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.

Gallistel rules

There is still quite a bit of skepticism in the cog-neuro community about linguistic representations and their implications for linguistically dedicated grammar specific nativist components. This skepticism is largely fuelled, IMO, by associationist-connectionist (AC) prejudices steeped in a nihilistic Empiricist brew.  Chomsky and Fodor and Gallistel have decisively debunked the relevance of AC models of cognition, but these ideas are very very very (very…) hard to dispel. It often seems as if Lila Gleitman was correct when she mooted the possibility that Empiricism is hard wired in and deeply encapsulated, thus impervious to empirical refutation. Even as we speak the default view in cog-neuro is ACish and that there is a general consensus in the cog-neuro community that the kind of representations that linguists claim to have discovered just cannot be right for the simple reason that the brain simply cannot embody them.

Gallistel and Matzel (see here) have deftly explored this unholy alliance between associationist psych and connectionist neuro that anchors the conventional wisdom. Interestingly, this anti representationalist skepticism is not restricted to the cog-neuro of language. Indeed, the Empiricist AC view of minds and brains has over the years permeated work on perception and it has generated skepticism concerning mental (visual) maps and their cog-neuro legitimacy.  This is currently quite funny for over the last several years Nobel committees have been falling all over themselves in a rush to award prizes to scientists for the discovery of neural mental maps. These awards are well deserved, no doubt, but what is curious is how long it’s taken the cog-neuro community to admit mental maps as legit hypotheses worthy of recognition.  For a long time, there was quite a bit of excellent behavioral evidence for their existence, but the combo of associationist dogma linked to Hebbian neuro made the cog-neuro community skeptical that anything like this could be so. Boy were they wrong and, in retrospect, boy was this dumb, big time dumb!

Here is a short popular paper (By Kate Jeffery) that goes over some of the relevant history. It traces the resistance to the very idea of mental maps stemming from AC preconceptions. Interestingly, the resistance was both to the behavioral evidence in favor of these (the author discusses Tolman’s work in the late 40s. Here’s a quote (5):

Tolman, however, discovered that rats were able to do things in mazes that they shouldn’t be able to do according to Behaviourism. They could figure out shortcuts and detours, for example, even if they hadn’t learned about these. How could they possibly do this? Tolman was convinced animals must have something like a map in their brains, which he called a ‘cognitive map’, otherwise their ability to discover shortcuts would make no sense. Behaviourists were skeptical. Some years later, when O’Keefe and Nadel laid out in detail why they thought the hippocampus might be Tolman’s cognitive map, scientists were still skeptical.

Why the resistance? Well ACism prevented conceiving of the possibility.  Here’s how Jeffery put it (5-6).

One of the difficulties was that nobody could imagine what a map in the brain would be like. Representing associations between simple things, such as bells and food, is one thing; but how to represent places? This seemed to require the mystical unseen internal ‘black box’ processes (thought and imagination) that Behaviourists had worked so hard to eradicate from their theories. Opponents of the cognitive map theory suggested that what place cells reveal about the brain is not a map, so much as a remarkable capacity to associate together complex sensations such as images, smells and textures, which all happen to come together at a place but aren’t in themselves spatial.

Note that the problem was not the absence of evidence for the position. Tolman presented lots of good evidence. And O’Keefe/Nadel presented more (in fact enough more to get the Nobel prize for the work). Rather the problem was that none of this made sense in an AC framework so the Tolman-O’Keefe/Nadel theory just could not be right, evidence be damned.[1]

What’s the evidence that such maps exist? It involves finding mental circuits that represent spatial metrics, allowing for the calculation of metric inferences (where something is and how it is from where you are). The two kinds of work that have been awarded Nobels involve place cells and grid cells. The former involve the coding of direction, the latter coding distance. The article does a nice job of describing what this involves, so I won’t go into it here.  Suffice it to say, that it appears that Kant (a big deal Rationalist in case you were wondering) was right on target and we now have good evidence for the existence of neural circuits that would serve as brain mechanisms for embodying Kant’s idea that space is a hard wired part of our mental/neural life. 

Ok, I cannot resist. Jeffery nicely outlines he challenge that these discoveries pose for ACism. Here’s another quote concerning grid cells (the most recent mental map Nobel here) and how badly it fits with AC dogma (8):[2]

The importance of grid cells lies in the apparently minor detail that the patches of firing (called ‘firing fields’) produced by the cells are evenly spaced. That this makes a pretty pattern is nice, but not so important in itself – what is startling is that the cell somehow ‘knows’ how far (say) 30 cm is – it must do, or it wouldn’t be able to fire in correctly spaced places. This even spacing of firing fields is something that couldn’t possibly have arisen from building up a web of stimulus associations over the life of the animal, because 30 cm (or whatever) isn’t an intrinsic property of most environments, and therefore can’t come through the senses – it must come from inside the rat, through some distance-measuring capability such as counting footsteps, or measuring the speed with which the world flows past the senses. In other words, metric information is inherent in the brain, wired into the grid cells as it were, regardless of its prior experience. This was a surprising and dramatic discovery. Studies of other animals, including humans, have revealed place, head direction and grid cells in these species too, so this seems to be a general (and thus important) phenomenon and not just a strange quirk of the lab rat.

