FPNA: interaction between FPGA and neural computation

An FPGA-based approach to high-speed simulation of conductance-based neuron models

The constant requirement for greater performance in neural model simulation has created the need for high-speed simulation platforms. We present a generalized, scalable field programmable gate array (FPGA)-based architecture for fast computation of neural models and focus on the steps involved in implementing a single-compartment and a two-compartment neuron model. Based on timing tests, it is shown that FPGAs can outperform traditional desktop computers in simulating these fairly simple models and would most likely provide even larger performance gains over computers in simulating more complex models. The potential of this method for improving neural modeling and dynamic clamping is discussed. In particular, it is believed that this approach could greatly speed up simulations of both highly complex single neuron models and networks of neurons. Additionally, our design is particularly well suited to automated parameter searches for tuning model behavior and to real-time simulation.