisman takes Randy as arguing that brain computation must supervene on DNA/RNA structure. Randy points out both in the paper and in the talk that this is not a central feature of his view. We know how information is coded in DNA/RNA and so it provides a proof of concept for intra-neuronal computation that DNA/RNA machinery could provide the physical mechanisms required for such computation. But, as Randy notes that the cognitive machinery be DNA/RNA is not necessary to his main point (see p. 6). Other kinds of molecules can serve to undergird such computation (the most relevant feature, it seems, having two stable states thus allowing the molecule to serve as a switch coding 1/0). This said, Randy notes in the video that we currently do not know much about how information is coded within the neuron. Thus, if Randy is correct, these very important details remain to be developed. Randy’s argument is that this is a reasonable place to look for the computational bases of cognition given that the apparent failure of the inter-neuron conception and the evidence that is amassing that individual cells (rather than networks) do in fact store acquired information in usable form.

To end: As I’ve said before, I find this to be really exciting stuff. So, watch the video, it’s really fun.

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[1] On this view, the competence/performance distinction is itself an important Rationalist one.

Neuronal computation: Randy's radical suggestion finds empirical traction

Sometimes you read something that, if correct, will lead to deep disruption of the accepted wisdom. In my intellectual life, I’ve read a few such pieces (Chomsky’s review of Skinner, Syntactic Structures, Chapter 1 of Aspects and “on Wh-movement” (among many others), Chapter 1 of Kayne’s antisymmetry book, Chapter 1 of Rizzi’s book on minimality, Fodor on modularity and the language of thought hypothesis spring immediately to mind. Recently, I read another. The paper is by Randy Gallistel (here) and it does three important things: (i) It outlines the relevance of the Language of Thought Hypothesis (LOT) for the computational theory of the brain, (ii) It discusses the main problems with the received neuro-wisdom concerning brain computation and shows how it is incompatible with what we know about mental computations and (iii) it provides evidence for the hypothesis (what I have elsewhere referred to as the Gallistel-King Conjecture (here, here)) that the neuron, rather than the neuronal ensemble (i.e. net), is the primary unit of brain computation. This last is what is truly revolutionary if correct, for it states that neuro-science has been looking in the wrong place for the computational architecture of the brain.  And that’s a big deal.  A very big deal. In fact, in neuro-science terms it is as big a deal as one can imagine. Let me say a little about each of these points. But before doing so, let me strongly encourage you to download and read Randy’s paper. It is exceedingly readable and very didactic. Let me also thank Randy for allowing me to link to it here. IMO, this is something that all linguists of the generative variety (i.e. those right thinking people that have a cognitive take on the linguistic enterprise) should read, understand and absorb.

The paper is intended as an encomium to Jerry Fodor and the LOT. Randy starts by observing the very tight connection between LOT and the current standard understanding of neural architecture where the unit of analysis is the net and the main operation is in/de-creasing connection strengths between nodes in the net. This is what the standard neuro wisdom takes thinking to effectively consist in; reordering connection strengths among neurons in response to experience.  Randy notes that this picture is incompatible with the LOT. In other words, if the LOT is right, then the current neural fascination with nets and connections is just wrong in that it is looking for the physical bases of cognition in the wrong place. Why? Because if LOT is correct then cognition is symbolic and thinking consists in operations over these symbols and neural nets suck at symbolic computation. 

This should not be news to those that have followed Fodor and Pylyshyn’s and Gallistel and King’s and Marcus’s critiques of connectionism over the last 30 years. What Randy adds here to this critique is the observation that connectionist have completely failed to address any of them. They have failed to show how such systems could symbolically compute anything at all. As Randy puts it (p. 2), there is no model of how such a brain could even add two numbers together:

"There is, however, a problem with this hypothesis: synaptic conductances are ill suited to function as symbols (Gallistel and King 2010). Anyone who doubts this should ask the first neuroscientist they can corner to explain to them how the brain could write a number into a synapse, or into a set of synapses. Then, step back and watch the hands wave. In the unlikely event of an intelligible answer, ask next how the brain operates on the symbols written into synapses. How, for example, does it add the number encoded in one synapse (or set of synapses) to the number encoded in a different synapse (or set…) to generate yet another synapse (or set…) that encodes the sum?"

The reason is that such “brains” cannot actually manipulate symbols, unlike, say, a classic machine with a Turing architecture (i.e. one with read/write memory and indirect addressing to name two important features).

