Santa Fe Institute Collaboration Platform

Thermodynamics of Computation

Editing Vijay Balasubramanian

From Thermodynamics of Computation

Warning: You are not logged in. Your IP address will be publicly visible if you make any edits. If you log in or create an account, your edits will be attributed to your username, along with other benefits.

The edit can be undone. Please check the comparison below to verify that this is what you want to do, and then save the changes below to finish undoing the edit.

Latest revision Your text
Line 4: Line 4:
 
As a biophysicist I have pursued these questions primarily in neuroscience.  I think about the brain as a statistical computational device and seek to uncover the principles that underlie the organization of neural circuits across scales from cells to the whole brain.  I have worked on systems in the brain that support many different functions: vision, audition, olfaction, spatial cognition, motor control and decision making.  Applying lessons about adaptive molecular sensing from the olfactory system, I have also written about the functional organization of the adaptive immune system in vertebrates and bacteria (CRISPR).   
 
As a biophysicist I have pursued these questions primarily in neuroscience.  I think about the brain as a statistical computational device and seek to uncover the principles that underlie the organization of neural circuits across scales from cells to the whole brain.  I have worked on systems in the brain that support many different functions: vision, audition, olfaction, spatial cognition, motor control and decision making.  Applying lessons about adaptive molecular sensing from the olfactory system, I have also written about the functional organization of the adaptive immune system in vertebrates and bacteria (CRISPR).   
  
As a theoretical physicist, I have pursued questions about the fundamental nature of space and time.  I have worked on the apparent loss of quantum information in the presence of black holes and the origin of entropy and thermodynamics in gravitating systems.  I have discussed how the familiar smooth structure of space-time can emerge as a long-distance effective description of more complex underlying physical constructs.  I have shown how some dimensions of space can be regarded as emergent, arising from the quantum entanglement and information structure of an underlying lower-dimensional theory.     
+
As a theoretical physicist, I have pursued questions about the fundamental nature of space and time.  I have worked on the the apparent loss of quantum information in the presence of black holes and the origin of entropy and thermodynamics in gravitating systems.  I have discussed how the familiar smooth structure of space-time can emerge as a long-distance effective description of more complex underlying physical constructs.  I have shown how some dimensions of space can be regarded as emergent, arising from the quantum entanglement and information structure of an underlying lower-dimensional theory.     
  
 
Finally, I have written on problems in statistical inference and machine learning, and in particular on “Occam’s Razor”, i.e., the tradeoff between simplicity and accuracy in quantitative models.  I am interested in this question because all scientific theories involve fitting models to data, and there is a fundamental tradeoff between the complexity of models and their ability to generalize correctly to new situations.  This tradeoff influences how scientists infer models of the world, how machines learn the structure in data, and how living things from the scale of single cells to entire organisms with brains adapt to their environment over timescales from milliseconds to evolutionary time.
 
Finally, I have written on problems in statistical inference and machine learning, and in particular on “Occam’s Razor”, i.e., the tradeoff between simplicity and accuracy in quantitative models.  I am interested in this question because all scientific theories involve fitting models to data, and there is a fundamental tradeoff between the complexity of models and their ability to generalize correctly to new situations.  This tradeoff influences how scientists infer models of the world, how machines learn the structure in data, and how living things from the scale of single cells to entire organisms with brains adapt to their environment over timescales from milliseconds to evolutionary time.

Please note that all contributions to Thermodynamics of Computation may be edited, altered, or removed by other contributors. If you do not want your writing to be edited mercilessly, then do not submit it here.
You are also promising us that you wrote this yourself, or copied it from a public domain or similar free resource (see Santa Fe Institute Collaboration Platform:Copyrights for details). Do not submit copyrighted work without permission!

To protect the wiki against automated edit spam, we kindly ask you to solve the following CAPTCHA:

Cancel Editing help (opens in new window)

Template used on this page: