The whole system noise for Open Ephys is not explicitly mentioned in the documentation

The Axion Maestro Edge is designed as an out-of-the-box benchtop electrophysiology system with maximum comfort and usability. Although it has the highest price per channel, it is also an incubator. The Intan RHD USB interface board and head stages require more effort to calibrate, ground, and shield. Unlike Axion, Intan designs and code are open source. Intan bio-amplifier chips have been used in many open source systems, including Intsy, Willow, Open Ephys, and now Piphys. Both Intan and Axion systems provide valuable perspectives for comparison to Piphys. Axion produces the lowest noise baseline but has a different bio-amplifier circuit. Piphys and Intan have the same bio-amplifier chip; therefore Intan is a good reference for ensuring Piphys has the same noise floor and low EMI. Piphys and Intan RHD interface board differ in the way they sample the bio-amplifier. Specifically, Intan has more stable sampling with FPGA, while Piphys samples the chip with a CPU, which has more clock jitter . Overall the neural waveforms recorded on both systems are statistically comparable in shape for neural spikes for the detected neuron. Other comparable platforms in the literature include Intsy, Willow, and Open Ephys. Intsy was designed for measuring gastrointestinal , cardiac , neural , and neuromuscular signals. Willow was designed for high channel count neural probes and resolved the need for many computers by writing data directly to hard drives. Open Ephys is an alternative system to Intan integrating more features into their GUI for closed-loop experiments and plugin-based workflows *. Noise measurements for Piphys, Intan, and Axion were experimentally recorded, blueberry pot while noise measurements for Intsy, Willow, and Open Ephys were cited. Intan claims 2.4 μV RMS as typical in the datasheet for their chips # which was likely inherited into Open Ephys documentation.

Remote longitudinal recording of neural circuits on an accessible platform will open up many exciting avenues for research into the physiology, organization, development, and adaptation of neural tissue. Integration with cloud software will allow in-depth experimentation and automation of analysis. The proof of principle for Piphys has been shown on 2D cultures. As experiments with other devices have shown, it should be applicable to measurements of 3D brain organoids, which are becoming an increasingly popular model for studying human brain tissue developmentand function. One example application of Piphys would be monitoring how genotypes affect neural activity over the course of organoid development. More generally, IoT devices would allow less invasive and less laborious collection of longitudinal datasets of organoid development, to benchmark what wild-type organoid activity looks like throughout the first few months of growth. It would be interesting to compare whether different protocols and cell lines affect organoid activity over the course of development. IoT devices could be distributed and shared to compare whether organoid datasets are replicable and comparable between different labs, using the same low-cost hardware. Many electrode probes have been designed to interface with tissues to provide measurement points for voltage recordings. Future work on Piphys would involve expanding the number of different electrodes types for long-term culture of the biological sample through collaborations with other research groups. Future work on Piphys also includes increasing sampling rate and precision of timing in between samples. Currently, the Raspberry Pi CPU samples the Intan RHD2132 bio-amplifier chip, and the sampling rates are limited by the CPU’s ability to multitask. Future solutions may involve adding another CPU or FPGA to the hardware shield.

The platform will continue to be improved, and its modularity allows adapting hardware and software components as different needs arise. The current proof of concept design is based on a Raspberry Pi chip and uses one 32 channel chip attached to one of the SPI ports. The system can be easily extended to sample 64 channels . The channel number can be doubled if the design would include an FPGA and alternative Intan chips that have 64 channels/chip . However, the true scalability advantage of the proposed system lies in its open source and open hardware architecture. If the number of channels is insufficient, the shield board could be modified to accept multiple Raspberry Pi’s, therefore, increase the number of channels. Piphys is the only electrophysiology device that supports Internet of Things software integration out of the box. The IoT hardware modules and cloud software allow for horizontal scalability, enabling long-term observations of development, organization, and neural activity at scale, and integration with other IoT sensors. Piphys has a low entry cost, and the cost per channel can also be significantly lowered by increasing the number of channels supported per device. This would be accomplished by engineering an inexpensive FPGA into the controller shield to sample multiple bio-amplifier chips and buffer those readings for the Pi. Piphys can have a large cost reduction if extra specialty connectors and adapters are removed and it is fitted with a USB cable which is less expensive. The signal-to-noise ratio could be improved by enabling and tuning on-chip filtering, and improving Faraday cage shielding. In vitro cultures typically fire with amplitudes between 10 – 40 μV . They demand sensitive recording equipment, as an increase of just afew μV in noise for spikes on the lower end of the spectrum can be considered a non-trivial variable. Overall, the open source Piphys design, programmability, and extreme flexibility of the Raspberry Pi significantly lowers the entry barrier of the electrophysiology system, providing an opportunity for broader applications in education and research.Prior to cell culture, the electrode surfaces of 6-well Axion plates were coated with 10 mg/mL poly-D-lysine at room temperature overnight.

