Large-Scale Bio-Inspired FPGA Models for Path Planning.

The hippocampus provides significant inspiration for spatial navigation and memory in both humans and animals. Constructing large-scale spiking neural network (SNN) models based on the biological neural systems is an important approach to comprehend the computational principles and cognitive function of the hippocampus. Such models are usually implemented on neuromorphic computing platforms, which often have limited computing resources that constrain the achievable scale of the network. This work introduces a series of digital design methods to realize a Field-Programmable Gate Array (FPGA) friendly SNN model. The methods include FPGA-friendly nonlinear calculation modules and a fixed-point design algorithm. A brain-inspired large-scale SNN of ∼21 k place cells for path planning is mapped on FPGA. The results show that the path planning tasks in different environments are finished in real-time and the firing activities of place cells are successfully reproduced. With these methods, the achievable network size on one FPGA chip is increased by 1595 times with higher resource usage efficiency and faster computation speed compared to the state-of-the-art.

Adaptive closed-loop paradigm of electrophysiology for neuron models.

The traditional electrophysiological experiments based on an open-loop paradigm are relatively complicated and limited when facing an individual neuron with uncertain nonlinear factors. Emerging neural technologies enable tremendous growth in experimental data leading to the curse of high-dimensional data, which obstructs the mechanism exploration of spiking activities in the neurons. In this work, we propose an adaptive closed-loop electrophysiology simulation experimental paradigm based on a Radial Basis Function neural network and a highly nonlinear unscented Kalman filter. On account of the complex nonlinear dynamic characteristics of the real neurons, the proposed simulation experimental paradigm could fit the unknown neuron models with different channel parameters and different structures (i.e. single or multiple compartments), and further compute the injected stimulus in time according to the arbitrary desired spiking activities of the neurons. However, the hidden electrophysiological states of the neurons are difficult to be measured directly. Thus, an extra Unscented Kalman filter modular is incorporated in the closed-loop electrophysiology experimental paradigm. The numerical results and theoretical analyses demonstrate that the proposed adaptive closed-loop electrophysiology simulation experimental paradigm achieves desired spiking activities arbitrarily and the hidden dynamics of the neurons are visualized by the unscented Kalman filter modular. The proposed adaptive closed-loop simulation experimental paradigm can avoid the inefficiency of data at increasingly greater scales and enhance the scalability of electrophysiological experiments, thus speeding up the discovery cycle on neuroscience.

Comparison and Selection of Spike Encoding Algorithms for SNN on FPGA.

The information in Spiking Neural Networks (SNNs) is carried by discrete spikes. Therefore, the conversion between the spiking signals and real-value signals has an important impact on the encoding efficiency and performance of SNNs, which is usually completed by spike encoding algorithms. In order to select suitable spike encoding algorithms for different SNNs, this work evaluates four commonly used spike encoding algorithms. The evaluation is based on the FPGA implementation results of the algorithms, including calculation speed, resource consumption, accuracy, and anti-noiseability, so as to better adapt to the neuromorphic implementation of SNN. Two real-world applicaitons are also used to verify the evaluation results. By analyzing and comparing the evaluation results, this work summarizes the characteristics and application range of different algorithms. In general, the sliding window algorithm has relatively low accuracy and is suitable for observing signal trends. Pulsewidth modulated-Based algorithm and step-forward algorithm are suitable for accurate reconstruction of various signals except for square wave signals, while Ben's Spiker algorithm can remedy this. Finally, a scoring method that can be used for spiking coding algorithm selection is proposed, which can help to improve the encoding efficiency of neuromorphic SNNs.