Welcome Guest: Login | Register
Login:  

Home           Research           Forum           About

Search: 

 

About

 

objective
community
support

 

Research

 

 

neuroscience

 

brainmaps.org
neural coding
baseline neural activity
neural connectivity
      [ more ]

 

Resources

 

neuroscience directory
neuroscience glossary
antibody database
primate brain atlases
cortical connectivity
Pubmed explorer
current publications

 

Essays

 

baseline consciousness
expanding consciousness 
universality of self
consciousness singularity
theory of consciousness
theories of mind
personality theories
self-actualization
ego evolution
philosophy

Neural Coding

 

How does the brain encode information? Traditionally, neuroscientists have recognized two types of coding in the brain: rate codes and temporal codes. Temporal coding assigns importance to the precise timing and coordination of action potentials or 'spikes', whereas rate coding does not assign any such importance and says that it is only the 'mean rate' or 'mean frequency' of spikes that matters. A special type of temporal coding is synchrony, whereby the synchronous firing of action potentials by neurons is proposed to encode information.

For many years now, there has been a debate in the neuroscience literature revolving around the significance of temporal coding vs. rate coding for the encoding of neural-based information. Interesting, the debate is better characterized as a pseudo-debate because it is clear that both synchrony and rate codes are being employed, as well as higher-order correlations and syn-fire chains, but no-one really knows what's going on. One idea is that the "attentional spotlight" increases synchrony between neuronal activities, thereby rendering them more effective for driving other neurons. Also, rate codes have been fairly conclusively demonstrated in the first-order sensory neurons.

For additional information, please refer to the following references:

Mikula S and Niebur E.  Rate and Synchrony in a Multilayer Feedforward Network of Coincidence Detectors: Analytical Solutions.  Neural Computation. 2005 Apr;17(4)  [PDF]

Mikula S and Niebur E.  Correlated inhibitory and excitatory inputs to the coincidence detector: Analytical solution.  IEEE Transactions on Neural Networks. 2004 Sep;15(5)  [PDF]

Mikula S and Niebur E.  Synaptic depression leads to nonmonotonic frequency dependence in the coincidence detector.  Neural Computation. 2003 Oct;15(10):2339-58.  [PDF]

Mikula S and Niebur E.  The Effects of Input Rate and Synchrony on a Coincidence Detector: Analytical Solution.   Neural Computation. 2003 Mar;15(3):539-47.  [PDF]







[ Back ]


NEUROSCIENCE, CONSCIOUSNESS, BRAIN, MIND, MIND-BRAIN, NEUROINFORMATICS, NEURAL NETWORKS, BRAIN ATLASES



Send this page to a friend


Home     |     About     |    Research     |    Forum     |    Feedback  


Copyright � BrainMeta. All rights reserved.
Terms of Use  |  Last Modified Mon Jun 08 2009 03:22 am
Your current IP Address is 207.241.237.207

Fatal error: Call to undefined function utimedb() in /home/bmserver/public_html/includes/insert3.txt on line 120