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| 132 Barker, MC #3190 <br /> | | 132 Barker, MC #3190 <br /> |
| Berkeley, CA 94720-3190 <br /> | | Berkeley, CA 94720-3190 <br /> |
| phone (415) 699 6502 <br /> | | phone (415) 568-0346 <br /> |
| fax (510) 643-4952 <br /> | | fax (510) 643-4952 <br /> |
| tbell@berkeley.edu <br /> | | tbell@berkeley.edu <br /> |
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| == Research Interest == | | == Research Interest == |
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| It's 2010.
| | (This webpage is under reconstruction. Only a few essential links are posted here.) |
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| Here's my [http://www.snl.salk.edu/~tony Salk web-page] from way back. | | Here's my [http://www.snl.salk.edu/~tony Salk web-page] from way back. |
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| Here's me giving a 30 minute talk [http://thesciencenetwork.org/programs/brains-r-us-2/tony-bell Levels, Time and Models] about Levels in Biology. <br> | | Here's me giving a 30 minute talk [http://thesciencenetwork.org/programs/brains-r-us-2/tony-bell Levels, Time and Models] about Levels in Biology. <br> |
| Here's me giving a 85 minute talk [http://vimeo.com/5812603 Emergence and Submergence in the Nervous System]. <br>
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| (The production on the latter is not so good, so here are the [http://redwood.berkeley.edu/tony/papers slides]. <br>
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| Also, if you wait a few minutes in, the audio drastically improves.)
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| What am I doing? If you watch either of these you will see, at least, where I am starting from.<br>
| | Here's the only paper I have written on the Levels issue. It covers my thinking up till about 2008: <br> |
| There are 3 steps to uniting physics, biology and machine learning:
| | [http://www.irp.oist.jp/ocnc/2008/bell07.pdf Towards a cross-level theory of neural learning] |
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| (1) Solve the time series density estimation problem (this will be ''very'' useful).
| | There are new results on time series analysis coming :) |
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| (2) Solve the sensory-motor density estimation problem
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| (3) Solve the levels density estimation problem
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| The clues to (2) and (3) lie in (1). The clues to (1) lie in non-equilibrium statistical mechanics. We are working on (1) and I think we've got it. <br>
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| A paper on this [http://www.kosmix.com/topic/tony_bell Learning out of equilibrium] will be ready shortly. Email me if you want it when it's ready.
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| The reinforcement learning literature, while elegant, has nothing relevant to say about these deep problems. Nor, in my view, do stochastic <br>
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| generative models, which estimate fictional hidden variables and mistakenly assume that probabilistic models must be stochastic. If you <br>
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| believe that "the world is noisy", you are confusing your uncertainty with something called "noise in the system", a completely undefined, <br>
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| and possibly mystical, concept.
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| Unfortunately there's no way around it: to really crack this we are going to have to ''get real''. <br>
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| That means we use real biology and real physics to guide us. Computational fantasizing has demonstrated its limits. <br>
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| We will have to absorb and augment the emerging non-equilibrium statistical mechanics and, in the end, also (cough) quantum theory. <br>
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| I know these are radical views, but still, after a lot of thought, I believe them to be correct. Fortunately, both these branches of physics, <br>
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| in their core mathematical structure, are relatively simple.
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| Here's an unsatisfactory paper [http://redwood.berkeley.edu/tony/papers Towards a cross-level theory of neural learning] that explains what I was thinking up till about 2008.
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| Here's my [http://www.kosmix.com/topic/tony_bell CV]. (Ignore the stuff about the cyclist journalist - that's not me :)
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| More later. Please send grant money :)
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Anthony J. Bell Ph.D.
Redwood Center for Theoretical Neuroscience
UC Berkeley
132 Barker, MC #3190
Berkeley, CA 94720-3190
phone (415) 568-0346
fax (510) 643-4952
tbell@berkeley.edu
Research Interest
(This webpage is under reconstruction. Only a few essential links are posted here.)
Here's my Salk web-page from way back.
Here's me giving a 30 minute talk Levels, Time and Models about Levels in Biology.
Here's the only paper I have written on the Levels issue. It covers my thinking up till about 2008:
Towards a cross-level theory of neural learning
There are new results on time series analysis coming :)
