|
|
|
|
|
|
|
Free Newsletters
Archive
My Subscriptions
News by Subject
News by Disease
News by Date
PLoS
Search News
Post Your News
JoVE
Job Seeker Login
Most Recent Jobs
Browse Biotech Jobs
Search Jobs
Post Resume
Career Fairs
Career Resources
For Employers
Regional News
US & Canada
Biotech Bay
Biotech Beach
Genetown
Pharm Country
BioCapital
BioMidwest
Bio NC
BioForest
Southern Pharm
BioCanada East
US Device
Europe
Asia
Market Summary
News
IPOs
Company Profiles
Companies
Events
Research Store
Biotech Events
Post an Event
Real Estate
Business Opportunities
|
|
|
|
PLoS By Category | Recent
PLoS Articles
|
|
Biophysics - Computer Science - Neuroscience - Physics - Physiology
|
Computation Emerges from Adaptive Synchronization of Networking Neurons
Published:
Friday, November 04, 2011
Author:
Massimiliano Zanin et al.
by Massimiliano Zanin, Francisco Del Pozo, Stefano Boccaletti
The activity of networking neurons is largely characterized by the alternation of synchronous and asynchronous spiking sequences. One of the most relevant challenges that scientists are facing today is, then, relating that evidence with the fundamental mechanisms through which the brain computes and processes information, as well as with the arousal (or progress) of a number of neurological illnesses. In other words, the problem is how to associate an organized dynamics of interacting neural assemblies to a computational task. Here we show that computation can be seen as a feature emerging from the collective dynamics of an ensemble of networking neurons, which interact by means of adaptive dynamical connections. Namely, by associating logical states to synchronous neuron's dynamics, we show how the usual Boolean logics can be fully recovered, and a universal Turing machine can be constructed. Furthermore, we show that, besides the static binary gates, a wider class of logical operations can be efficiently constructed as the fundamental computational elements interact within an adaptive network, each operation being represented by a specific motif. Our approach qualitatively differs from the past attempts to encode information and compute with complex systems, where computation was instead the consequence of the application of control loops enforcing a desired state into the specific system's dynamics. Being the result of an emergent process, the computation mechanism here described is not limited to a binary Boolean logic, but it can involve a much larger number of states. As such, our results can enlighten new concepts for the understanding of the real computing processes taking place in the brain.
More...
|
|
|
 |
|
|
|
|
|
|
|
|