Molecular Devices® has announced the launch of its next generation Axon™ Digidata® 1550 Low-Noise Data Acquisition System. Considered the industry standard in neuroscience research labs, the new Digidata Platform enables the parallel patching of up to eight cells simultaneously to provide better insight of neuronal network function and capture more data points per experiment. This new system extends the precision and quality of the existing Axon Digitizer platform to include a higher sampling rate to monitor rapid responders, combined with enhanced performance in low-noise, high resolution 16-bit data acquisition.
The Digidata 1550 Digitizer features eight independent analog output channels to simultaneously send command waveforms to multiple cells at one time, essential for higher throughput toxicology experiments or to study synaptic function in complex neuronal networks. Combined with a sampling rate of up to 500 kHz per channel, the Digidata 1550 Digitizer allows researchers to gather more data points in demanding experiments such as nanopore and bilayer studies. This rapid rate monitors the fastest of signals in the biorelevant frequency range, whilst achieving a total data throughput rate of 4 mega-samples per second. Independent analog-to-digital converters for each input channel also ensure low crosstalk levels and high data acquisition rates, making the system one of the quietest digitizers on the market.
Designed for intuitive set-up and fast results, the Digidata 1550 Digitizer comes with AxoScope Data Acquisition Software for Microsoft Windows OS (Windows 7 32- or 64-bit) and is ready to acquire data immediately after installation - absolutely no programming is necessary.
Klaus Lun, Vice President of Product Marketing at Molecular Devices commented: "The Digidata 1550 Digitizer builds on Molecular Devices' experience in electrophysiology and biophysical research fields. Our commitment to bring additional utility to the conventional electrophysiology hardware and software allows our customers further understanding of neuronal networks, allowing researchers to make better, quicker decisions based on robust data.”