Understanding how the brain works is arguably one of the most significant scientific challenges of our time and the focus of the BRAIN initiative. It is widely believed that neural circuit function is emergent, the result of complex interactions between constituents with individual neurons forming synaptic connections with thousands of other neurons. Mapping of these complex circuits has been virtually impossible because of the reliance on electrophysiological recordings which sample these networks extremely sparsely. These tools for extracellular spike recordings are only able to simultaneously record from several tens to a few hundred neurons. Raw signals from these recording electrodes are first filtered to remove out-of-band signals. Putative spike events are then detected and extracted. Finally, these snippets of time-series event are sorted, typically on the basis of waveform shapes, into clusters. Even at the very modest bandwidths for these systems, computing systems struggle to save the data and process the resulting data sets. Scalability of these measurement techniques by many orders of magnitude in recording density and channels will be essential to future progress in understanding neuron circuits.
This project is exploiting emerging electrophysiological recording systems in which the electrode (and channel) count is increased by almost three orders of magnitude over conventional systems with data bandwidths exceeding 1GB/sec. To handle these data bandwidths and resulting data volumes and deliver scalability, this project will develop dedicated hardware and associated algorithms for spike detection and sorting that allow these tasks to be performed in real-time in close proximity to the recording system. Compression by more than three orders of magnitude is possible by these means by taking advantage of the special spatiotemporal local structure in these data sets; by exploiting strong prior information about the spiking signal and reducing the dimensionality of the problem accordingly; and by adapting and extending modern scalable nonparametric Bayesian inference methods. In addition to providing important new tools for neuroscience, the tools developed here for scalable real-time event detection and annotation have broad applicability to other spatiotemporal data sets (or more generally, any data set comprising multiple streams of data, in which the streams could involve different data modalities) in which objects of interest are spatially and temporally localized with fixed spatial footprints. Examples abound in cell and molecular biology, particle and solid-state physics, financial monitoring, monitoring of power networks, and sensor networks.
