Tony Bell: Difference between revisions

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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>
Here's the only paper I have written on the Levels issue. It covers my thinking up till about 2008: <br>
[http://www.irp.oist.jp/ocnc/2008/bell07.pdf Towards a cross-level theory of neural learning]


There are 3 steps to uniting physics, biology and machine learning:
There are new results on time series analysis coming :)
 
(1) Solve the time series density estimation problem
 
(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. <br>
Nor, in my view, do generative models, which estimate fictional hidden variables.
 
And 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 :)

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 :)

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