Abstract
Objective. The prospect of real-time and on-node spike sorting provides a genuine opportunity to push the envelope of large-scale integrated neural recording systems. In such systems the hardware resources, power requirements and data bandwidth increase linearly with channel count. Event-based (or data-driven) processing can provide here a new efficient means for hardware implementation that is completely activity dependant. In this work, we investigate using continuous-time level-crossing sampling for efficient data representation and subsequent spike processing. Approach. (1) We first compare signals (synthetic neural datasets) encoded with this technique against conventional sampling. (2) We then show how such a representation can be directly exploited by extracting simple time domain features from the bitstream to perform neural spike sorting. (3) The proposed method is implemented in a low power FPGA platform to demonstrate its hardware viability. Main results. It is observed that considerably lower data rates are achievable when using 7 bits or less to represent the signals, whilst maintaining the signal fidelity. Results obtained using both MATLAB and reconfigurable logic hardware (FPGA) indicate that feature extraction and spike sorting accuracies can be achieved with comparable or better accuracy than reference methods whilst also requiring relatively low hardware resources. Significance. By effectively exploiting continuous-time data representation, neural signal processing can be achieved in a completely event-driven manner, reducing both the required resources (memory, complexity) and computations (operations). This will see future large-scale neural systems integrating on-node processing in real-time hardware.
Highlights
Electrophysiology provides a powerful tool for observing neural signals by recording extracellular biopotentials [1]
Considerably lower data rates are achieved with LC analogue-to-digital converter (ADC), while at resolutions higher than 7 bits, LC ADCs produced more data due to the excessive amount of events triggered on the slopes of spikes
The results demonstrate that event-driven features (EDF) features achieve a high sorting accuracy, the best performance is still achieved with conventional features sampled with relatively high resolutions and sampling rates (e.g. principal component analysis (PCA) at 24 kHz and with 12-bit)
Summary
Electrophysiology provides a powerful tool for observing neural signals by recording extracellular biopotentials [1]. This allows us to study single unit activity that is of great importance to understanding fundamental mechanisms of the brain, such as neural coding and plasticity [2]. With recent advances in microfabrication and integrated electronic technology, it is possible to integrate electrodes with amplification and data conversion electronics, creating several hundreds of recording channels to monitor the spiking activity of neurons in real-time [1, 6, 7]. The number of channels in a single device has been doubling every 7 years [8].
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