As readers of FL know, this is a point that Gallistel and colleagues have been making for quite a while now and every day the evidence for neural mechanisms that code for spatial information per se grows stronger. Here is another very recent addition to the list, one that directly relates to the idea that dead-reckoning involves path integration. A recent Science paper (here) reports the discovery of neurons tuned to vector properties. Here’s how the abstract reports the findings:

To navigate, animals need to represent not only their own position and orientation, but also the location of their goal. Neural representations of an animal’s own position and orientation have been extensively studied. However, it is unknown how navigational goals are encoded in the brain. We recorded from hippocampal CA1 neurons of bats flying in complex trajectories toward a spatial goal. We discovered a subpopulation of neurons with angular tuning to the goal direction. Many of these neurons were tuned to an occluded goal, suggesting that goal-direction representation is memory-based. We also found cells that encoded the distance to the goal, often in conjunction with goal direction. The goal- direction and goal-distance signals make up a vectorial representation of spatial goals, suggesting a previously unrecognized neuronal mechanism for goal-directed navigation.

So, like place and distance, some brains have the wherewithal to subserve vector representations (goal direction and distance). Moreover, this information is coded by single neurons (not nets) and is available in memory representations, not merely for coding sensory input. As the paper notes, this is just the kind of circuitry relevant to “the vector-based navigation strategies described for many species, from insects to humans (14–19)— suggesting a previously unrecognized mechanism for goal-directed navigation across species” (5).

So, a whole series of neurons tuned to abstracta like place, distance, goal, angle of rotation, and magnitude that plausibly subserve the behavior that has long been noted implicates just such neural circuits. Once again, the neuroscience is finally catching up with the cognitive science. As with parents, the more neuro science matures the smarter classical cognitive science becomes.
Let me emphasize this point, one that Gallistel has forcefully made but is worth repeating at every opportunity until we can cleanly chop off the Empiricist zombie’s head. Cognitive data gets too little respect in the cog-neuro world. But in those areas where real progress has been made, we repeatedly find that the cog theories remain intact even as the neural ones change dramatically. And not only cog-neuro theories. The same holds for the relation of chemistry to physics (as Chomsky noted) and genetics to biochemistry (as Gallistel has observed). It seems that more often than not what needs changing is the substrate theory not the reduced theory. The same scenario is being repeated again in the cog-neuro world. We actually know very little about brain hardware circuitry and we should stop assuming that ACish ideas should be given default status when we consider ways of unifying cognition with neuroscience.

Consider one more interesting paper that hits a Gallistel theme, but from a slightly different angle. I noted that the Science paper found single neurons coding for abstract spatial (vectorial) information. There is another recent bit of work (here) that ran across my desk[3] that is also has a high Gallistel-Intriguing (GI) index.

It appears that slime molds can both acquire info about their environment and can pass this info on to other slime molds. What’s interesting is that these slime molds are unicellular, thus the idea that learning in slime molds amounts to fine tuning a neural net cannot be correct. Thus whatever learning is in this case must be intra, not inter-neural.  And this supports the idea that one has intra cellular cognitive computations. Furthermore, when slime molds “fuse” (which they apparently can do, and do do) the information that an informed slime mold has can transfer to its fused partner. This supports the idea that learning can be a function of the changed internal state of a uni-cellular organism.
This is clearly grist for the Gallistel-King conjecture (see here for some discussion) that (some) learning is neuron, not net, based. The arguments that Gallistel has given over the years for this view have been both subtle, abstract and quite arm-chair (and I mean this as a compliment). It seems that as time goes by, more and more data that fits this conception comes in. As Gallistel (and Fodor and Pylyshyn as well) noted, representational accounts prefer certain kinds of computer architectures over others (Turing-von Neumann architectures). These classical computer architectures, we have been told, cannot be what brains exploit. No, brains, we are told repeatedly, use nets and computation is just the Hebb rule with information stored in the strength of the inter-neuronal connections. Moreover, this information is very ACish with abstracta at best emergent, rather than endogenous features of our neural make-up. Well, this seems to be wrong. Dead wrong. And the lesson I draw form all of this is that it will prove wrong for language as well. The sooner we dispense with ACism, the sooner we will start making some serious progress. It’s nothing but a giant impediment, and has proven to be so again and again.

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[1] This is a good place to remind you of the difference between Empiricist and empirical. The latter is responsiveness to evidence. The former is a theory (which, IMO, given its lack of empirical standing has become little more than a dogma).

[2] It strikes me as interesting that this sequence of events reprises what took place in studies of the immune system. Early theories of antibody formation were instructionist because how could the body natively code for so many antibodies? As work progressed, Nobel prizes streamed to those that challenged this view and proposed selectionist theories wherein the environment selected from a pre-specified innately generated list of options (see here). It seems that the less we know, the greater the appeal of environmental conceptions of the origin of structure (Empiricism being the poster child for this kind of thinking). As we come to know more, we come to understand how rich is the contribution of the internal structure of the animal to the problem at hand. Selectionism and Rationalism go hand in hand. And this appears to be true for both investigations of the body and the mind.

[3] Actually, Bill Idsardi feeds me lots of this, so thx Bill.

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.