A scriptable DSP-based system for dynamic conductance injection.
A variety of software and hardware systems have been developed to inject controlled electrical conductances into excitable cells, to investigate the physiological mechanisms of action potential generation. These systems face several challenges: the need to model complex conductances, including voltage-gated ion channels, synaptic conductances controlled by electrical models of entire cells or even networks of cells, to do so rapidly and stably, with precisely controlled update intervals of 20micros or less, and to present an easy and flexible interface to the user, allowing new experiments to be designed and executed easily. In this paper I describe a new software system (SM-2) which is designed to meet these requirements, and which runs on the current generation of digital-signal-processing (DSP) analog input-output I/O boards, hosted in Windows PCs. Its key innovation is its configurability by simple user-written text scripts, or "scriptability", which gives it a high flexibility of purpose, and allows non-programmers the capacity to rapidly design and use new Hodgkin-Huxley-type active conductances, conductances with arbitrary current-voltage relationships, Markov process conductance mechanisms with user-specified rate matrices, and hybrid networks of virtual cells. At the same time, the hardware platform allows this to be achieved with a fast and accurately timed input-computation-output cycle.
Robinson
Hugh P C
HP
Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3EG, UK. hpcr@cam.ac.uk
D000818:Animals; D004594:Electrophysiology / Q000379:methods*; D008390:Markov Chains; D009431:Neural Conduction / Q000502:physiology; D018408:Patch-Clamp Techniques / Q000295:instrumentation*; D011381:Programming Languages; D051381:Rats; D017208:Rats, Wistar; D012815:Signal Processing, Computer-Assisted; D012984:Software; D013003:Somatosensory Cortex / Q000502:physiology; D013569:Synapses / Q000502:physiology