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Related Experiment Video

Updated: Feb 21, 2026

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

Event-driven processing for hardware-efficient neural spike sorting.

Yan Liu¹, João L Pereira, Timothy G Constandinou

¹Centre for Bio-Inspired Technology, Imperial College London, SW7 2AZ, United Kingdom. Dept. of Electrical & Electronic Eng., Imperial College London, SW7 2BT, United Kingdom.

Journal of Neural Engineering

|October 6, 2017

Summary

This study introduces continuous-time level-crossing sampling for efficient neural spike sorting. The method reduces data rates and hardware resources, enabling real-time, on-node processing for large-scale neural recording systems.

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Last Updated: Feb 21, 2026

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

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Published on: February 10, 2017

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Area of Science:

Neuroscience
Signal Processing
Hardware Engineering

Background:

Large-scale neural recording systems face challenges with increasing hardware resources, power, and data bandwidth as channel count grows.
Event-based processing offers an activity-dependent, efficient approach for hardware implementation.

Purpose of the Study:

Investigate continuous-time level-crossing sampling for efficient neural signal representation and spike sorting.
Demonstrate hardware viability for on-node, real-time processing in neural recording systems.

Main Methods:

Compared continuous-time level-crossing sampling with conventional methods using synthetic neural datasets.
Extracted time-domain features from the bitstream for neural spike sorting.
Implemented the proposed method on a low-power FPGA platform.

Main Results:

Achieved significantly lower data rates with 7 bits or less while maintaining signal fidelity.
Demonstrated comparable or superior spike sorting accuracy to reference methods.
Required relatively low hardware resources on the FPGA platform.

Conclusions:

Continuous-time data representation enables event-driven neural signal processing, reducing resource and computation demands.
This approach facilitates real-time, on-node processing for future large-scale neural systems.