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From Neuron to Memory System: How Memory Might Work
Graeme Smith
Friday, 12 June 2009 04:34 UTC
How does memory work?
We think we know, based on thousands of years of research into it, but there are still indications that we might be completely wrong. Scientists created computers with the best scientific approach to memory based on current scientific theories, in the 1940’s, and they got it wrong. Most neuroscientists today will tell you that the computer is nowhere close to a good model of memory, yet many of the attitudes that informed the decisions on how to make computer memories remain unchallenged. Our popular theories of how memory works were laid down in the middle ages, by people who thought memory was a fluid, and that the brain was a reservoir. Somehow they thought, we opened valves, and let the memory flow from the brain to where it was needed. I call this a demand model of memory because you demand the memory by opening the valves. In computing terms we have replaced the fluid theory of electricity but retained the idea of current, and at one time tubes which were replaced with transistors were called Valves. So computer memories could be seen to be operating in a demand memory model as well.
As Jerry A. Fodor said in The Mind Doesn’t Work That Way!:The scope and limitations of Computational Psychology Neither Phenomenal approaches such as Neural Networks nor strict computational approaches such as Truth Preserving Functions, seem likely to achieve a suitable computational model of consciousness.
As a researcher in Artificial Consciousness, my main thrust is to eventually get support for my own Artificial Consciousness to be built. However to get there, I had to start with a model of memory. I chose to hedge my bets, to begin with a Neural Model, and add Functional support to that model, where it was needed. Further I wasn’t going to limit myself to Truth Preserving Functions, where Soft computing would be more effective. This type of approach to consciousness is called a Hybrid approach. To start off, however I wanted a model of memory, and one of the things I had to do, to get there was overcome Fodors assurance that no-one had yet developed a phenomenal version of a demand memory.
Those who are stuck on William James idea that phenomenal systems can’t be built based on what we know about the brain, have problems with my use of the word phenomenal in the way that Jerry Fodor used it in his book. They like to look for exotic things like QM entanglement to explain the fact that some things just seem indivisible. I however think that there are indivisible elements in the memory but that it doesn’t matter because the mind doesn’t try to divide them, it finesses the system, without that effect.
Fodor’s claim is not without merit, phenomenal systems are definitely easier to build out of neural networks, than demand memories. However, David Marr defined a type of memory he thought might help explain the cerebral neocortex back in 1970, and by understanding what his work uncovered, and brushing off some of the unfortunate assumptions of his day, I think I have undercovered one of the hidden biases that is keeping us from understanding the way the brain, or at least the memory works. And that bias is the assumption that explicit (demand Model) memory, is the natural state of the memory system, and that anything that doesn’t fit the model must be an add on to the basic explicit memory model. So we expect Implicit Memory to somehow be an add-on to the essential demand memory, and we are wrong.
What Marr described in 1970 is a type of memory more basic than explicit memory when implemented in neurons. To computer guys like me, this seems counterintuitive. How can anything be more basic than a good demand memory, we can implement dynamic ram with a capacitor, a resistor, and a transistor, how much more basic can you get? But what we keep forgetting is that neurons don’t work like transistors, there is no technological overlap. Within the logic that makes neural networks work, demand memory is much more expensive to build than a simple content addressable memory. That is what Marr claimed his 4 layer CODON was, was a self-classifying content addressable memory.
To understand why this might be, we need a little theory. Although in the 80’s and 90’s the connectionist school was over-run by the Parallel Distributed Processing guys from the A.I. discipline, and so we have to take some of its theory as being deliberately misleading to steer people away from trying to understand real neural systems, The basics are fairly equivalent. A neuron is first of all a cell, it has to survive like other cells, by absorbing nutrients and getting rid of wastes. However at some point in the evolution of animals, neurons gave up some of their survival functions to a helper cell called a glial cell, and converted those functions over into mechanisms for transferring information between cells.
The Parallel Distributed Processing guys figured what was important to this communication was the firing of the cell. Sorry that is a misconception, firing just speeds the process of transferring information up, it is not the only mode of transfer, nor is it the most important one for understanding natural systems based on nerve cells. However because the PDP boys wrote the manuals for the industry, they got to tell 20 years of modelers what to think. As a result of mistakes like this, they set the neural modeling of Natural Neural Networks back, to the point where Gary Marcus, has clearly stated that he feels it necessary to do a hostile takeover of the connectionist school.
