Ph.D. dissertation: Hierarchical learning of conjunctive concepts in spiking neural nets

[Cengiz Gunay]

Dear connectionists,

My Ph.D. dissertation titled "Hierarchical learning of conjunctive
concepts in spiking neural networks", together with related publications,
is available from the web page:

http://www.cacs.louisiana.edu/~cxg9789/Neuroidal

Below you can find the abstract for the dissertation.

Abstract
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The temporal correlation hypothesis proposes that synchronous activity in 
different regions of the brain describes integral entities (von der 
Malsburg, 1981; Singer and Gray, 1995). This temporal binding approach is 
a possible solution to the longstanding binding problem of representing 
composite objects (Rosenblatt, 1961). To complement the dynamic nature of 
temporal binding, a recruitment learning method has been proposed for 
providing long-term storage (Feldman, 1982; Valiant, 1994). We improve the 
recruitment method to use a more biologically realistic and 
computationally powerful spiking neuron model.

However, using continuous-time spiking neurons and brain-like connectivity 
assumptions poses new problems in hierarchical recruitment. First, we 
propose timing parameter constraints for recruitment over asymmetrically 
connected delay lines. We verify these constraints using simulations. 
These constraints are useful for both building abstract networks and 
providing insight into bio-mechanisms that ensure signal integrity in the 
brain. As a second problem, we calculate the required feedforward 
excitatory and lateral inhibitory connection densities for stable 
propagation of activity in hierarchical structures of the network. We give 
analytic solutions using a stochastic population model of a simplified 
layered network. Our approach is independent of the network size, but 
depends on lateral inhibition and noisy feedforward delays.

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Thanks,
Cengiz Gunay
Postdoctoral Fellow, Dept. of Biology
Emory University, Atlanta, GA, USA.
--
cgunay at emory.edu  cengique at users.sf.net 	cengique at yahoo.com
Lab: +1-404-727-4103	Home/Cell: +1-337-255-3660
http://www.cacs.louisiana.edu/~cxg9789
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