In this thesis, I have studied CMOS dendrites, implemented them on a reconfigurable analog platform and modeled them using MATLAB Simulink. The dendrite model was further used to build a computational model. I implemented a Hidden Markov Model (HMM) classifier to build a simple YES/NO wordspotter. I also discussed the inter-relation between neural systems, CMOS transistors and HMM networks. The physical principles behind the operation of silicon devices and biological structures are similar. Hence silicon devices can be used to emulate biological structures like dendrites. Dendrites are a branched, conductive medium which connect a neurons synapses to its soma. Dendrites were previously believed to be like wires in neural networks. However, recent research suggests that they have computational power. We can emulate dendrites using transistors in the Field Programmable Analog Array (FPAA). Our lab has built the Reconfigurable Analog Signal Processor (RASP) family of FPAAs which was used for the experiments. I analytically compared the mathematical model of dendrites to our model in silicon. The mathematical model based on the device physics of the silicon devices was then used to simulate dendrites in Simulink. An automated tool, sim2spice was then used to convert the Simulink model into a SPICE netlist, such that it can be implemented on a FPAA. This is an easier tool to use for DSP and Neuromorphic engineers who's primary areas of expertise isn't circuit design.