Cephalove

Almost 40 years ago, a video called “The Squid and its Giant Nerve Fiber” was filmed, showing (among others) J. Z. Young and A. L. Hodgkin preparing a squid giant axon for electrophysiological study and demonstrating some experimental techniques. I’ve embedded one clip, but be sure to check out more clips at this course website from Smith College (thanks to whoever teaches Bio 300 there for putting these up, by the way.)

As far as Worldcat knows, this videocassette is only housed in one library in the world – in Massachusetts. If anybody is hanging around Hampshire College and wants to make me a copy of this video, I’d be greatly appreciative.

Let’s take a minute to talk about connectomics.  No, not genomics.  No, not metabolomics.  Not any of the other -omics, but connectomics.  It’s a new-ish field that the computational neuroscience geek in all of us can love.

By way of introduction, the “connectome” is the “network of elements and connections forming the human brain” (according to Sporns et al, 2005).  Let’s forget the part about human brain, and (for the purposes of this post) say that a connectome is the set of all the neural connections in a nervous system.  Connectomics is the subfield of neuroscience that attempts to understand the structure and function of nervous systems by studying these connections as a whole.  The goal of this discipline is to determine the anatomy of the nervous system to a very fine scale (on the order of individual cells), and then relate this structural information to the functioning of the nervous system.  “Solving” a connectome is achieved when the connections between all the neurons in a nervous system are mapped.  This has been done for C. Elegans, a nematode who has only 302 neurons, by the use of electron microscopy – this work is summarized in White et al., 1986.  Since then, we’ve expanded our goals, with the Human Connectome Project aiming to solve the connectome of the human brain.   Let’s step back for a second, though, and ask: why do we want to know all of this?

Since the elucidation of the electrical properties of neurons (which, by the way, started with the squid giant axon – you can read one of my older posts about that topic here ), neuroscientists have been interested in the information that neurons carry.  A nervous system can be thought of as an organ that processes information to tell the rest of the body what to do.  Some stimulus might impinge upon a sensory organ (let’s say, for example, that one sees a car speeding towards one’s self,) which causes a cascade of electrical activity through the nervous system, eventually leading to such diverse effects as the movement of one’s muscles to carry one’s self out of the path of the car, the emotional distress that occurs when one is almost killed by some jerk who isn’t paying attention to the road, and the realization that one has just gotten very lucky.  Later on, one might tell the story of this near miss to her friends over dinner, exhibiting the ability of the nervous system not only to process, but to store information for later use.  Further demonstrating this ability, one might learn not to walk in the middle of the road in the future.  The ability of animals to exhibit behavior, to move, feel, and learn, is all due (according to the dogma of neuroscience) to the processing of information by cells in the nervous system.

Now, it’s relatively routine to study how a single neuron processes information.  To sum it up very briefly, information enters a “typical” neuron in the form of electrical impulses on that neuron’s dendrites.  The information flows through the neuron (as an easy-to-get analogy, imagine electronic information flowing through the wire) and then a decision is made: at any given time, if a neuron is electrically excited enough, it can discharge an action potential.  An action potential is a burst of electrical activity that will travel through the neuron’s axon to affect the activity of other neurons that the axon makes synapses with.  Thus, one can imagine a general flow of information through a neuron from the dendrites, through the cell body, and out the axon.  A nerve cell is diagrammed below, with the dendrites and axon clearly labeled, showing the flow of electrical impulses through the neuron.  Such neurons are linked together to form functional circuits that accomplish complex tasks such as recognizing objects, coordinating movement, and recognizing when food tastes good or bad (to name but a few.)

Think of it this way: each neuron works like a tiny computer processor.  At any given time, it’s integrating all of the electrical signals coming to it, and deciding whether or not to fire an action potential.  Nervous systems can process information because they have many such relatively simple processors connected together to process that information.  (Keep in mind that this is a very simplified, and thus necessarily inaccurate description of the nervous system.  It will have to do for now, however, and gets across those aspects of the function of neurons that are most relevant to the problem of the connectome well enough.)  Thus, to understand the function of a nervous system, one only has to understand the functioning of each of its neurons.  This turns out to be incredibly difficult, and at best we only achieve approximations of this goal.  I’ll come back to this later on.

