Archive for December, 2006
The Mouse is dreaming that it is in a Trap!!
Dec 28th
New research has established that mice dream and during their sleep there is a two-way dialog between the hippocampal recent day memory area and the neo-cortex that is believed to be involved in long-term memory.
The content of the mice dream is also no longer secret. In the sleep they are replaying the sequence of steps that they had executed in a maze, but in a reverse order, and in lesser time and in general are rehearsing the structure of the maze (the mouse trap). Learning, it is to be remembered, arises from these replays of fast rewinds and sleep it seems is necessary for learning.
Some quotes from the article:
During nondreaming sleep, the neurons of both the hippocampus and the neocortex replayed memories — in repeated simultaneous bursts of electrical activity — of a task the rat learned the previous day.Earlier this year Dr. Wilson reported that after running a maze, rats would replay their route during idle moments, as if to consolidate the memory, although the replay, surprisingly, was in reverse order of travel. These fast rewinds lasted a small fraction of the actual time spent on the journey.
In the findings reported today, the M.I.T. researchers say they detected the same replays occurring in the neocortex as well as in the hippocampus as the rats slept.The rewinds appeared as components of repeated cycles of neural activity, each of which lasted just under a second. Because the cycles in the hippocampus and neocortex were synchronized, they seemed to be part of a dialogue between the two regions.
Because the fast rewinds in the neocortex tended to occur fractionally sooner than their counterparts in the hippocampus, the dialogue is probably being initiated by the neocortex, and reflects a querying of the hippocampus’s raw memory data, Dr. Wilson said.
“The neocortex is essentially asking the hippocampus to replay events that contain a certain image, place or sound,” he said. “The neocortex is trying to make sense of what is going on in the hippocampus and to build models of the world, to understand how and why things happen.”
PS: My blog post has deliberately used words like ‘dream’, ‘mouse’ and ‘traps’ instead of the correct ‘sleep’, ‘rats’ and ‘mazes’: just to come up with a juicy headline!!
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Neurogeneisis, learning and small-world networks
Dec 28th
Continuing this blog’s recent focus on categorization, one possibility of how new items are classified has been hypothesized as either assimilitaion (adding the item to an existing schema in the feature space) or accomodation (addition of a new schema around the item in the feature space). We’ll leave aside the newly introduced concept of Restructuring for this particular discussion.
Schemata, it is to be remembered, are conceptualized as nothing but a named cluster in the feature space. If we become a bit more audacious, we can posit that the clustering in the feature space is mimicked by the actual clustering/ connectivity of neurons in the Hippocampus (or the appropriate semantic memory brain module), with each neuron representing a particular item- say a neuron being a Halley Barry neuron. These neurons would not be randomly distributed- they form a small-world model with local clustering and bistability. whenever a group of neurons get activated together (and also belong to a cluster or clique), we can say that the memory of that category is activated.
Further suppose that learning and memory are crucially dependent on Neurogeneisis and new learning (of concepts ) happens by insertion of a new node (neuron in the small-world network of brain) and connecting it appropriately with other neurons.
As an example consider that all face recognition cells cluster together in the brain and the concept of face is activated by simultaneous activation of all cells of this cluster. The fact that a new visual stimulus (a novel human face of a stranger) is a face is determined by calculating the stimulus features and their difference from the prototypical/ exemplar face neurons and their features. A match so determined not only enables us to say that this new stimulus is a face (as this input would activate the face clique) , but would also give us an idea of where to place a new neuron that may encode for this new face and how to connect this with other neurons and with which other neurons.
Now whenever we encounter a novel stimulus we have two possibilities. If it matches some existing cluster / category, we encode this new memory by placing a new neuron coding for this in the region of that category in the feature space and (crucially) following preferential attachment attach it in a manner such that the probability of its linking to any other neighboring neuron is in proportion of the links that old neuron already has. (This can be readily implemented in brains as axonal connections will whither if not much functional activity happens at the synapse formed between the new neuron and the older one) . This is akin to assimilation of a new memory/ learning neuron. this method of insertion still keeps the neural net a small-world network.
Now consider the second case when the novel stimuli matches no older categories but necessitates that we form a new category if we have to represent that new item in the feature space. We need accommodation here. On the neural level this is still accomplished by inserting a new neuron, but this time the new node is not peripheral- the new neuron is a hub (category) neuron. So we use the method of copy to insert the new element. We copy the links (partially) of a neighboring hub (cluster center/ category label neuron) and use that link structure to link the newly introduced neuron in the small-world network. the network still remains scale-free and we have introduced a hub or a new category in this case.
