Abstract
Objective. Longitudinal observation of single unit neural activity from large numbers of cortical neurons in awake and mobile animals is often a vital step in studying neural network behaviour and towards the prospect of building effective brain–machine interfaces (BMIs). These recordings generate enormous amounts of data for transmission and storage, and typically require offline processing to tease out the behaviour of individual neurons. Our aim was to create a compact system capable of: (1) reducing the data bandwidth by circa 2 to 3 orders of magnitude (greatly improving battery lifetime and enabling low power wireless transmission in future versions); (2) producing real-time, low-latency, spike sorted data; and (3) long term untethered operation. Approach. We have developed a headstage that operates in two phases. In the short training phase a computer is attached and classic spike sorting is performed to generate templates. In the second phase the system is untethered and performs template matching to create an event driven spike output that is logged to a micro-SD card. To enable validation the system is capable of logging the high bandwidth raw neural signal data as well as the spike sorted data. Main results. The system can successfully record 32 channels of raw neural signal data and/or spike sorted events for well over 24 h at a time and is robust to power dropouts during battery changes as well as SD card replacement. A 24 h initial recording in a non-human primate M1 showed consistent spike shapes with the expected changes in neural activity during awake behaviour and sleep cycles. Significance. The presented platform allows neural activity to be unobtrusively monitored and processed in real-time in freely behaving untethered animals—revealing insights that are not attainable through scheduled recording sessions. This system achieves the lowest power per channel to date and provides a robust, low-latency, low-bandwidth and verifiable output suitable for BMIs, closed loop neuromodulation, wireless transmission and long term data logging.
Highlights
There is currently a global effort to expand our knowledge of the brain by recording large-scale cortical neural activity over a long period of time
This paper describes an end-to-end solution based on the aforementioned two-step process and template matching
(ii) During the online template matching phase, the headstage is untethered from the computer and a small, low-power FPGA is used to perform template matching based spike sorting
Summary
There is currently a global effort to expand our knowledge of the brain by recording large-scale cortical neural activity over a long period of time. The ‘Brain Research through Advancing Innovative Neurotechnologies’ (BRAIN) Initiative in particular is looking to deliver a step change in our understanding of the brain’s networks and function—a key element of this is research to advance neural recording from the current state-of-the-art (short term targeted recordings of in the order of 1000s of channels [1,2,3]) to round-the-clock recordings of more than 100 000 channels in freely behaving animals [4] These ever increasing channel counts and recording periods naturally lead to an enormous amount of raw data to be stored and processed to extract useful information. Real time classification is performed using template matching on an FPGA Such a system reduces the required data storage by circa 2 to 3 orders of magnitude (assuming a nominal spike rate of ten spikes per second per channel [23]), and provides an architecture which addresses the key challenges of data transmission bandwidth and system power consumption for scaling up to systems with thousands of channels. The remainder of this paper is organised as follows: section 2 describes the system design and implementation; section 3 presents performance metrics from benchtop electronic testing as well as the results obtained from in vivo experiments; and section 4 discusses the results, limitations and expected future development of the system
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