To study how a single neuron processes information is relatively easy, if we’re selective about which neurons we study.  For example, we could record electrical activity from the optic nerve while we expose the eyes of the animal we’re studying to light.  In this way, we would see the way that different visual signals are encoded by neurons in the optic nerve.  In fact, this has been done countless times in studies on the visual system in cats, frogs, ferrets, and many other species.  Using this method of electrical recording, we can determine how neurons are functionally connected to each other, and how they respond to various inputs.  However, to record electrical information from a neuron requires that one physically place an electrode into the brain, and also requires that one focus on a single neuron or a small subset of neurons at a time.  To figure out how an entire brain functions in terms of the interaction of millions of individual neurons would be all but impossible using these electrophysiological methods.  In addition, this method can never be employed on humans, as it is considered unethical to put electrodes into peoples brains without an urgent medical need (of course!)

Another now-classical way of learning about the connections between neurons in a nervous system is through tracer studies.  In these studies, neurons in one part of the brain are dyed in some way.  Then, other parts of the brain (or the whole brain) can be examined to see if they have dye in them.  If they do, it’s concluded that the neurons make some sort of connection between the part of the brain where the dye was injected and the part of the brain where the dye was later seen.  This has many of the same downfalls as electrophysiological methods of figuring out neural circuits.  For one, it can only be done in a small group of neurons in any preparation, and so the connections in the nervous system must be mapped out piecemeal, a few at a time.  In addition, it is often difficult to tell what route an axon might take from the cell body to its destination, even if it is clear where each of these points are.

The difficulty that these methods have in resolving the microscopic structure of the nervous system beg for a faster, more flexible technique.  Even the reconstruction of the relatively simple nervous system of C. elegans, done using images from an electron microscope, had to be done largely by hand.  These processes are labor- and time-intensive, and do not lend themselves well to the reconstruction of nervous systems that may have millions or billions of neurons.

Enter the field of computational neuroscience.  Computational neuroscientists study nervous systems in terms of their information processing capabilities.  Standing at the junction of computer science and neuroscience, they have both the tools and the impetus to understand the details of the connectomes of whichever organisms they study.

An approach that has been taken in humans involves using the technique of diffusion tensor imaging, and MRI technique that can determine the direction that axons run in in an intact brain.  For example, the following image (by Thomas Schultz) shows a DTI-derived image of the connections that run through the midline of a living human brain:

Such images are of great potential use in studying brain lesions, doing studies on brain function, clinical diagnosis, and whole-brain level analysis of neural circuits.  However, they lack the resolution needed to map individual synapses, thus falling short (for the time being) of being able to comprehensively map the connections between neurons in a brain.  For this, we have to go to microscopy techniques that involve looking directly at neural tissue.  These can only been done in animals, because it is presently illegal to harvest brain tissue from humans for experimentals purposes (again, a no-brainer.)

By now, you’re wondering when I’ll mention a cephalopod.  After all, this is a blog about cephalopods.  You have every right to expect that I’ll mention squids, octopodes, or nautiloids at least once in each post.  Never fear!

Last week I came across a poster presentation write-up on Biomed Central called “Charting out the octopus connectome at submicron resolution using the knife-edge scanning microscope.”  As you might imagine, I was tickled.  A research team with members from Texas, Naple, Michigan, Illinois, California, and Seoul (including Graziano Fiorito, notable for his research on observational learning in the octopus) is working on reconstructing the octopus connectome using a mostly-automated 3D microscope called the knife-edge scanning microscope (KESM).  This microscope takes a block of tissue and slices it, taking a picture of each slice of the tissue as it is cut.  Then, a computer program can create a high-resolution 3D image of the tissue.  From this, the computer can (and this is the tricky part) automatically trace the paths that nerve cells take through the tissue, and – this being the goal of this research – reconstruct a detailed network showing the morphology of each neuron in the tissue.  For examples of the resulting images, you can see this gallery from the brain networks laboratory at Texas A&M.

