Tony Bell: Difference between revisions
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== Research | == Research Interest == | ||
My | My interest in 2007 is:- | ||
To unify ideas from probabilistic unsupervised machine learning | |||
with the cross-level information flows that occur in the biological hierarchy. | with the cross-level information flows that occur in the biological hierarchy. | ||
The | The idea is that macroscopic variables can use microscopic variables to | ||
store information. You might not realise it but this sentence you are | |||
reading is changing your gene expression (your genes heard it here first!) | |||
I am demonstrating this concept with new learning algorithms, focusing | |||
on synaptic learning phenomena such as spike timing-dependent plasticity (STDP), | |||
and on systems of kinetic equations inspired by the processes that govern | |||
the flow of information inside a cell (for example ion channel and enzyme kinetics). | |||
In the process, I am creating dynamic signal processing algorithms with new properties. | |||
I hope they will be powerful. | |||
I know this requires some explanation. I need to completely revamp my web-presence, and I hope to do so soon, | I know this requires some explanation. I need to completely revamp my web-presence, and I hope to do so soon, | ||
along with papers explaining | along with papers explaining this idea. | ||
My old page, at the Salk Institute, is quite out-of-date, but contains some useful material. | My old page, at the Salk Institute, is quite out-of-date, but contains some useful material. | ||
It is [http://www.snl.salk.edu/~tony here]. | It is [http://www.snl.salk.edu/~tony here]. | ||
Revision as of 02:06, 10 March 2007
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 at berkeley dot edu>
Research Interest
My interest in 2007 is:-
To unify ideas from probabilistic unsupervised machine learning with the cross-level information flows that occur in the biological hierarchy. The idea is that macroscopic variables can use microscopic variables to store information. You might not realise it but this sentence you are reading is changing your gene expression (your genes heard it here first!) I am demonstrating this concept with new learning algorithms, focusing on synaptic learning phenomena such as spike timing-dependent plasticity (STDP), and on systems of kinetic equations inspired by the processes that govern the flow of information inside a cell (for example ion channel and enzyme kinetics).
In the process, I am creating dynamic signal processing algorithms with new properties.
I hope they will be powerful.
I know this requires some explanation. I need to completely revamp my web-presence, and I hope to do so soon,
along with papers explaining this idea.
My old page, at the Salk Institute, is quite out-of-date, but contains some useful material.
It is here.
