Mak Sui Tung Terrence. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 132-142). / Abstracts in English and Chinese. / Abstract --- p.i / List of Tables --- p.iv / List of Figures --- p.v / Acknowledgements --- p.ix / Chapter 1. --- Introduction --- p.1 / Chapter 1.1 --- Bioinformatics --- p.1 / Chapter 1.2 --- Neural Prosthetics --- p.4 / Chapter 1.3 --- Learning in Uncertainty --- p.5 / Chapter 1.4 --- The Field Programmable Gate Array (FPGAs) --- p.7 / Chapter 1.5 --- Scope of the Thesis --- p.10 / Chapter 2. --- A Hybrid GA-DP Approach for Searching Equivalence Sets --- p.14 / Chapter 2.1 --- Introduction --- p.16 / Chapter 2.2 --- Equivalence Set Criterion --- p.18 / Chapter 2.3 --- Genetic Algorithm and Dynamic Programming --- p.19 / Chapter 2.3.1 --- Genetic Algorithm Formulation --- p.20 / Chapter 2.3.2 --- Bounded Mutation --- p.21 / Chapter 2.3.3 --- Conditioned Crossover --- p.22 / Chapter 2.3.4 --- Implementation --- p.22 / Chapter 2.4 --- FPGAs Implementation of GA-DP --- p.24 / Chapter 2.4.1 --- System Overview --- p.25 / Chapter 2.4.2 --- Parallel Computation for Transitive Closure --- p.26 / Chapter 2.4.3 --- Genetic Operation Realization --- p.28 / Chapter 2.5 --- Discussion --- p.30 / Chapter 2.6 --- Limitation and Future Work --- p.33 / Chapter 2.7 --- Conclusion --- p.34 / Chapter 3. --- An FPGA-based Architecture for Maximum-Likelihood Phylogeny Evaluation --- p.35 / Chapter 3.1 --- Introduction --- p.36 / Chapter 3.2 --- Maximum-Likelihood Model --- p.39 / Chapter 3.3 --- Hardware Mapping for Pruning Algorithm --- p.41 / Chapter 3.3.1 --- Related Works --- p.41 / Chapter 3.3.2 --- Number Representation --- p.42 / Chapter 3.3.3 --- Binary Tree Representation --- p.43 / Chapter 3.3.4 --- Binary Tree Traversal --- p.45 / Chapter 3.3.5 --- Maximum-Likelihood Evaluation Algorithm --- p.46 / Chapter 3.4 --- System Architecture --- p.49 / Chapter 3.4.1 --- Transition Probability Unit --- p.50 / Chapter 3.4.2 --- State-Parallel Computation Unit --- p.51 / Chapter 3.4.3 --- Error Computation --- p.54 / Chapter 3.5 --- Discussion --- p.56 / Chapter 3.5.1 --- Hardware Resource Consumption --- p.56 / Chapter 3.5.2 --- Delay Evaluation --- p.57 / Chapter 3.6 --- Conclusion --- p.59 / Chapter 4. --- Field Programmable Gate Array Implementation of Neuronal Ion Channel Dynamics --- p.61 / Chapter 4.1 --- Introduction --- p.62 / Chapter 4.2 --- Background --- p.63 / Chapter 4.2.1 --- Analog VLSI Model for Hebbian Synapse --- p.63 / Chapter 4.2.2 --- A Unifying Model of Bi-directional Synaptic Plasticity --- p.64 / Chapter 4.2.3 --- Non-NMDA Receptor Channel Regulation --- p.65 / Chapter 4.3 --- FPGAs Implementation --- p.65 / Chapter 4.3.1 --- FPGA Design Flow --- p.65 / Chapter 4.3.2 --- Digital Model of NMD A and AMPA receptors --- p.65 / Chapter 4.3.3 --- Synapse Modification --- p.67 / Chapter 4.4 --- Results --- p.68 / Chapter 4.4.1 --- Simulation Results --- p.68 / Chapter 4.5 --- Discussion --- p.70 / Chapter 4.6 --- Conclusion --- p.71 / Chapter 5. --- Continuous-Time and Discrete-Time Inference Networks for Distributed Dynamic Programming --- p.72 / Chapter 5.1 --- Introduction --- p.74 / Chapter 5.2 --- Background --- p.77 / Chapter 5.2.1 --- Markov decision process (MDPs) --- p.78 / Chapter 5.2.2 --- Learning in the MDPs --- p.80 / Chapter 5.2.3 --- Bellman Optimal Criterion --- p.80 / Chapter 5.2.4 --- Value Iteration --- p.81 / Chapter 5.3 --- A Computational Framework for Continuous-Time Inference Network --- p.82 / Chapter 5.3.1 --- Binary Relation Inference Network --- p.83 / Chapter 5.3.2 --- Binary Relation Inference Network for MDPs --- p.85 / Chapter 5.3.3 --- Continuous-Time Inference Network for MDPs --- p.87 / Chapter 5.4 --- Convergence Consideration --- p.88 / Chapter 5.5 --- Numerical Simulation --- p.90 / Chapter 5.5.1 --- Example 1: Random Walk --- p.90 / Chapter 5.5.2 --- Example 2: Random Walk on a Grid --- p.94 / Chapter 5.5.3 --- Example 3: Stochastic Shortest Path Problem --- p.97 / Chapter 5.5.4 --- Relationships Between λ and γ --- p.99 / Chapter 5.6 --- Discrete-Time Inference Network --- p.100 / Chapter 5.6.1 --- Results --- p.101 / Chapter 5.7 --- Conclusion --- p.102 / Chapter 6. --- On Distributed g-Learning Network --- p.104 / Chapter 6.1 --- Introduction --- p.105 / Chapter 6.2 --- Distributed Q-Learniing Network --- p.108 / Chapter 6.2.1 --- Distributed Q-Learning Network --- p.109 / Chapter 6.2.2 --- Q-Learning Network Architecture --- p.111 / Chapter 6.3 --- Experimental Results --- p.114 / Chapter 6.3.1 --- Random Walk --- p.114 / Chapter 6.3.2 --- The Shortest Path Problem --- p.116 / Chapter 6.4 --- Discussion --- p.120 / Chapter 6.4.1 --- Related Work --- p.121 / Chapter 6.5 --- FPGAs Implementation --- p.122 / Chapter 6.5.1 --- Distributed Registering Approach --- p.123 / Chapter 6.5.2 --- Serial BRAM Storing Approach --- p.124 / Chapter 6.5.3 --- Comparison --- p.125 / Chapter 6.5.4 --- Discussion --- p.127 / Chapter 6.6 --- Conclusion --- p.128 / Chapter 7. --- Summary --- p.129 / Bibliography --- p.132 / Appendix / Chapter A. --- Simplified Floating-Point Arithmetic --- p.143 / Chapter B. --- "Logarithm, Exponential and Division Implementation" --- p.144 / Chapter B.1 --- Introduction --- p.144 / Chapter B.2 --- Approximation Scheme --- p.145 / Chapter B.2.1 --- Logarithm --- p.145 / Chapter B.2.2 --- Exponentiation --- p.147 / Chapter B.2.3 --- Division --- p.148 / Chapter C. --- Analog VLSI Implementation --- p.150 / Chapter C.1 --- Site Function --- p.150 / Chapter C.1.1 --- Multiplication Cell --- p.150 / Chapter C.2 --- The Unit Function --- p.153 / Chapter C.3 --- The Inference Network Computation --- p.154 / Chapter C.4 --- Layout --- p.157 / Chapter C.5 --- Fabrication --- p.159 / Chapter C.5.1 --- Testing and Characterization --- p.161

Hybrid neural-microelectronic systems, systems composed of biological neural networks and neuronal models, have great potential for the treatment of neural injury and disease. The utility of such systems will be ultimately determined by the ability of the engineered component to correctly replicate the function of biological neural networks. These models can take the form of mechanistic models, which reproduce neural function by describing the physiologic mechanisms that produce neural activity, and empirical models, which reproduce neural function through more simplified mathematical expressions. We present our research into the role of model complexity in creating robust and flexible behaviors in hybrid systems. Beginning with a complex mechanistic model of a leech heartbeat interneuron, we create a series of three systematically reduced models that incorporate both mechanistic and empirical components. We then evaluate the robustness of these models to parameter variation, and assess the flexibility of the models activities. The modeling studies are validated by incorporating both mechanistic and semi-empirical models in hybrid systems with a living leech heartbeat interneuron. Our results indicate that model complexity serves to increase both the robustness of the system and the ability of the system to produce flexible outputs.