Why the octopus?  Well, in an introduction that makes the comparative neuroanatomist in me jump for joy, the authors suggest that because “the neural architecture of this cephalopod mollusk differs markedly from that of any vertebrate… [investigating] the difference and simlarities between the neural architecture – or connectome – of the octopus and mammals, such as the mouse, may lead to deep insights into the computational principles underlying animal cognition.”  In their concluding remarks, the authors note that they “expect that this pilot study and the more detailed investigations to follow will allow fruitful comparisons of the neural circuitries of individual octopuses with different ecological life histories, as well as of animals that have been exposed to a variety of neurodegenerative insults… In sum, this approach should contribute greatly to our understanding of the computational architecture of invertebrates and ultimately provide insights into the differences between invertebrate and vertebrate cognitive capabilities.”

I’m intriguied by this article, but also a little dissappointed.  Mostly, I’m dissappointed that a more complete study isn’t out yet!  I’ll be watching these guys from now on, and I’ll cover any other publications they put out on the topic.  Hurrah for octopus connectomics!

In closing, I want to mention that a complete neuroanatomical picture of a nervous system does not actually explain its computational properties.  To understand how nervous systems process information, we need to know the physiology of each cell and the biochemistry of the interactions, a topic that is probably more complex than even the very fine-grained study of neuroanatomy represented by the studies I’ve mentioned here.  In terms of our understanding of nervous systems, however, connectomics offers and opportunity to study the relationship between the cellular structure of the nervous system and its overall capabilities – a relationship whose description has been one of the goals of neuroscience practically since its inception.

Thanks for reading!

Sporns, O., Tononi, G., & Kötter, R. (2005). The Human Connectome: A Structural Description of the Human Brain PLoS Computational Biology, 1 (4) DOI: 10.1371/journal.pcbi.0010042

Yoonsuck Choe, Louise C Abbott, Giovanna Ponte, John Keyser, Jaerock Kwon, David Mayerich, Daniel Miller, Donghyeop Han, Anna Maria Grimaldi, Graziano Fiorito, David B Edelman, & Jeffrey L McKinstry (2010). Charting out the octopus connectome at submicron resolution using the knife-edge scanning microscope BMC Neuroscience, 11 (Supplement 1), 136-137 : 10.1186/1471-2202-11-S1-P136

MAYERICH, D., ABBOTT, L., & McCORMICK, B. (2008). Knife-edge scanning microscopy for imaging and reconstruction of three-dimensional anatomical structures of the mouse brain Journal of Microscopy, 231 (1), 134-143 DOI: 10.1111/j.1365-2818.2008.02024.x

White, J., Southgate, E., Thomson, J., & Brenner, S. (1986). The Structure of the Nervous System of the Nematode Caenorhabditis elegans Philosophical Transactions of the Royal Society B: Biological Sciences, 314 (1165), 1-340 DOI: 10.1098/rstb.1986.0056

MORI, S., & ZHANG, J. (2006). Principles of Diffusion Tensor Imaging and Its Applications to Basic Neuroscience Research Neuron, 51 (5), 527-539 DOI: 10.1016/j.neuron.2006.08.012

          (Note: I apologize if this post seems jargon-ey.  I’ve tried to explain or reference any hard to get terms, but I do assume that readers know the very basics of neural functioning.  If you need a primer on this, check out wikipedia’s page on neurons or this great tutorial.  Feel free to post in the comments if there’s anything you want explained more thoroughly, and I’ll give it a crack.)