All this seems very exciting. Some snippets from wikipedia article on scale -free networks are very relevant.
The mostly widely known generative model for a subset of scale-free networks is Barabási and Albert’s (1999) rich get richer generative model in which each new Web page creates links to existent Web pages with a probability distribution which is not uniform, but proportional to the current in-degree of Web pages.A different generative model is the copy model studied by Kumar et al. (2000), in which new nodes choose an existent node at random and copy a fraction of the links of the existent node. This also generates a power law.
Recently, Manev and Manev (Med. Hypotheses, 2005) proposed that small world networks may be operative in adult brain neurogenesis. Adult neurogenesis has been observed in mammalian brains, including those of humans, but a question remains: how do new neurons become functional in the adult brain? It is proposed that the random addition of only a few new neurons functions as a maintenance system for the brain’s “small-world” networks. Randomly added to an orderly network, new links enhance signal propagation speed and synchronizability. Newly generated neurons are ideally suited to become such links: they are immature, form more new connections compared to mature ones, and their number but not their precise location may be maintained by continuous proliferation and dying off.
I am excited, what about you?
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Markers for Psychosis and Mania
Dec 28th
A recent review of the COMT genotype Met/VAL SNP on psychiatric phenotypes of schizophrenia, bipolar mood disorder and schizoaffective disorder seems to suggest that the SNP’s effcet mya be more of modifying the symptoms (with Val conferring positive symptom susceptibility and MET negative symptom susceptibility) of psychosis and mania, rather than conferring susceptibility to the diseases per se. Also the association, in European populations primarily, would be between both psychosis and mania (schizoaffcetive) present rather than juts a simple diagnosis of schizophrenia or bipolarity.
The narrowing of COMT linkages to the combination of Mania and Psychosis loks like a step forward and the distinction between symptom modifying effects and the distinction between symptoms based on their being positive (additions of functionality) or negative (deletion of functionality) seems to be a step in the right direction.
This differential effect of having a Met or Val allele on symptom type (positive and negative) is also inline with the inverted U model of dopamine levels that suggests that there is a range of dopamine levels that is good for the body(brain) and beyond either end there are deleterious effects. It could be that while a Met allele confers protective advantage for positive symptoms, it is an aggravator for negative symptoms. Depending on dopamine environmental levels, the person having Met allele may or may not show the symptoms of mania/ scizophrenia.
I am also intrigued by the BDNF met/val allele effect on anxiety susceptibility and forced to think whether there too the effect may be that of symptom modification rather than susceptibility?
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Categorization, Memory, small-world networks and neural architecture
Dec 27th
In the last post I had wondered about the clustering based solution to categorization and how they may also inform us about how memory (semantic variety) is stored in brain, as semantic memory is best modeled by an associational or confectionist network.
Thus, a semantic memory based on clustering models may consist of associations between clusters or categories of information. For example one cluster may correspond to the names of countries and another to name of cities. A particular type of connection or association between these two clusters may map a relation of —-IS A CAPITAL OF —- type where for example the fact that Paris is the capital of France is stored. For this knowledge to exist, one has to have prior notions of France is a Country and Paris is a City and on top of that an associational relation between the individual entities France and Paris belonging to particular clusters.
Much of this would be more apparent once relational models of categorization are also covered. For now let us assume that (semantic) memory itself may consist of clusters of neurons that are also interconnected. Interestingly one such neural architecture, that has also been able to simulate short-term memory has been the small-world network model. In this a large number of nodes (neurons ) are connected by edges (synapses) as in a typical random graph. These small-world networks are special in the sense that they have high clustering coefficients and low mean path length. Translated in English, this means they exhibit more than chance clustering (to enhance local processing) as well as display a small value of smallest mean path length (reflecting ease of global processing).
It is intriguing thta in the short term memory model using small-world networks simulation, the researchers found that the model could exhibit bistability, which may be crucial for memory formation. In bistability, the cluster or functional region corresponding to a particular memory can be in two states, depending on an input variable. Thus, a pulse (direction of attention) can activate/ deactivate a memory.
