\chapter{Introduction}

	\hsp{8mm} The primary goal of this thesis work is to create a
	novel simulator based on biological principles of a mammalian
	neocortex. Parallel processing of this very large-scale
	object-oriented neural network is key for modeling biological accuracy
	of synaptic, membrane, and network dynamics, while preserving
	relationships among neurons. Clustering
	algorithms applied to the dense cell connection matrix enable
	load balancing and data parallelism by organizing groups of
	cells onto a particular node, thus reducing the performance of
	inter-nodal communication.

	The design of the simulator was done using object-oriented
	techniques to facilitate modularity, salability, and
	portability. This approach allowed us to encapsulate functionality
	into specific objects and to swap these objects out with others
	that employed different implementations. We did extensive research
	into the neuro-biological principles governing cortical cell
	dynamics in order to accurately represent a large-scale network
	{\it in silico}. It was through this research that we understood that
	the importance of a biological neural network rested in the
	relationships among neurons in a given community communicating
	through electro-chemical processes. Out object-oriented design
	allowed us to model these relationships and to utilize spike
	encoding in our neural communication paradigm.

	Once the simulator was designed and implemented, optimization
	was performed in several key areas to increase performance and
	conserve memory. The object-oriented design employed in the
	development of this simulator allows for a coarse or fine grain
	parallel model to be run. IN order to be biologically realistic,
	however, a very fine grain parallel model is used to simulate
	the highly communicative cell/compartment connections within
	the cortex. It is because of this fine grain approach that a
	high-speed/low-latency interconnection network is utilized.

	The rest of this thesis, is structured as follows: In Chapter 2,
	we present a review of current technology, including other neural
	simulators being utilized by researchers, as well as current
	spike encoding techniques. Chapter 3 give a background into the
	biomechanics of neurons, synapses and channels, as well as the
	computational design that is based upon this biology. In Chapter
	4 we cover our computational results, including a discussion of
	the parallel implementation. We also show the biological results
	of the simulator and the biological accuracy of them. Chapter
	5 details our conclusions and a sampling of future work to be
	done on the simulator.