          The Octopus research group in Jerusalem is back with a paper in the August issue of Neuroscience about the function of serotonin in the octopus vertical lobe, Serotonin is a facilitatory neuromodulator of synaptic transmission and “reinforces” long-term potentiation induction in the vertical lobe of Octopus vulgaris.  I’m very excited to blog about this paper – it’s the very first time in my short blogging career that I’ve gotten to cover a study as it was coming out!  You can read my other posts about their work here and here (that second one has a basic description of the technique of stimulation-induced LTP, which I’ll be very brief with here.)

          Basically, LTP (long-term potentiation) is one of the mechanisms by which neurons are thought to adjust how they connect to each other during the process of learning – specifically, they become stronger (or potentiated,) meaning that signals are carried across the synapse more effectively.  The authors of this paper use a technique by which they induce LTP in synapses in the octopus vertical lobe (a structure thought to be involved in learning and memory) and study the effects of serotonin (also called 5-HT, which is short for 5-hydroxytryptamine, the terminology I’ll be using from now on) on the properties of the induced LTP.  Presumably, this can tell us something about the function of 5-HT in the normal functioning of the vertical lobe, although this point is very debatable.

          Why look at 5-HT?  Well, for starters, it’s one of the big neurotransmitters these days (along with such illustrious nearly-lay-term chemicals as dopamine, norepinephrine, GABA and glutamate.)  You hardly need to have a specific reason to study it these days because it’s involved in pretty much every process that contemporary neurobiology cares about: consumptive behavior, mood and depression, social cognition, the action of addictive drugs.  More than that, though, it’s conserved across all bilaterians, the group of bilaterally symmetrical animals including people, the rest of the vertebrates, the insects, and, among many others, the molluscs!  If there is any neurotransmitter that is interesting to study comparatively, it’s 5-HT, as it’s been shown to be involved in learning in animals as distantly related to each other as sea slugs, rats, humans, and (now) cephalopods.  If we learn how 5-HT does its job in a wide variety of animals, it will help us understand how neurotransmitters function within nervous systems in general.  This is, we will hopefully agree, a Good Thing. 

          The authors begin with the hypothesis that, as has been shown in Aplysia (a beautiful little sea slug who is relatively widely studied in neuroscience,) 5-HT probably has a role in the modulation of LTP rather than inducing it directly, making it a putative neuromodulator.  It is not hard to imagine how this might be a good thing to have in a memory system.  Let’s pretend that our animals has just been injured, or that it has just found a great big source of food.  All of these events call for a general upregulation in the formation of memories, since remembering what happened around these events will help the animal repeat or avoid them in the future, depending on whether they were good or bad.  If a chemical can increase the amount of LTP (a process thought to be involved in learning,) it would make sense that it might be selectively secreted or expressed during times when the animal’s memory system needs to pay attention to what’s going on, and not when there is nothing of consequence happening.  This is an extremely limited view of the role of neuromodulators in learning, but it illustrates the principal as well as I know how to.  In short, neuromodulators, while not responsible for neurotransmission and plasticity themselves, have some effect on it.  This sort of effect is one of the things that allows the great flexibility of neural systems, one of their key features.

          In the first part of their study, the authors stained slices of the octopus vertical lobe for 5-HT, and then described what they say – this is good old fashioned neuroscience.  They found that 5-HT shows up in fibers from the medial superior frontal lobe (MSF) that innervate large areas of the vertical lobe.  The MSF is thought to be one of the main sources of input of sensory information to the vertical lobe, and this tract of fibers (known as the MSF-VL tract) is thought to be involved in the formation of sensory memories in the octopus, as per J. Z. Young’s early lesion experiments in the octopus.  The authors note that this wide spread of 5-HT is typical of neuromodulators, supporting the idea that MSF neurons use 5-HT to modulate LTP in the vertical lobe.

          In the second part of the study, the authors use a technique where they induce LTP in live slices of octopus brain (cool, right?) by repeatedly stimulating the axons running from the MSF to the vertical lobe.  They measure the “strength” of neurotransmission as fPSP’s, or synaptic field potential, which is roughly an indicator of how much electrical activity is generated by activity in many synapses within a small area of the tissue.  I’ll only summarize one of their several experiments here, because it is the one that really illustrates the neuromodulatory effect.

