Tuesday, 25 May 2004, 2pm:

<CENTER><B>Neuronal Supervision of Synaptic Plasticity</B>

<P>
Christian Swinehart<BR>
Brandeis University
</CENTER>

Neural networks that are trained to perform specific tasks must be 
developed through a supervised learning procedure. This normally takes 
the form of direct supervision of synaptic plasticity. We explore the 
idea that supervision takes place instead through the modulation of 
neuronal excitability. Such supervision can be done using conventional 
synaptic feedback pathways rather than requiring the hypothetical 
actions of unknown modulatory agents. During task learning, supervised 
response modulation guides Hebbian synaptic plasticity indirectly by 
establishing appropriate patterns of correlated network activity. This 
results in robust learning of function approximation tasks even when 
multiple output units representing different functions share large 
amounts of common input. Reward-based supervision is also studied and a 
number of potential advantages of neuronal response modulation are 
identified.