Difference between revisions of "Tony Bell"

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(1) Solve the time series density estimation problem  
 
(1) Solve the time series density estimation problem  
  
(2) Solve the sensory-motor density estimationproblem
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(2) Solve the sensory-motor density estimation problem
  
 
(3) Solve the levels density estimation problem
 
(3) Solve the levels density estimation problem
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I am working on (1) and I think I've got it.
 
I am working on (1) and I think I've got it.
  
The reinforcement literature has nothing relevant to say about these deep problems.
+
The reinforcement learning literature has nothing relevant to say about these deep problems.
  
 
And unfortunately there's no way around it: to really crack this we are going to have to "get real". <br>
 
And unfortunately there's no way around it: to really crack this we are going to have to "get real". <br>

Revision as of 09:10, 9 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) 699 6502
fax (510) 643-4952
tbell@berkeley.edu

Research Interest

It's 2010.

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 me giving a 85 minute talk Emergence and Submergence in the Nervous System.
(The production on the latter is not so good, so here are the slides.
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.
I really want to crack this, and I think it can be done before too long.
Also, I believe we must solve these problems.

There are 3 steps to uniting physics, biology and machine learning:

(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).
I am working on (1) and I think I've got it.

The reinforcement learning literature has nothing relevant to say about these deep problems.

And unfortunately there's no way around it: to really crack this we are going to have to "get real".
We will have to absorb and augment the emerging non-equilibrium statistical mechanics and (cough) quantum theory.
I know this is not a common view, but still, I think it is correct. Fortunately, both these branches of physics, in their core form, are relatively simple.

Here's an unsatisfactory paper Towards a cross-level theory of neural learning that explains what I was thinking up till about 2008.

Here is my CV. (Ignore the stuff about the cyclist journalist - that's not me :)