A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well.

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Beat-to-beat variability of the QT interval (QTV) measured on surface ECG has emerged as a potential marker for ventricular repolarization instability and has been used along with heart rate variability (HRV) to predict arrhythmic risk. Since measurement modalities of QTV have not been standardized, the objective of this study was to investigate the effect of ECG recording duration on QTV as well as HRV. Using a database of 30 min ECG recorded from 500 patients with acute myocardial infraction during rest, we extracted RR and QT interval time series and estimated different HRV and QTV metrics over windows of varying length. Analysis of variance (ANOVA) and intra-class correlation analyses were computed to investigate the effect of recording length on consistency and short-term reproducibility of HRV and QTV variables. Good consistency (non-significant ANOVA results) and short-term reproducibility (intra-class correlation coefficients >0.8) were demonstrated for all but standard deviation based metrics when at least 200 beats were included in the estimation. In conclusion, QTV can be quantified from resting ECG with good short-term consistency and reproducibility that is comparable to that of HRV.

In this paper an implementation of Hodgkin-Huxley single neuron is provided. Unlike almost all of the existing implementations, the arithmetic logics are implemented with computation techniques (i.e. CORDIC) and look-up-tables (LUTs) are used only in few modules. This makes our design more robust and flexible to simulate the functionality of a large network of neurons. Most of the previous works are based on the software implementations which overshadow the parallel nature of the neural system or using LUTs for hardware implementation which needs more space and also limited flexibility. In this paper, an FPGA is selected as our hardware implementation platform to provide an appropriate reconfigurable platform for simulating the functionality of a network of neurons. We validated our design based on our high level implementation of Hodgkin-Huxley neuron in MATLAB and report our implementation results based on Xilinx SPARTAN 3 FPGA in Xilinx ISE Design Suite.