One approach that the PDP guys thought was dangerous was David Marr’s attempt to use probability Mathematics to define a circuit that was made up of heterogeneous neurons. Despite the fact that they did not have the ability to model heterogeneous groups of neurons, a direct attack on his claiming that the heterogeneous group he called a codon was a content addressable memory, was made pointing out that the model did not exibit wave-forms similar to the real cortex.
This attack, assumed that Marr’s circuit had to have frequency artifacts similar to the real cortex in order to suggest the role of content addressable memory for the cerebral neocortex. Well Marr was a pioneer in the field and he got some things wrong, and others even more wrong, but it was a seminal stage in the science, and what he got right is more important.
Marr was able to use probability mathematics to analyze heterogeneous networks of neurons and predict their function. Nobody before that even tried, and after the way he was treated few after him had the nerve to try again.
I am not going to start spouting probability equations, if only because I don’t follow his math. But the PDP guys called a neuron a processor that had input, process and output capabilities. I however look at it from a different perspective I see a neuron that has storage, processing, and transfer capabilities. And when I look at the 4 layer cortex model that Marr worked with, I also see a content addressable memory, but I think that perhaps he expected too much equivalency between neurons in the connections department, and was sadly dissapointed before his death in 1980. So I wonder at how much self-classification the system is doing.
If you look at neurons from my viewpoint you begin to see that the shape of the neuron has a logic, that memory neurons have lots of synapses, and opportunistic process growth, that processing neurons tend to have complex and often bushy or mossy dendrites, and that transport neurons tend to have virtually no division at the dendrite level and form long thin neurons.
If you look at the type of organs that are produced, Memory Neurons tend to form tissues, Processing Neurons tend to form globular organs, and transport neurons tend to form fibrous bundles. In other words the shape of the neuron, indicates it’s function, and the shape of the organ might indicate to some small extent what type of function it performs.
Looking at Marr’s 4 layer model, we see that the first layer is processing neurons, the second and third layer are memory neurons, and the fourth layer is dominated by processing neurons again. The whole structure is probably a memory role because it is a tissue.
Stimuli coming in at the first layer, are processed affecting their storage in the second and third layers, and the 4th layer probably is mostly control neurons, and in some cases inputs of senses that have been directed through the thalamus. These latter signals condition the general inhibitive environment caused by the Martinotti cells and thus reduce the inhibition at the first layer encouraging the layer 2/3 neurons to fire.
This simple memory circuit can be seen to be designed to respond to either signals from layer 4 or layer 1, and the first layer is used to train the second and third layers to respond to patterns of stimulus. Given a stimulus each pyramidal cell in layer 2/3 decides whether or not to fire, and this type of system is exactly what is needed for a content addressable memory.
It is also exactly wrong for an explicit memory suggesting that it must be the explicit memory that is the add on. Indeed when we look at the micro-architecture we see that a type of tissue called Allocortical tissue makes up the tissues at the bottom of the Sulcys and Divides of the brain. and since Sulcys and Divides are a later architectural configuration there might be an indication that the six layer tissue called isocortical tissue, that pushes the gyruses up away from the sulcys and divides, was a specialization of the brain.
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Sorry about the Jargon, sometimes well most of the time, actually, it is easier to use a word than to explain it.
Part of the problem I have with your characterization of phenomenal stuff, is that there seems to be a missing step, you say we experience stuff, and then some part of it remains in our memory. But the missing step, is that we don’t know we experience stuff, until we tell ourselves we are experiencing it, and that is much later in the process, than what I am talking about with this early form of memory. I think of it this way, experiencing stuff, is a process that has a start and an end. Near the end of experiencing stuff we become aware that we are experiencing stuff, but near the beginning of the process we are already creating memories, and storing them in order to have meaning to the stuff we experience.
So, I would say that part of the Ontology, of the Philosophers View of Phenomenal stuff, is the error of assuming that when we tell ourselves that we are experiencing stuff, that is the first point at which processing happens, and it is only after that the stuff gets stored.
