Recording data from one million neurons in real time
March 23, 2018
(credit: Getty)
Neuroscientists at the Neuronano Research Centre at Lund University in Sweden have developed and tested an ambitious new design for processing and storing the massive amounts of data expected from future implantable brain machine interfaces (BMIs) and brain-computer interfaces (BCIs).
The system would simultaneously acquire data from more than 1 million neurons in real time. It would convert the spike data (using bit encoding) and send it via an effective communication format for processing and storage on conventional computer systems. It would also provide feedback to a subject in under 25 milliseconds — stimulating up to 100,000 neurons.
Monitoring large areas of the brain in real time. Applications of this new design include basic research, clinical diagnosis, and treatment. It would be especially useful for future implantable, bidirectional BMIs and BCIs, which are used to communicate complex data between neurons and computers. This would include monitoring large areas of the brain in paralyzed patients, revealing an imminent epileptic seizure, and providing real-time feedback control to robotic arms used by quadriplegics and others.
The system is intended for recording neural signals from implanted electrodes, such as this 32-electrode grid, used for long-term, stable neural recording and treatment of neurological disorders. (credit: Thor Balkhed)
“A considerable benefit of this architecture and data format is that it doesn’t require further translation, as the brain’s [spiking] signals are translated directly into bitcode,” making it available for computer processing and dramatically increasing the processing speed and database storage capacity.
“This means a considerable advantage in all communication between the brain and computers, not the least regarding clinical applications,” says Bengt Ljungquist, lead author of the study and doctoral student at Lund University.
Future BMI/BCI systems. Current neural-data acquisition systems are typically limited to 512 or 1024 channels and the data is not easily converted into a form that can be processed and stored on PCs and other computer systems.
“The demands on hardware and software used in the context of BMI/BCI are already high, as recent studies have used recordings of up to 1792 channels for a single subject,” the researchers note in an open-access paper published in the journal Neuroinformatics.
That’s expected to increase. In 2016, DARPA (U.S. Defense Advanced Research Project Agency) announced its Neural Engineering System Design (NESD) program*, intended “to develop an implantable neural interface able to provide unprecedented signal resolution and data-transfer bandwidth between the human brain and the digital world. …
“Neural interfaces currently approved for human use squeeze a tremendous amount of information through just 100 channels, with each channel aggregating signals from tens of thousands of neurons at a time. The result is noisy and imprecise. In contrast, the NESD program aims to develop systems that can communicate clearly and individually with any of up to one million neurons in a given region of the brain.”
System architecture overview of storage for large amounts of real time neural data, proposed by Lund University researchers. A master clock pulse (a) synchronizes n acquisition systems (b), which handles bandpass filtering, spike sorting (for spike data), and down-sampling (for narrow band data), receiving electro-physiological data from subject (e). Neuronal spike data is encoded in a data grid of neurons time bins. (c). The resulting data grid is serialized and sent over to spike data storage in HDF5 file format (d), as well as to narrow band (f) and waveform data storage (g). In this work, a and b are simulated, c and d are implemented, while f and g are suggested (not yet
implemented) components. (credit: Bengt Ljungquis et al./Neuroinformatics)
* DARPA has since announced that it has “awarded contracts to five research organizations and one company that will support the Neural Engineering System Design (NESD) program: Brown University; Columbia University; Fondation Voir et Entendre (The Seeing and Hearing Foundation); John B. Pierce Laboratory; Paradromics, Inc.; and the University of California, Berkeley. These organizations have formed teams to develop the fundamental research and component technologies required to pursue the NESD vision of a high-resolution neural interface and integrate them to create and demonstrate working systems able to support potential future therapies for sensory restoration. Four of the teams will focus on vision and two will focus on aspects of hearing and speech.”
Abstract of A Bit-Encoding Based New Data Structure for Time and Memory Efficient Handling of Spike Times in an Electrophysiological Setup.
Recent neuroscientific and technical developments of brain machine interfaces have put increasing demands on neuroinformatic databases and data handling software, especially when managing data in real time from large numbers of neurons. Extrapolating these developments we here set out to construct a scalable software architecture that would enable near-future massive parallel recording, organization and analysis of neurophysiological data on a standard computer. To this end we combined, for the first time in the present context, bit-encoding of spike data with a specific communication format for real time transfer and storage of neuronal data, synchronized by a common time base across all unit sources. We demonstrate that our architecture can simultaneously handle data from more than one million neurons and provide, in real time (< 25 ms), feedback based on analysis of previously recorded data. In addition to managing recordings from very large numbers of neurons in real time, it also has the capacity to handle the extensive periods of recording time necessary in certain scientific and clinical applications. Furthermore, the bit-encoding proposed has the additional advantage of allowing an extremely fast analysis of spatiotemporal spike patterns in a large number of neurons. Thus, we conclude that this architecture is well suited to support current and near-future Brain Machine Interface requirements.
Bruce's Blog 