Why don’t neuroscientists find this disturbing? Randy notes one important reason: they are all card carrying Empiricists with a strong commitment to associationism. And given this psychology, there is nothing wrong with neural nets (e.g. even the “fires together, wires together” slogan has the pungency of empiricism around it). Of course, if LOT is right then associationsim is dead wrong (as Fodor has repeatedly emphasized to no apparent avail), and so if Fodor is right then the neurosciences are destined to get nowhere as they simply misunderstand what the brain needs to do in order to mentate. It needs to symbolically compute and any brain architecture that cannot do that (and do that fairly easily) is the wrong brain architecture.[1]

I, of course, find this completely compelling. However, even if you don’t, the paper is still worth reading for the relation it establishes between Empiricism/associationism and connectionist-synaptic theories of the brain. This link explains a lot about the internal logic of each (and hence their staying power). Each position reinforces the other, which is why connectionists tend to be associationists and vice versa. Again as Randy puts it (p. 3):

"…the synaptic theory of memory rested on a circularly reinforcing set of false beliefs: The neuroscientists’ belief in the synaptic theory of memory was sustained in no small measure by the fact that it accorded with the psychologists’ associative theory of learning. The psychologists’ belief in the associative theory of learning was sustained in no small measure by its accord with what neuroscientists took to be the material realization of memory."

Of course, the tight link between the two also shows how to bring the whole thing (hopefully, crashing) down: debunk Empiricism/associationsim then bye bye connectionism. That in fact is what Randy concluded a while ago: Empiricism is clearly false so that any brain architecture that relies on its being true is also false. Good argument. Sane conclusion. Or as Jerry Fodor might have put this: neuroscience’s modus ponens is Randy’s modus tollens. Nothing so gratifying as watching someone being hoist on their own petard!

Importantly, Randy has over the years presented many other arguments against this conception of neural computation. He has argued that the basics of the theory never delivered what was promised.  I discussed a recent review paper by Gallistel and Matzel (here) that demonstrates this in detail (and that I urge you all to read). So, not only is the learning theory widely assumed (at least tacitly) by the neuro-science community wrong from a cognitive point of view, but the theory’s purported mechanisms get you very little empirically even in its own terms.[2]  Given this, why hasn’t the whole thing collapsed already? Why are we still talking as if there is something to learn about the computational powers of the brain by treating it as a connectionist network of some sort? 

The main reason anything hangs around even if patently inadequate is that there is nothing else on offer. As the saying goes, you cannot beat something with nothing, no matter how little that nothing contains. So, we had nets because we had no real alternative to neural/synaptic computations (and, of course, because Empiricism is rampant everywhere in the cog-neuro world. It really seems to be innate, as Lila once quipped). Until now. Or this is what Randy argues.

Randy reviews various kinds of evidence that the brain unit of computation is the single neuron. In other words, thinking goes on inside single neurons not (or not only) among neurons.[3]  Why there?

Randy provides various reasons for thinking that this is the right place to look. First, he provides some conceptual reasons (truth be told, these convinced me a while ago, the more recent empirical evidence being tasty icings on the cake). Randy points out that all the hardware required for symbolic computation lives inside each cell. DNA, RNA are mini digital computers. They can write information to memory, read it from memory, allow for indirect addressing (and hence variable binding), etc.  Thus, each cell carries with it lots of computing power of the sort required for symbolic computation, and it uses it to carry inherited information forward in time and computationally exploits this information in development in building an organism.

Indeed, we’ve known this for a very long time. It goes back at least to Watson and Crick’s discovery that genes code for information. Recently, we’ve further discovered that some of the genetically coded information is very abstract. Genes and proteins can code for “eyes” and body positions (e.g. anterior, dorsal, distal) independently of the detailed programs that build the very specific actual structures (e.g. human vs insect eye). As Randy puts it (p. 3):

"The old saw that genes cannot represent complex organic structures and abstract properties of a structure is simply false; they can and they do."

