Abstract
Spike sorting is a crucial step to extract information from extracellular recordings. With new recording opportunities provided by the development of new electrodes that allow monitoring hundreds of neurons simultaneously, the scenario for the new generation of algorithms is both exciting and challenging. However, this will require a new approach to the problem and the development of a common reference framework to quickly assess the performance of new algorithms. In this work, we review the basic concepts of spike sorting, including the requirements for different applications, together with the problems faced by presently available algorithms. We conclude by proposing a roadmap stressing the crucial points to be addressed to support the neuroscientific research of the near future.
Highlights
Monitoring the activity of single neurons in vivo is the basis for understanding the brain mechanisms supporting behavior
The recorded data is lowpass filtered to obtain the so called Local Field Potentials (LFPs), which reflect the dynamics of the neural tissue surrounding the electrode
It has been shown that the identity of a visual stimulus can be decoded using the firing rates of neurons recorded in the human medial temporal lobe and that spike sorting improves decoding performance by 10% on average, going up to 50% in some sessions (Quian Quiroga et al, 2007)
Summary
Monitoring the activity of single neurons in vivo is the basis for understanding the brain mechanisms supporting behavior. The number of simultaneously recorded neurons has grown exponentially since the 1950s, doubling every 7 years, and currently allowing electrical observation of hundreds of neurons at sub-millisecond timescales (Stevenson and Kording, 2011) This improvement should be largely attributed to the development of better recording techniques—i.e., the use of tetrodes, namely, 4 recording sites typically 25–50 m apart (Gray et al, 1995), followed by polytrodes, namely, one or more columns with 8–64 channels spaced 50–70 m (Blanche et al, 2005; Buzsáki, 2004; Csicsvari et al, 2003)—and has so far not being matched by analogous improvements in spike sorting algorithms. We outline the lines to be followed in the few years to provide adequate support to the new generation of recording probes
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