          This figure shows the results of an experiment using induced LTP in octopus brain slices.  The experimenters stimulated the brain slices along the MSF-VL tract and recorded the resultant electrical activity in the VL.  Let’s start with the first graph.  The y-axis shows the amount of activity recorded in the vertical lobe after a very small electrical stimulation (this is what each data point is.)  The x-axis shows the time from the beginning of the experiment.  At about 30 minutes, MSF-VL neurons were stimulated with a “triplet”, which consisted of three pulses in quick succession.  As we can see in the control preparation (the blue line,) this w pas not enough to induce LTP, which would be evident as an increase in the field potential.  In a preparation treated with 5-HT, however, this stimulation was enough to elicit some LTP, which is apparent as a stable elevation of the recorded f
ield potential at times 50 and 60 minutes.  After 60 minutes, each preparation was subject to high-frequency stimulation, which caused maximal LTP in both cases.   The bar graph next to it (B) shows the results of multiple experiments, showing that before high-frequency stimulation, the treatment with 5-HT caused an increase in the LTP resulting from the triple-pulse, indicating that the presence of 5-HT made MSF-VL synapses prone to undergo LTP.  The second line graph (C) shows the results of a set of similar experiments, except that the stimulation was done once per minute.  As is apparent, treatment with 5-HT (shown by the red bar) increased the rate of LTP; however, as indicated in the adjacent bar graph (D), it did not increase the maximum amplitude of LTP.

          It’s important to remember that in the active nervous system, it’s unlikely that synapses are ever stably at a maximal strength.  That increase in the rate of induction of LTP, modest though it may seem in this experiment, could be crucial in affecting the functioning of a memory system in a behaving animal.  In the “real world”, the stimuli involved in learning are often only present for a short time, and the state of any particular synapse in the nervous system is determined by an incredibly complex set of chemical factors.  Neuromodulatory activity (like that argued for in this paper) provides a sensitive mechanism by which the functioning of a neural system could be finely coordinated, allowing the integration of a variety of information into one system that can make a timely decision about whether an action was good enough to repeat or bad enough to avoid in the future.

          For convenience’s sake, I skipped a variety of other interesting experiments that the authors did, and I encourage you to get the paper yourself and read it, if you can.  I very much like this type of research, and I like the challenge that blogging about it presents.  Anyways, I hope you’ve enjoyed this as much as I have!

          Thanks for reading!

Shomrat T, Feinstein N, Klein M, & Hochner B (2010). Serotonin is a facilitatory neuromodulator of synaptic transmission and “reinforces” long-term potentiation induction in the vertical lobe of Octopus vulgaris. Neuroscience, 169 (1), 52-64 PMID: 20433903

          I’ve heard the assertion that octopuses have short- and long-term memories several times in the past few days, mostly in discussions of the ethics of eating octopuses prompted by ethical questions raised about Paul, the famous German octopod.  It’s interesting to me what these people don’t say – that they think that having a multiphasic memory process makes octopuses worth not eating (because, well, people have multiphasic memories, and you wouldn’t eat them, would you?!?  Sicko.)  While I don’t think that memory capacity of an animal is associated in an uncomplicated way with its ability to suffer or its moral status, it seems to me like a nonetheless interesting question.  I’m almost sure that most of the people who use (read: copy and paste) this bit of information to support their beliefs have very little idea of what sort of research is behind it.  Let’s face it: developing a working knowledge of behavioral research on cephalopods is something that just isn’t on most of the public’s mind.  In fact, until I began writing this blog, I had very little knowledge of the subject.  I plan to set the record straight, so that internet users need never make an unfounded or unqualified statement about memory processes in cephalopods again (a lofty goal, huh?)

