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Abstract Motivated by the unexplored potential of in vitro neural systems for computing and by the corresponding need of versatile, scalable interfaces for multimodal interaction, an accurate, modular, fully customizable, and portable recording/stimulation solution that can be easily fabricated, robustly operated, and broadly disseminated is presented. This approach entails a reconfigurable platform that works across multiple industry standards and that enables a complete signal chain, from neural substrates sampled through micro‐electrode arrays (MEAs) to data acquisition, downstream analysis, and cloud storage. Built‐in modularity supports the seamless integration of electrical/optical stimulation and fluidic interfaces. Custom MEA fabrication leverages maskless photolithography, favoring the rapid prototyping of a variety of configurations, spatial topologies, and constitutive materials. Through a dedicated analysis and management software suite, the utility and robustness of this system are demonstrated across neural cultures and applications, including embryonic stem cell‐derived and primary neurons, organotypic brain slices, 3D engineered tissue mimics, concurrent calcium imaging, and long‐term recording. Overall, this technology, termed “mind in vitro” to underscore the computing inspiration, provides an end‐to‐end solution that can be widely deployed due to its affordable (>10× cost reduction) and open‐source nature, catering to the expanding needs of both conventional and unconventional electrophysiology.
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Light-weight electrophysiology hardware and software platform for cloud-based neural recording experiments
Abstract Objective.Neural activity represents a functional readout of neurons that is increasingly important to monitor in a wide range of experiments. Extracellular recordings have emerged as a powerful technique for measuring neural activity because these methods do not lead to the destruction or degradation of the cells being measured. Current approaches to electrophysiology have a low throughput of experiments due to manual supervision and expensive equipment. This bottleneck limits broader inferences that can be achieved with numerous long-term recorded samples.Approach.We developed Piphys, an inexpensive open source neurophysiological recording platform that consists of both hardware and software. It is easily accessed and controlled via a standard web interface through Internet of Things (IoT) protocols.Main results.We used a Raspberry Pi as the primary processing device along with an Intan bioamplifier. We designed a hardware expansion circuit board and software to enable voltage sampling and user interaction. This standalone system was validated with primary human neurons, showing reliability in collecting neural activity in near real-time.Significance.The hardware modules and cloud software allow for remote control of neural recording experiments as well as horizontal scalability, enabling long-term observations of development, organization, and neural activity at scale.
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- PAR ID:
- 10307389
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Journal of Neural Engineering
- Volume:
- 18
- Issue:
- 6
- ISSN:
- 1741-2560
- Page Range / eLocation ID:
- Article No. 066004
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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