I think that this is similar to the fallacy that makes Libett’s work so controversial, the assumption that when we tell ourselves that we are making a decision is the causal point for making that decision, so Libett’s work that shows that processing is ongoing significantly previous to the awareness of starting to make a decision, is consdered somehow wrong.
I think this is just another case where Naive Realism trips over the fact that it feels to us as if we had just begun to make the decision, when we first became aware of starting to make the decision.
Naive realism is the recognition by philosophy that we operate as if we had a single Point of View, even though we know that because of stereo vision we actually integrate two points of view into a larger visual field and depth perception. I think this is just another case where philosophy has been tricked by the subjective experience of volition, into thinking that it is bounded on one side by the awareness of the pending decision, and tricked by the subjective experience of experiencing stuff, into thinking that the awareness of the experience is the boundary of the experience.
So Hans I think you have been practicing Naive Realism in your assumptions about phenomenalism.
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Dear Graeme,
Your Wikipage suggests that you are addressing something of genuine interest and novelty. However, (with due respect) in this forum you seem to alternate between stringing together long words without any idea how other people use them and giving lectures on sucking synapses to a row of metaphorical grandmothers. You seem to have missed most of the points in my last post. Telling Hans he is a naïve realist is a bit like telling Hegel he has not quite got the hang of dialectics. I had asked what you thought Edelman meant by relations between cells that lay outside information theory and you produce a bog standard analysis in terms of what I would call information theory. The answer may indeed be revealing because it suggests that the element in brain that is missing from computers is the relationship between events at specific synapses in an individual cell, but we are still no further with Edelman and his phenomenal can of worms.The regulars on this list are by and large people who are widely read in areas including philosophy, psychology, basic physics, macroneurobiology, microbiophysics, and other individual interests. One thing we tend to share is a perception that many people in the field of ‘mind study’ suffer from severe bouts of thinking two incompatible things at once without being aware of ‘cognitive dissonance’. In fact, from my perspective the difference between people in the field is largely one of whether their incompatible thoughts are countable on the fingers of one hand or run into double figures. The self-contradictory ideas that you have mentioned are widespread but the working assumption is that most people have at least got past those. There are some much tougher ones deeper in which are making everyone go round in circles. I am afraid that you are giving the distinct impression that you are unaware of them. They are not ‘philosophical’; they are just basic flaws in reasoning which probably arise from our tendency to swap around verbal and temporospatial labels for dynamic ideas that have to be labeled somewhat unreliably because they are inherently ‘unenvisageable’ (i.e. the implications of the deeper layers of indirect realism). The problem of understanding the problem is closely related to the problem itself.
Inasmuch as I can see what you mean by phenomenalist and functionalist the differences seems to be waving a magic wand to the right and waving it to the left. That would make a hybrid a figure of eight. I think we are better off disallowing magic wands and trying to solve the problem in a way that makes sense.
I am afraid that people are going to lose interest unless you can lay things out here in comprehensible form, at least in the way that you have done elsewhere.
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Jonathon, You expected me to come up with a different analysis, what makes you think there is a different analysis to come up with? What I think is really a shame is that you grandmothers are so full of sucking eggs, that you can’t build a good souffle any more.
You guys are all acting like I should get up like a trained seal, and perform for you. you won’t give me any buy in on the basics, and you expect me to bother to tell you the real theory behind them? You don’t deserve to get the theory or at least you haven’t proven it to me.
So, I should weather all these attacks by people who claim to know everything, but can’t even accept a straightforward analysis when it’s requested by them?
What were you expecting me to do, start talking martian? You say that there are deeper issues. But you can’t see that part of the reason you have deeper issues is that you brushed past the basics too quickly, because they seemed simple.Hans is just as much a Naive Realist, as someone who believes that we have a single point of view, if he believes that we experience things, and then maybe remember a few of them. That is the simple interpretation but there is no backing in the architecture of the brain to support that interpretation, and yet he still believes it. Why? simply because that is the subjective impression our minds give us about how it is done. The same reason the naive realist has for thinking that we have a single point of view.
If you don’t accept that, then Prove how he experiences things without the stimuli being stored in memory. Go ahead, Make my day!
If you can’t prove it, then maybe what you need to do, is step down from your lofty perch, and admit that maybe you breezed a little too fast through the basics and missed something important, like the nature of implicit memory!
