Difference between revisions of "Tony Bell"

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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 ==
  
It's 2010.
+
(This webpage is under reconstruction. Only a few essential links are posted here.)
  
 
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.
  
 
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>
 
(The production on the latter is not so good, so here are the [http://redwood.berkeley.edu/tony/papers slides]. <br>
 
Also, if you wait a few minutes in, the audio drastically improves.)
 
  
What am I doing? If you watch either of these you will see, at least, where I am starting from.<br>
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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:
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[http://www.irp.oist.jp/ocnc/2008/bell07.pdf Towards a cross-level theory of neural learning]
  
(1) Solve the time series density estimation problem (this will be ''very'' useful).
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There are new results on time series analysis coming :)
 
 
(2) Solve the sensory-motor density estimation problem
 
 
 
(3) Solve the levels density estimation problem
 
 
 
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>
 
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.
 
 
 
The reinforcement learning literature, while elegant, has nothing relevant to say about these deep problems. Nor, in my view, do stochastic <br>
 
generative models, which estimate fictional hidden variables and mistakenly assume that probabilistic models must be stochastic. If you <br>
 
believe that "the world is noisy", you are confusing your uncertainty with something called "noise in the system", a completely undefined concept.
 
 
 
Unfortunately there's no way around it: to really crack this we are going to have to ''get real''. <br>
 
That means we use real biology and real physics to guide us. Computational fantasizing has demonstrated its limits. <br>
 
We will have to absorb and augment the emerging non-equilibrium statistical mechanics and, in the end, also (cough) quantum theory. <br>
 
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>
 
in their core mathematical structure, are relatively simple.
 
 
 
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.
 
 
 
Here's my [http://www.kosmix.com/topic/tony_bell CV]. (Ignore the stuff about the cyclist journalist - that's not me :)
 
 
 
More later :) Please send grant money.
 

Latest revision as of 10:50, 14 September 2010

Tony.jpg

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 :)