Crucially, it can be hypothesized that as the small-world network model of memory/ categorization is good for local-global processing as well as reflective of the actual brain and AI simulation architectures, the entire brain is a small-world network adequately categorizing and representing the sensory, motor and cognitive information and processing them.
A recent MEG based study has established the fact that the small-world network topology exists in functional sphere in the brain at all oscillatory levels (crucial for binding) and that seems very promising.
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Categoristation: how to bookmark the interesting pages on the web!
Dec 26th
In an earlier post, I had touched upon the different categorization theories that are in prevalence. One of these that was discussed in details was the prototype Vs exemplar method that was based on clustering and involved different representational methods of the categories thus derived.
This post is about how a new item is allocated to a pre-existing category. Simplistically, and in the last post this was the position I had taken, it seems apparent that by calculating the distance of a new item in feature space from the central tendencies of the neighboring clusters (the prototypes/ exemplars) one can find a best fit with one of the clusters and allocate the new item to that category.
This is simplistic as it explains fitting of new items to existing categories, but does not include any mechanisms for formation of new categories.
The analogical approach I take here is of how do I decide in which folder to add a new bookmark of an interesting page found on the web. Most probably the names I have chose for my bookmarks folders are reflective of the central tendencies (common prominent features) of all pages bookmarked in that folder. I would normally look at the new page, and also at my existing folders and see if there is a best fit. If so I juts file the new bookmark under the best-fit existing folder. Slightly extending the concept of categorization to resemble that of a schema, this is the classical case of assimilation in a schema.
However, in case the new web-page cannot be filed under any existing bookmark folder, I would usually create a new folder (with an adequate descriptive name based on the location of the web page in the feature space) and file the new bookmark under that new folder. This is akin to trying to fit in a novel item into existing clusters in the feature space, only to discover, it doesnt fit well with any cluster, but is an outlier. The best way to accommodate such an outlier , in my opinion, is to create a new cluster around the outlier. Extending this to schema, it is not hard to see that this is the classical case of accommodation and formation of a new schemata to incorporate a novel item that cannot be assimilated in existing schema.
Piaget, of course , stopped here (and so do I, sometimes, when managing my bookmarks!). but I would like to venture firth and discuss the other process that I engage in , very infrequently, to keep my bookmarks in good shape. This is what I would call reorganization or restructuring. when I restructure my bookmarks, I change the names, I move bookmarks form one folder to another , I merge bookmarks and also at times create more than a few sub folders. Also, interestingly, I delete some of the old bookmarks; while am captivated by some of the bookmarks and even forget to complete the restructuring part.
I believe that we too indulge in restructuring of our Schema/ categories periodically (it may be as frequent as daily during REM sleep) and that a crucial form of learning is not juts Assimilation and Accommodation, but also Restructuring. Also it is my contention, that we consciously remember anything only because we have actively restructured that information and embedded it in a contextual narrative. In the absence of restructuring, there can be information that can be used, but no conscious knowledge.
I plan to tie this up with the 3 factor model of memory that is emerging. One factor of the memory system uses familiarity detection (assimilation), the other novelty detection(accommodation), while the other involves conscious and contextual recollection(restructuring).
I also propose that these three factors are behind the three kinds of memory (content-wise and not duration wise). The first type of memory is semantic (or noetic)- facts like France’s capital is Paris; the second is procedural (or anoetic) – learning how to drive- and is unconscious; while the third is episodic or autonoetic) – personally remembered events and feelings) . Of course memories would also differer along the time dimension- working memory, long-term memory etc. , but that discussion is for another day.
Also a brief not to myself – how this may be linked with Hughling-Jackson’s theory of 3 states of consciousness and how they are differentially affected in dissociation- the autonoetic memory would be affected first- the noetic second and the anoetic or unconscious memory last in dissociation.
Returning back to categorization, this approach of adding new items either by assimilation, accommodation or restructuring is more guided my Mind-Is-A-Container metaphor. Other metaphors of mind- assuming it theory like – may yield to new and interesting views of how we form a theory-like theory of categorization. The other minor variation to above mind is a container metaphor may be using labels for bookmarks (instead of folders)- this is what Google bookmarks and del.icio are using. I haven’t experimented with that approach to bookmarking extensively, so am not sure what new insights can be gained form them. For those readers, who use labels to organize bookmarks, their insights as comments, would be greatly appreciated.
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