If you won’t let those that do understand it explain it to you, then don’t expect us to give you the respect due someone who at least listens, and asks questions that have something to do with the topic. The fact is that none of you grandmothers are sucking on the right eggs, or it wouldn’t be like pulling hens teeth to get consensus.
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Dear Graeme,
instead of giving labels, why not address statements, wether you agree to them or not?
I have a view of phenomena such as memory, exerience and consciousnes if you will, that does not come from a philosophical stance, but from an empirical one: give a proper description of what actually happens.If you think my description is inaccurate, give your own. I think that has to be the starting point. That is why I prefer phenomenology over theorising (unfounded as it can be) and philosphising.
I am certainly not advocating naive realism!
Yours friendly
Hans -
OK, Hans, I will accept that you don’t think you are supporting Naive Realism, at least in its classic form. But I think that Naive Realism is much more subtle than you give it credit for being. If you let it, it will creep back into your thinking every chance it gets, because it is a natural function of the brain.
The problem I have with your Phenomenal definition of experience, as you formulated it yourself, is that it starts too late in the process of experience.
by implying that nothing is important before you become aware that you are experiencing anything. Thus as I said you have placed a boundary on experience that presupposes that the Phenomena only starts with the awareness.In actuality, any experience starts at birth, when we start recording stimuli and setting up the cerebral cortex to recognize familiar stimuli. This is part of the reason I am having so much trouble with you guys. I am talking past you because I am presenting familiar material in an unfamiliar manner. You match up the familiar material with what I am saying, and expect one response, and instead, because I am using it in an unfamiliar way you hear another, and it doesn’t make sense. I just read a book The Structure of Scientific Revolution that states that this is a natural problem whenever two similar but competing paradigms come in contact with each other.
Anyway to get back to a model of experience. In order to understand an experience you have to pick it apart into bits and pieces you recognize and then synthesize those bits and pieces back together to form information about the experience. It is only then, that you can tell yourself, what you are experiencing.
So, in my model, becoming aware of an experience, comes later than collecting stimuli, detecting familiar patterns in the stimuli, and synthesizing the patterns into information. If you haven’t done these previous steps, you don’t know what you have experienced, and so it doesn’t matter to you.
Now, in my model, each of these steps requires more memory, so your assumption that you remember some of the experience, is also in some ways a fallacy, the fact is that you remember a lot more about the experience than you declare to yourself, and this is an important distinction, because you only know what you tell yourself that you know, but you store much more than that.
This is why implicit memory is so important. It is where you store, the greater bulk of the information that you don’t know that you know. It is also why the cerebral cortex takes up 1/3 of your brain. Now, if I can get past the point I am at right now with you guys, I can discuss the interface between implicit memory and explicit memory and the implications that has for how memory works, but it’s hardly worth my while, if you haven’t yet figured out what I am talking about when I talk about implicit memory, if only because the nature of explicit memory in my model is so dependent on the nature of implicit memory, that you will miss the implications if you don’t understand implicit memory the way that I do.
You may think that your model is based on empirical observation, but one of the real problems in consciousness studies, is the fact that subjective elements of consciousness color our study of consciousness, and get in the way of understanding it. Naive realism is one of those subjective elements that keeps coming back to haunt us. One of the ways it does, is by subtly shifting the way we stress our descriptions of our empirical observations to favor the natural bias towards giving more import to awareness and consciousness than they might actually have in the physical system that they are found in. Thus we look for the NCC of consciousness, instead of trying to fit the NCC’s of other functions together into a larger picture, and seeing where consciousness fits into the larger picture.
If I am right the NCC’s of consciousness are much more subtle than we can see right now. We will have to wait for the function of the PFC to be more clearly understood before we find them.
In the meantime, I am trying to explain the micro-anatomy of the cerebral cortex, as part of a larger model of the brain’s memory system, and I have no assurance that even one of you guys has yet even an inkling of how important implicit memory is. Your description of experience so badly missed the mark that it was obvious that we were talking on different pages.
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Well, Ottmar, you have me there. See you are only describing two different types of memory, and my model requires three. So we have a problem, Either I have to coin a different phrase to name the memory between implicit memory as defined by my Allocortical Model, and Declarative Memory, as defined by my as yet unexplicated Declarative Memory Model, Or I have to stretch the existing labels to fit. Then within the types of memory I have to deal with the different stages going through each type, which multiplies it to six subtypes.