          If you don’t know octopus neuroanatomy very well (and who does?) you might want to check out the figures in this post.  I’ll be talking about the vertical and superior frontal lobes of the octopus brain, and I know it sometimes helps to be able to visualize things like that when you’re reading about them.  Just so that it’s clear: the term “biphasic memory” means that the memory system in question has two discrete parts or processes (ie. short-term and long-term memory.)  A monophasic memory would have only one process, so that memories would last for a certain amount of time and then fade similarly in all circumstances.  A multiphasic memory system (which could be biphasic, triphasic, or more) is a general term to describe memory systems that are clearly more than monophasic, but are not completely characterized yet – and no memory system is.  Now, on to the research!

          J. Z. Young, that demigod of cephalopod neurobehavioral research, published one of the few papers I could find on this topic back in 1970, following up on his earlier work on the subject.  In it, he investigated the development of short and long term memory in O. vulgaris (I assume – he doesn’t actually mention what species he uses in this paper, but he almost always used O. vulgaris) as well as the role of two brain areas in memory, the median superior frontal lobe (MSF) and the vertical lobe (VL).  To do so, he performed surgeries to remove one of these two areas of octopuses’ brains and put them through a learning task.  In this task, octopuses were trained to either attack a rectangle (rewarded with a piece of fish) or withhold attacking a crab (which was punished with electric shock.)

          It turned out that octopuses whose vertical lobes had been removed were greatly impaired in learning to attack the rectangle.  Young explains this by claiming that the vertical lobe is involved in short-term memory, and that the acquisition of stable behavior day-to-day was impaired because the animals without vertical lobes could not remember events long enough for the training to be effective.  The animals without median superior frontal lobes, however, learned the task just fine, but were impaired in their long-term retention of it., suggesting that the MSF lobe might have some role in retaining learned information.  Interestingly, Young also found (in other experiments) that removing the vertical lobe after a task was learned resulted in a greater retention of the task.  These results suggest that the vertical lobe plays a role in the updating of memory stores, but is not absolutely essential for the recall of memories.

          His results from the attack-withholding task were less clear, but they suggest that animals with lesions, especially those with vertical lobe lesions, were less consistent than intact animals in learning not to attack a crab after being shocked each time they attacked it.

          Basically, Young argues (on the basis of this and some of his other experiments) that octopuses have a memory system that can be disrupted in more than one way; that is, it is possible to dissociate memory acquisition from long term retention, just like in vertebrates.  For the most part, more current research has agreed with his position, as we’ll see in this next paper.

          Moving forward (past a lot of great research that I’ll skip over for the sake of brevity) to 2008, Shomrat et al. used electrophysiological methods to test this hypothesis.  Before we get into their methods, let’s look a bit more closely at the system that we are talking about (this figure is from Shomrat et al. (2008)):

          On the left is a sagittal slice of the supraoesophageal (over-the-oesophagus) mass of the octopus brain.  On the right is a diagram of the memory system in question.  Sensory information flows into the MSF from the arms and eyes before being sent along to the VL.  The VL neurons in turn send out information encoding attack.  It’s been established that long-term potentiation (LTP) can occur in this area of the octopus brain, and this is a likely mechanism for the formation of memories in octopus (I blogged about this here – check it out if you need a little more background.)

          The authors’ procedure went as so: O. vulgaris who had already been trained to attack a white ball either had their MSF tract cut (at the dashed line in each image,) severing the sensory input to the vertical lobe, or this tract was stimulated, causing LTP at the synapses indicated in the figure.  Shortly after the procedure, the animals were trained to avoid a red ball through electric shock.  It was found that animals with severed MSF tracts were slower than controls to learn to withhold attack, while animals in whom LTP was induced were quicker.  This is all well and good – it confirms what we already thought about the role of the vertical lobe in acquiring memories in the octopus.  The really important result from this paper came when the authors tested the octopuses a day later.  It was found that both MSF tract transection and LTP induction impaired recall after 24 hours.  So even though stimulation of the MSF tract improved short-term memory (presumably by hyper-activating the memory system in the vertical lobe,) it impaired long-term memory.  This suggests that these two processes are not identical; that is, that octopuses have discrete and dissociable short- and long-term memory circuits.  This general finding has been replicated in cuttlefish (see my post on cuttlefish memory
) and nautiluses (Crook and Basil, 2008).