The following day, plates were rinsed 4 times with water and dried at room temperature. Primary cells were obtained from human brain tissue at gestational week 21. Briefly, cortical tissue was cut into small pieces, incubated in 0.25% trypsin for 30 minutes, then triturated in the presence of 10 mg/mL DNAse and passed through a 40 μm cell strainer. Cells were spun down and resuspended in BrainPhys supplemented with B27 , N2 , and penicillin-streptomycin , then diluted to a concentration of 8,000,000 cells/mL. Laminin was added to the final aliquot of cells, and a 10 μL drop of cells was carefully pipetted directly onto the dried, PDL-coated electrodes, forming an intact drop. The plate was transferred to a 37 °C, 5% CO2 incubator for 1 hour to allow the cells to settle, then 200 μL of supplemented BrainPhys media was gently added to the drops. The following day, another 800 μL of media was added, and each well was kept at 1 mL media for the duration of the cultures, with half the volume exchanged with fresh media every other day. Activity was first observed at 14 days in culture, and the second recordings were performed on day 42 of culture.The power supplied to the Raspberry Pi is through a mains adapter plugged into the wall outlet. To reduce environmental noise and maximize the signal-to-noise ratio , we use a Faraday cage during recording. The Faraday cage is made of 1 mm thick steel and connected to the wall outlet ground. For noise measurement benchmarks on Piphys, nursery pots an empty Axion plate was filled with the same media used in cell culture and placed in the Faraday cage. The noise baseline of this media-only system was an average of 2.36 ± 0.4 μV RMS for all the channels with software filters. Comparison of the baseline noise we measured for Piphys, Intan, and Axion is in Table 1. During the experiment, the systems were compared by measuring the same neural culture on the same plate within a similar time frame. Recordings occurred within 1 to 3 hours of each other. A 300–6000 Hz 3rd order Butterworth bandpass filter was used to attenuate frequency components outside the neural activity range after the recording. Data was analyzed by a spike sorting algorithm and shown side by side in Figure 6 over an identical time length. Instructions and source files for construction of Piphys hardware and software are available open source on GitHub ††. All files are provided ‘as is’ and endusers are encouraged to freely use and adapt the system for their own application-specific protocols.The printed circuit board was designed in Autodesk Eagle. The board has four layers of copper. The top and bottom layers of the board are GND, while the two layers inside are signal and power. Every signal via has a ground via next to it to sink EMI as signals switch layers. The layout of the circuit board is done in modules. Via stitching was done around the perimeter and throughout the board area to separate modules and fill in areas with no components. The amplifier chip and Raspberry Pi computer are separated by a cable such that noise from the computer would not interfere with the sensitive neural signal recording. During data acquisition, all of the electronics and biology were shielded by a 1 mm thick steel faraday cage.We deployed servers and cloud computing platforms to achieve permanent data storage and messaging between the local device and the dashboard. We used Remote Dictionary Server , Amazon Web Services Internet of Things , and Simple Storage Service .

All services are platform agnostic and can be hosted anywhere. For our particular experimental setup, Redis and S3 were hosted on the Pacific Research Platform [30]. The Internet of Things service with MQTT messaging and device management was coordinated through Amazon Web Services . The dashboard was hosted on a server at UC Santa Cruz. The thresholding spike detection shown in the dashboard runs inside a Docker container, which reads from the Piphys data Redis stream and writes to another Redis stream shown in the dashboard. In our case the Docker, the Redis service and the dashboard run on the Pacific Research Platform . However, this could be transferred to any cloud storage provider . The IoT architecture of these cloud services is explicitly described in [53].Redis, near real-time data stream—Neuronal action potential recording with a high sample rate and multiple channels requires a high throughput pipeline to make near real-time streaming possible. Remote Dictionary Server is a good choice for the implementation of this objective. It is a high-speed cloud-based data structure store that can be used as a cache, message broker, and database. Based on bench marking results, Redis can handle hundreds of thousands of requests per second. The highest data rate for every push from Piphys system to Redis is 7.68 Mb for each second .Pi data stream to Redis requires the network bandwidth to be at least 7.68 Mbps so that uploading to the Dashboard through Redis can be uninterrupted. Internet of Things communication—The dashboard is programmed to be an IoT device that sends Message Queuing Telemetry Transport messages to control and check the Piphys system. In response, the Piphys subscribes to a particular MQTT topic to wait for instructions. The AWS IoT supports the communication of hundreds of devices, making the Piphys system’s extension to a large scale possible in the future. Simple Storage Service —The Simple Storage Service is the final data storage location. S3 is accessible from anywhere at any time on the internet. It supports both management from a terminal session and integration to a custom web browser application. After each experiment, a new identifier will be updated on the dashboard. When a user asks for a specific experiment result, the dashboard can pull the corresponding data file directly from S3 for visualization.Managing the benefits people receive from nature, or ecosystem services, requires a detailed understanding of ecosystem processes. In particular, biodiversity-driven services, such as pest control on farms, requires knowledge of cropping systems, the habitats in and around croplands, and the interactions among the many organisms that inhabit them. Interactions are complex and often change over space and time ; therefore, a critical first step is identifying the species and populations that provide benefits to society . Identifying service providers, however, may not be straightforward. For example, predation is rarely witnessed directly, making it difficult to identify the predators of crop pests. Pest control is a critical service; in the United States, insect predators save farmers billions of dollars annually in avoided pest damage . Several different techniques have been utilized to identify predator–prey interactions. An indirect approach is using stable isotopes to determine trophic positions .