So rather than get all fancy and give all six different names, I have tried to stick as close to the original models as possible, and just use three definitions. Implicit Memory 1 (My Allocortical Model)
Implicit Memory 2 (Long term Implicit Memory (Requires a biochemical pathway to be opened in order to trigger growth)
Explicit Memory 1 (The retrievable version of Implicit Memory)
Explicit Memory 2 (Derived Explicit Memory requires secondary perception areas to be activated)
Declarative Memory 1 (We Told ourselves we know it)
Declarative Memory 2 (We consolidated it back to the cortex)You can argue about the names of the terms all you want, they are arbitrary, but I can show you the physical structures that the brain uses to create all these different types, and where not, I can show you evidence in sleep studies, and amnesia studies that indicate that they probably do exist.
Again remember these are not all the different types of memory that have different areas of the brain to store them, just the ones in the direct main memory loop, and the declarative memory loop that require some sort of redescription in order to develop. There are actually signs that there might be a greater number of memory types, but I am not bold enough to push for the larger number because most of them do not require redescription just remapping and storage. An example that has only recently come to light is the possibility of a Medium Term implicit memory, that is developed in parallel to the long term implicit memory and uses a different biochemical channel to create.
Until we know just how many biochemical channels there are through the actual cell, for memory, we will not know exactly how many types of implicit memory there are, nor how many different types of Declarative Memory there are, since there is evidence of more than one term of Declarative Memory as well.
The two label memory model is just obsoleted by new information about the nature of memory.
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Thanks for your kind words Ottmar. I understand your frustration, the two of us have been talking past each other since we started this thread. The thing is that we can’t start up a dialogue if you ignore everything I say. I have good and yes, technical reasons for working with a larger model than you will accept.
It is not that what you say has not been empirically useful, and sufficient up until now, but there has been a major shift in our understanding of memory since your model was designed. Even a year or two ago, we did not understand that the biochemical channels that retained short term and long term memories were parallel within the cell, so we couldn’t realize that it was possible to store different results in short term memory than we did in long-term memory.
I wonder which annoyed you more, the fact that I countered your model with one of my own, or the fact that I suggested it was obsolete? Ah well, good luck, and better days in the future, it was nice knowing you.
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This reminds me, That the think I want to stress the most about implicit memory, is the nature of the Data-Cloud. Whether the hecklers on this group will let me call it phenomenal or not, The characteristics of the data cloud are important.
1. It is organizationally bankrupt due to the lack of structure to the connections between neurons.
2. It is (I can’t use Phenomenal it seems) impossible to find the address of a specific memory because of the characteristics of the network, and is therefore indivisible, at least in a naive system that hasn’t mapped out the divisions in the data.
3. It is highly parallel, redundant, and rich in content
4. It can be filtered by association of specific zones with GSO frequencies.
Now let me suggest that these are the characteristics of the memory before it passes through the bottleneck. If I am right, the bottleneck itself is a conversion process that redescribes memory from this form, to something a little bit more useful. Whether I call the data-cloud a Quale or not, what this suggests is that instead of a single memory passing through the bottleneck, a large block of parallel data, passes through. As a result of this process we get a redescribed memory that we can address and eventually split into smaller memories. But we don’t split the data-cloud, we finesse it by splitting the addressing block that addresses the data-cloud.
As I implied before, the only way to address such a data-cloud is to isolate the data-cloud, and test each neural group address, to see if it is part of the data-cloud. An interesting thing happens here, we start to manipulate the neural group addresses, instead of the data. In fact, the natural storage unit was described by Miller in 1956 in his magic number paper, and has been called a Chunk ever since. People reading early forms of my work will find that I misremembered this name and called it a clump. The name is arbitrary but essentially Miller characterized it as a list of references to long-term memory.
The role of the bottleneck seems therefore to redescribe the memory from a cloud of data, to a list of neural group addresses. It probably does this by a serial search through the data-cloud to find the active neural groups.