          Unfortunately, that’s just about all that we know at this point: that cephalopods appear to have biphasic memories, meaning that the behavioral evidence of short-term memories can be dissociated from that of long-term memories.  This is hardly (by itself) a basis on which we can imply any sort of consciousness or advanced cognitive capacity, as animal-rights supporters who mention this fact seem to imply.

          In interpreting these results in the context of our knowledge of cephalopods as a whole, we should keep in mind what is meant by short- and long-term memory in humans.  Short-term memory is what happens when newly learned information is bouncing around the cortex somewhere, being continually processed but not permanently encoded somewhere.  These memories will disappear if they are not rehearsed (or otherwise actively retained).  Long-term memory has been (relatively permanently) encoded into neural circuits, so that it can be retrieved after periods when it has not been actively processed in short-term (or working) memory circuits.  These processes have been studied intensely in humans, and can be precisely because we have a complex cognitive system build around them (or on top of or parallel to them, depending on who you ask) that we can access.  As of yet, we don’t have the experimental techniques to assess exactly how “human-like” or “vertebrate-like” cephalopod memory systems are, because we can’t study them in nearly as much detail as language-based and other cognitive tasks allow us to in humans.  Thus, making any strong conclusions about the nature of cephalopod memory other than that it appears to be multiphasic (with no implied “and-so-cephalopods-are-smart-like-people”) is untenable.

          Lastly, I find it frustrating that animal rights activists use our (very primative) knowledge of cephalopod memory systems to try to support their position that eating cephalopods is wrong.  Not only is it an inconclusive (what does memory have to do with suffering and morality?) and nonspecific argument (did anybody think that ungulates, swine and birds don’t have complex memory systems?), but it misses some of the big points that the animal rights movement has taught us.  First of all, it implies that cephalopods are somehow special because they are intelligent and human-like.  However, having compassion for animals explicitly demands that we not judge their worth by analogy to our own abilities – this has proved to be an attitude that encourages cruelty to animals simply because we are ignorant of them and their behavioral and cognitive capacities.  If we didn’t know about cephalopod memory systems, would they still be worth defending from fishing and consumption as food?  Hopefully, the answer is yes – so why try to use this (admittedly inadequate) argument now that we conveniently have information that appeals to one’s emotional predispositions?  I find this to be irresponsible and counter-productive, as it diminshes the credibility of other, more valid arguments against the consumption of cephalopods (or any animal, for that matter) that animal rights activists might use.

          Sorry if this was a bit heavy on editorial material.  Being very concerned about animal welfare myself, I get annoyed when people make the cause look stupid by saying things that are ill-informed, ill-reasoned, or just plain wrong.  Although I wish that people would stop killing cephalopods for food, spinning information to try to get people to agree with a point is dishonest, and at best a very poor strategy for debate, as there’s bound to be at least one attentive person on the other side who will point out that you’re not being true to the facts – and nobody will listen to you after that.

Thanks for reading!

SHOMRAT, T., ZARRELLA, I., FIORITO, G., & HOCHNER, B. (2008). The Octopus Vertical Lobe Modulates Short-Term Learning Rate and Uses LTP to Acquire Long-Term Memory Current Biology, 18 (5), 337-342 DOI: 10.1016/j.cub.2008.01.056

J. Z. Young (1970). SHORT AND LONG MEMORIES IN OCTOPUS AND THE INFLUENCE OF THE VERTICAL LOBE SYSTEM Journal of Experimental Biology (52), 385-393

Crook, R., & Basil, J. (2008). A biphasic memory curve in the chambered nautilus, Nautilus pompilius L. (Cephalopoda: Nautiloidea) Journal of Experimental Biology, 211 (12), 1992-1998 DOI: 10.1242/jeb.018531