So, given that we’ve got all this computing power sitting there in our neuronal cells a natural question arises: how reasonable is it that the greatest of all innovators, Mother Nature, never thought to use this available computational system for cognitive ends? To Randy’s (and my) mind, not very. After all, if a nose can become a “hand” or a wrist bone a “thumb” why couldn't a digital computational system in place for carrying inherited information across generational time be repurposed to carry acquired cognitive information within an organism’s lifetime. Information is information. Carry one kind and you can carry another. Indeed, given the availability of such a useful digital coding architecture in every living creature the possibility that evolution never “thought” to repurpose it for cognitive ends seems downright un-Darwinian (and no card carrying rational scientist would want to be that nowadays!). So, it seems that only a believer in a malevolent, slovenly deity, should reject the likelihood that (at least part of) thinking supervenes on DNA/RNA/protein computational machinery. And this machinery resides inside each neuron, hence thinking is (at least in part) intra (not inter)-neuronal (i.e. the unit of computation is the neuron). Or as Randy puts this (p. 6):

"…processes operating within the cells at the level of individual molecules implement the basic building blocks of computation, and they do so in close connection with the reading of stored information. The information in question is hereditary information, not experientially acquired information. Nonetheless, it is tempting to think that evolution long ago found a way to use this machinery, or closely related machinery, or, at least, functionally similar molecular machinery, to do the same with experientially acquired information." (emphasis is Randy’s)

Yes indeed. Very very tempting. Some might say “irresistible.”

Randy notes other virtues of intra-neuronal molecular computation. He touts the efficiency of such a chemically based neuronal computation (p. 6). It can pack lots of info in very small dense package and this allows both for computational speed and energy efficiency. Apparently, this is the trick that lies behind the huge increasing in computing power we’ve seen over the last decades (putting more and more circuitry and memory into smaller and smaller packages) and taking biological computation to be grounded in large molecules within the neuron yields similar benefits as a matter of course.

So, a mechanism exists to support a cognitively friendly conception of computation and that mechanism is able to stably store a vast amount of info cheaply and is able to computationally use it quickly and at low (energy) cost. Who could want more? So conceptually speaking, wouldn’t it have been very dumb (if not downright criminal) if Mother Nature (coy tinkerer that she is) had not exploited this readily available computational machinery for cognitive ends? Guess what I think?

So much for the “conceptual” arguments. I find these completely compelling, but Randy rightly notes that these have heretofore convinced nobody (but me, it seems, a sad commentary on the state of neuro-science IMO). However, recently, experimental evidence has arisen to support this wonderful idea. I’ve mentioned before some tantalizing bits of evidence (here and here) in favor of this view. Randy reviews some new experimental stuff that quite directly supports his basic idea. He discusses three recent experiments, the most impressive (to me) being work in Norway Sweden (A correction: my mistake and apologies to Nordics everywhere) that shows that the acquired eyeblink response in ferrets “resides within individual Purkinje cells in the cerebellar conrtext” (7). Randy describes their paper (not an easy read for a tyro like me) in detail, as well as two other relevant experiments. It’s a bit of a slog, but given how important the results are, it is worth the effort.

In the popular imagination science is thought of as the careful accumulation of information leading to the development of ever more refined theories. This is not entirely false, but IMO, it is very misleading. Data is useful in testing and elaborating ideas. But, we need the ideas. And, surprisingly, sometimes some hard thinking about a problem can lead to striking novel ideas, and even the dislodging of well-entrenched conceptions.  Chomsky did this in his review of Skinner and subsequent development of generative grammar. Randy’s suggestion that thinking is effectively DNA/RNA/Protein manipulation that takes place within the single cell does this for neuro-science building on the Fodor/Chomsky conception of the mind as a computational symbolic processing engine.  Such a conception demands a Turing like brain architecture.  And, it seems, that nature has provided such at the molecular level. DNA/RNA/proteins can do this sort of stuff. Amazingly, we are starting to find evidence that what should be the case, indeed actually is the case. This is scientific thinking at its most exciting. Read the paper and enjoy.

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[1] This points bears repeating: what Fodor&Pylyshyn and Marcus argued effectively is that connectionist models of the mind were deeply inadequate. They did not argue against such models as brain models (i.e. on the implementation level). Randy extends this criticism to implementational level: not only is connectionism a bad model of the mind, it is also a bad model of the brain. Interestingly, it is a bad model of the brain precisely because it cannot realize the architecture needed to make it a good model of the mind.

[2] If correct, this is an especially powerful argument. It is one thing not to get what others think important, quite another to not even gain much of what you think is so. This is what marks great critiques like Chomsky’s of Skinner, Fodor&Pylyshyn’s and Marcus’ of connectionist mental architectures and Gallistel’s of connectionist models of the brain.

[3] Randy’s criticisms of neural nets as units of computation invites the strong reading that computation only goes on intra-neuronally. But this need not be correct for Randy’s main point to be valid, viz. that lots of computing relevant to cognition goes on inside the neuron, even if some may take place inter-neuronally.