As a result of this search, the storage capacity of working memory is limited by the throughput of the bottleneck. Psychologists have found that the actual capacity of the working memory seems to be limited by the fact that there is no parallelism in the actual search, and so searches have to be strung serially in order to process their data-clouds. A good example is the fact that the number of syllables in a digit name seems to have an impact on how many digits we can store in our memories. For instance chinese students averaged 2 more digits than English and Japanese who averaged at least one more digit than Welsh.
This suggests that there is some factor in the data-cloud that is sensitive to the number of syllables there are in the name of the word. As a result it takes longer for multi-syllabic words to resolve than it does for single syllabic words. This limits the speed with which they can be rehearsed in the working memory, which when combined with a best-before data to the data, means that only a limited number of digits can be stored if they are to be maintained for longer than a few seconds.
Now one thing that separates the memory before the bottleneck from the memory after the bottleneck is that the memory after the bottleneck can be retrieved, and rehearsed, so we know that the chunk is critical to storage capacity of the short-term memory or working memory.
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I have silenced the hecklers for a moment. Does that mean I can go on, in confidence that I am right, or has the only audience for this thread dropped away? I wonder. I hate to turn it into a blog, but I have to have confidence that my view of this is important, if only for my own sanity.
It is time to consider Isocortical Tissue again. Isocortical tissue is the six layer cortex that characterizes most of the cerebral cortex. My theory, as has been alluded to, earlier, is that Isocortical Tissue allows explicit access to implicit memory. One of the problems that has to be dealt with, before such explicit access can happen, is the location problem. Neural Networks do not have locations for data per se. I have implied that to some extent this is taken care of by the neural groups, in that neural groups have a location that might be addressed in some manner, but more to the point, is the relationship between neural groups and the inhibitive marinotti cells. What I propose is that this relationship favors the storage of data, in the Neural Groups. One possible interpretation is that the the Layer 6 pyramidal cells, balance the Martinotti cells promoting the general activation of the Column when the neural groups within it are active.
Studies of the nature of the connections between neurons in the cerebral cortex, suggest that they are vertically aligned, and thus that the columns tend to operate as a unit. Further activation of a column seems to have an inhibiting effect on other local columns, suggesting that there is some sort of competitive relationship between local columns. It is my believe that the net effect is the concentration of similar signals within the same column. This is caused by the dynamic interaction between the columns and the inhibitive reactions within the columns that tend to gather like terms into the neural groups.
The concept therefore is that layer 6 is mainly there to stabilize the location of the data within the network. Since Layer 4 is the layer at which the inputs from the thalamus that have to do with the rerouting of sensory data, impact on the system, that leaves layer 5 as unexplained.
Before I go on, I would like to introduce you to the work of David LaBerge who has made two very important contributions that I wish to commemorate.
The first contribution is the Triangular Circuit Theory of Attention, which Hans will hate, because it suggests that there are forms of attention that are not directed by consciousness, and a Theory about the role of the Apical Dendrites in Layer 5, and their connection to the thalamus.Essentially what his triangular circuit theory supposes is that there are three areas of the brain that light up during awareness, the Cerebral Cortex, the Thalamus and the Prefrontal Cortex. Having linked these areas, let us go on to the Apical Dendrite theory,in which he supposes that the Layer 5 Pyramidal Neurons are pre-activated by their connections to the thalamus, and that this is the route of Bottom-up Attention. The Prefrontal Cortex, is therefore the seat of Top-Down attention, which it seems has been coopted by the phenomenalists because of its association with the executive function, and by the fact that it responds to Voluntary Activity, by suppressing activation of the cerebral cortex.
Of essential understanding is the connection to the general cerebral cortex, through the network connections recently discovered by Hagmann via DSI analysis of MRI results. It seems that the cerebral cortex reports to the prefrontal cortex, via the Anterior Cingulate Cortex, which is the furthest frontal extent of this central network.
The Anterior Cingulate cortex, on the other hand, has been implicated in the selection between equivalent choices. Since the prefrontal cortex is heavily dependent on Dopamine this choice making is probably done by suppressing the output of the cerebral cortex areas. One possible avenue that this might be managed by, is the Basket Cells, in the cortex, that are thought by some to be soma shunts, and thus to shunt the output of the layer 2/3 pyramidal neurons without affecting their inputs, and thus short term learning.
