[Hplusroadmap] Fwd: [mea-users] Paper modeling in-vitro learning

[Bryan Bishop]

----------  Forwarded Message  ----------

Subject: [mea-users] Paper modeling in-vitro learning
Date: Wednesday 16 April 2008
From: "Steve M. Potter" <steve.potter at bme.gatech.edu>
To: Steve Potter <steve.potter at bme.gatech.edu>

Dear MEA Users,

We would like to announce the publication of a paper in which we use a  
leaky integrate-and-fire model network on a model MEA to create a  
successful learning paradigm for future in vitro studies. The  
supplement has full details of the model, for those wishing to try it.


Online Open-Access paper, supplement, and movie:
http://www.ploscompbiol.org:/article/info%3Adoi 
%2F10.1371%2Fjournal.pcbi.1000042

PLoS Computational Biology 4(3): e1000042

"Shaping Embodied Neural Networks for Adaptive Goal-directed Behavior"

Zenas C. Chao, Douglas J. Bakkum, Steve M. Potter*

Laboratory for Neuroengineering, Department of Biomedical Engineering,  
Georgia Institute of Technology and Emory University School of  
Medicine, Atlanta, Georgia,
United States of America


Abstract
The acts of learning and memory are thought to emerge from the  
modifications of synaptic connections between neurons, as guided by  
sensory feedback during behavior. However, much is unknown about how  
such synaptic processes can sculpt and are sculpted by neuronal  
population dynamics and an interaction with the environment. Here, we  
embodied a simulated network, inspired by dissociated cortical  
neuronal cultures, with an artificial animal (an animat) through a  
sensorymotor loop consisting of structured stimuli, detailed activity  
metrics incorporating spatial information, and an adaptive training  
algorithm that takes advantage of spike timing dependent plasticity.  
By using our design, we demonstrated that the network was capable of  
learning associations between multiple sensory inputs and motor  
outputs, and the animat was able to adapt to a new sensory mapping to  
restore its goal behavior: move toward and stay within a user-defined  
area. We further showed that successful learning required proper  
selections of stimuli to encode sensory inputs and a variety of  
training stimuli with adaptive selection contingent on the animat’s  
behavior. We also found that an individual network had the flexibility  
to achieve different multi-task goals, and the same goal behavior  
could be exhibited with different sets of network synaptic strengths.  
While lacking the characteristic layered structure of in vivo cortical  
tissue, the biologically inspired simulated networks could tune their  
activity in behaviorally relevant manners, demonstrating that leaky  
integrate and- fire neural networks have an innate ability to process  
information. This closed-loop hybrid system is a useful tool to study  
the network properties intermediating synaptic plasticity and  
behavioral adaptation. The training algorithm provides a stepping  
stone towards designing future control systems, whether with  
artificial neural networks or biological animats themselves.
Citation: Chao ZC, Bakkum DJ, Potter SM (2008) Shaping Embodied Neural  
Networks for Adaptive Goal-directed Behavior. PLoS Comput Biol 4(3):  
e1000042.
doi:10.1371/journal.pcbi.1000042


As always, I encourage other MEA Users to alert us to relevant  
publications!

-Steve

____________________________________________________________________________
Steve M. Potter, PhD  <steve.potter at bme.gatech.edu>
Personal email to: steve.potter at gmail.com
Associate Professor of Biomedical Engineering
Neuroengineering Laboratory
Coulter Department of Biomedical Engineering
Georgia Institute of Technology and Emory University
http://www.neuro.gatech.edu/groups/potter/potter.html
Administrative Assistant, Amber Burris
(404) 385-0126 <mailto:amber.burris at bme.gatech.edu>






------------------------------------

____The MEA-Users_____.
Steve M. Potter, PhD, Administrator  <steve.potter at bme.gatech.edu>

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