> On the face of it, these place maps are very reminiscent of attractors as > found in formal "attractor neural networks." When multiple noncorrelated > maps are stored in the same collection of neurons, this sounds like multiple > attractors being stored in the same formal neural net.
Yeap, there's well developed theories about how an autoassociate network like CA3 could support multiple, uncorrelated attractor maps and sustain activity once one of them was activated. The big debate is about how they are formed. > > About the ability to study 200 neurons at once: With what time granularity > can this be done? Do there exist time series of the activity of these 200 Raw data is usually acquired at 50+ Khz, and then the spikes are identified as to which neuron they belong to and are stored in a reduced form (ie spike X of neuron Y occurs at time T) > neuron, both during map learning and during map use? Analyzing this > 200-dimensional time series would be interesting. (Not that I have time to > do it .. but it would be interesting.) We are currently using Novamente to They're working on it. At present such labs are acquiring data faster than they can analyze it. Figuring out how the maps form is a tricky business because you can only sample the formation of a place field when the rat is in it. Consequently the data is very sparse during the formation. They are making progress though. > analyze coupled time series in another biological domain (gene expression > data). If there is decent time series data, it could be interesting to > codevelop a grant application with someone to see what Novamente can find in > this data.... Very interesting idea. The lab with most of this data is the McNaughton lab in Arizona. They are somewhat reluctant to give it out though, because of the money and time investment in collecting it. It would be very cool if Novamente could be applied to it though. > > On a more philosophical note, I like the idea that the machinery used for > place mapping in rats is similar to the machinery used for more abstract > sorts of "mapping" in humans. Indeed, this reflects the point someone made > last week on this list, regarding the fact that humans have much better > reasoning ability in familiar domains than unfamiliar ones. Maybe one of > the reasons is that when we know a domain well we figure out how to map the > domain into a physical-environment metaphor so we can use some "physical > mapping machinery" to reason about it. But some familiarity is needed to > create the map into the physical-environment metaphor. I think this is what > someone suggested last week -- and your essay makes me like the hypothesis > even more. That was me. It will be awhile before we have such human data of course, but they are starting to record from human hippocampi (in eplileptic patients). I'm a big fan of using landscape analogies to reason about problems, it tends to work well for me. But I wonder if such abilities are more reliant on visuospatial areas of the cortex. One of the limitations that strikes me is that of dimensionality. I used to spend time while driving on road trips trying to think in 4-dimensions in a similar way that I can visualize 3-d. I just couldn't get it to work well. The best I could come up with was layers of 3-d representations with 1 feature varying. This is an excellent example of how powerful our minds are at certain kinds of computation, but limited outside of our innate domains. > > I am reminded a bit of some management-consulting ideas developed by my > friend Johan Roos, see e.g. > > http://www.seriousplay.com/images/landscapes.pdf > > His work explores the notion of "knowledge landscapes", and the use of the > physical-landscape metaphor in human thinking about business. I'll check it out, thanks. ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
