Closed-loop, multichannel experimentation using the open-source NeuroRighter electrophysiology platform

• Authors: Jonathan P. Newman, Georgia Institute of Technology; Riley Zeller-Townson, Georgia Institute of Technology; Ming-Fai Fong, Georgia Institute of Technology; Sharanya Arcot Desai, Georgia Institute of Technology; Robert Gross, Emory University; Steve Potter, Emory University
• Journal Title: Frontiers in Neural Circuits
• Volume: Volume 6, Number 98
• Publisher: Frontiers Media | 2013-01-18, Pages 1-35
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.3389/fncir.2012.00098
• Subjects: Biology, Neuroscience; Engineering, Biomedical; Biology, Physiology
• Keywords: Science & Technology; Life Sciences & Biomedicine; Neurosciences; Neurosciences & Neurology; NEUROSCIENCES; closed-loop; multichannel; real-time; multi-electrode; micro-electrode array; electrophysiology; open-source; network; RESPONSIVE CORTICAL STIMULATION; NEOCORTICAL NEURONS; LONG-TERM; EPILEPSY; DYNAMICS; CELLS; INFORMATION; PLASTICITY; PREDICTION; CULTURES

Abstract

Single neuron feedback control techniques, such as voltage clamp and dynamic clamp, have enabled numerous advances in our understanding of ion channels, electrochemical signaling, and neural dynamics. Although commercially available multichannel recording and stimulation systems are commonly used for studying neural processing at the network level, they provide little native support for real-time feedback. We developed the open-source NeuroRighter multichannel electrophysiology hardware and software platform for closed-loop multichannel control with a focus on accessibility and low cost. NeuroRighter allows 64 channels of stimulation and recording for around US $10,000, along with the ability to integrate with other software and hardware. Here, we present substantial enhancements to the NeuroRighter platform, including a redesigned desktop application, a new stimulation subsystem allowing arbitrary stimulation patterns, low-latency data servers for accessing data streams, and a new application programming interface (API) for creating closed-loop protocols that can be inserted into NeuroRighter as plugin programs. This greatly simplifies the design of sophisticated real-time experiments without sacrificing the power and speed of a compiled programming language. Here we present a detailed description of NeuroRighter as a stand alone application, its plugin API, and an extensive set of case studies that highlight the system's abilities for conducting closed-loop, multichannel interfacing experiments.