Dean, Thank you for the details - I didn't know about performance of the Spaun simulator. But what additional structures do you plan to add ?
" it seems that what it offers from a functional perspective is applicable to nearly all brain regions (even brainstem, cerebellum and sub-cortical regions)" - are there works that suggest this ? Azat PS: Your message made me think of an analogy: we don't have to know how the muscles move in a bird's wing as long as we can fly. -------------------------------------------- On Fri, 11/1/13, Dean Horak <[email protected]> wrote: Subject: Re: [nupic-dev] Using NuPIC as an engine for WBE To: "NuPIC general mailing list." <[email protected]> Date: Friday, November 1, 2013, 11:49 AM Azat, Yes, as I noted, I've been using the Nengo simulator for my models and could continue to do so. The problem is performance. The current Spaun simulation contains a rudimentary model of the basal ganglia, thalamus and a couple pre-frontal cortical areas - pretty much the bare bones components to being able to perform the tasks it was designed to perform. Already, with only that basic structure, the Spaun simulation requires 24MB RAM (minimum) and is able to simulate about 1 second of real-time every 3 hours of processing time (on a typical quad-core processor). This resource intensity is largely due to the fact that Spaun is using a spiking neural network based on some very computationally expensive models (Izhikevich, Hodgkin-Huxley, LIF). I'm looking to extend these basic structures and include many additional ones, so the performance is only going to get worse. Since I'm looking for something that is at least an order of a magnitude or two closer to real-time (and I don't have a BlueGene computer at my disposal), there is simply no way I can accomplish my goals using this system. That is why I'm looking for an alternative platform. I'm willing to give up the biological realism of a spiking neural network to gain a dramatic increase in performance because my conjecture is that the route to AGI lies less in the low level implementation details of the human brain, and more in the interactions between brain regions honed over billions of years to perform specific functions which, taken together at a holistic level result in the wide spectrum of abilities we consider to be human intelligence. Jeff, Thanks for your well considered response. I fully understand the need to make trade-offs wrt biological realism in order to achieve design goals. Indeed, I am facing those same sorts of design decisions regarding my project. As I noted, I've been following Numenta since it was first publicly announced (in fact you probably still have the NDA on file I seem to recall signing to gain access to the original algorithms). In fact, I largely credit "On Intelligence" for inspiring me (for which I'm am deeply indebted) to turn to neuroscience, a subject I had only casually studied for answers. This, after about 25 years of attacking the AI problem through more traditional AI techniques. However, once I turned to neuroscience, I found a subject so fascinating that it drew me in and has kept me immersed ever since, somewhat to the detriment of my pure AI research. Getting back to the matter at hand however, I understand that the CLA is rooted in the neuroscience of the neocortex and based on your hypotheses of HTM. While my objectives are quite different, and while my perspective of how intelligence is implemented in the brain may not precisely align with yours (I view the neocortex as significant, but only one part of the picture and cortical columns as far less homogeneous than they might at first appear). Still, for my purposes, I'm looking for a platform to construct my models of functional brain regions which internally will require features that a system like NuPIC exhibits (i.e. pattern recognition, unsupervised learning, prediction). The internal (local) networks within each region will be developed in such a way as to exhibit behavior analogous to it's biological counterpart and the connectivity between regions guided by data from neuroscience literature. So (finally), while I understand the CLA is an implementation of an interpretation of cortical processing, it seems that what it offers from a functional perspective is applicable to nearly all brain regions (even brainstem, cerebellum and sub-cortical regions). The main reason for the original question was to test the waters to see if there was some architectural limitation I had overlooked that might prevent me from pursuing this project using NuPIC (for example, an limitation such as not allowing more than one network allowed per process). My apologies for the length of the message. Thanks,Dean -----Inline Attachment Follows----- _______________________________________________ nupic mailing list [email protected] http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org _______________________________________________ nupic mailing list [email protected] http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org