One idea that has been perhaps prematurely shot down, is the idea that the Anterior Cingulate Cortex, resolves similar elements by their frequency. It has been suggested that the Thalamus might impose a frequency (Via Layer 5) on the cerebral cortex, by pre-activating neural groups in synchrony with a base signal, and that this produces a tendency for the Layer 2/3 neurons to resonate at that same synchronous frequency. In any case the frequency which has been called a GSO because it is a 40 hertz or Gamma-range Synchronous Oscillation, These 40 hertz signals have been found to synchronize large groups of neurons, and to have possibly also a role in triggering Implicit Long-term Memory.
One of the reasons that they have not been accepted as being an operative part of the attention system, is that they do not seem to distinguish between foreground and background, and their imposition on the Layer 2/3 signals seems additive rather than like a carrier wave.
Dr. Edelman advanced the opinion that these “Bound” the signals together which has an impact on Hans theory of “Binding”. He suggested that the bound groups of neurons be called Functional Clusters.
But the accusation that there is no separation between foreground and background, in this binding, seems to fly in the face of current understanding of memory, and so, many people have simply abandoned the issue.
Before it was abandoned however cells were found in the Ventro-Lateral PFC that the Anterior Cingulate Cortex is part of, that responded to different frequencies. This suggests that the Ventro-Lateral Cortex has the ability to distinguish between different frequencies.
One possible although by no means popular hypothesis is that it is these frequencies that the ACC uses to distinguish different Functional Clusters of neurons. The question then becomes, why are they formed, and what is being selected between when the ACC suppresses one Functional Cluster over another?
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Scientists capture the first image of memories being made
June 18th, 2009 in Medicine & Health / Neuroscience Scientists capture the first image of memories being made <http://www.physorg.com/newman/gfx/news/hires/scientistsca.jpg> The increase in green fluorescence represents the imaging of local translation at synapses during long-term synaptic plasticity. Credit: Science
The ability to learn and to establish new memories is essential to our daily existence and identity; enabling us to navigate through the world. A new study by researchers at the Montreal Neurological Institute and Hospital (The Neuro), McGill University and University of California, Los Angeles has captured an image for the first time of a mechanism, specifically protein translation, which underlies long-term memory formation.
The finding provides the first visual evidence that when a new memory is formed new proteins are made locally at the synapse – the connection between nerve cells <http://www.physorg.com/tags/nerve+cells/> – increasing the strength of the synaptic connection and reinforcing the memory. The study published in Science, is important for understanding how memory traces are created and the ability to monitor it in real time will allow a detailed understanding of how memories are formed.
When considering what might be going on in the brain at a molecular level two essential properties of memory need to be taken into account. First, because a lot of information needs to be maintained over a long time there has to be some degree of stability. Second, to allow for learning and adaptation the system also needs to be highly flexible.
For this reason, research has focused on synapses which are the main site of exchange and storage in the brain. They form a vast but also constantly fluctuating network of connections whose ability to change and adapt, called synaptic plasticity, may be the fundamental basis of learning and memory.
“But, if this network is constantly changing, the question is how do memories stay put, how are they formed? It has been known for some time that an important step in long-term memory formation is “translation”, or the production, of new proteins locally at the synapse, strengthening the synaptic connection in the reinforcement of a memory, which until now has never been imaged," says Dr. Wayne Sossin, neuroscientist at The Neuro and co-investigator in the study. “Using a translational reporter, a fluorescent protein that can be easily detected and tracked, we directly visualized the increased local translation, or protein synthesis, during memory formation.
Importantly, this translation was synapse-specific and it required activation of the post-synaptic cell, showing that this step required cooperation between the pre and post-synaptic compartments, the parts of the two neurons that meet at the synapse. Thus highly regulated local translation occurs at synapses during long-term plasticity and requires trans-synaptic signals.”Long-term memory and synaptic plasticity require changes in gene expression and yet can occur in a synapse-specific manner. This study provides evidence that a mechanism that mediates this gene expression during neuronal plasticity involves regulated translation of localized mRNA at stimulated synapses. These findings are instrumental in establishing the molecular processes involved in long-term memory formation <http://www.physorg.com/tags/memory+formation/> and provide insight into diseases involving memory impairment.
Source: McGill University (news
<http://www.physorg.com/partners/mcgill-university/> : web <http://www.mcgill.ca/> ) http://www.physorg.com/news164554667.html
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