Cell assemblies at multiple time scales with arbitrary lag constellations
This provides a methodological advance for neuroscientists studying neural coding, though it is incremental in improving statistical tools for assembly detection.
The authors tackled the problem of extracting neural cell assemblies with arbitrary time lags and at multiple temporal scales, which was hindered by a lack of statistical tools and computational burden, and they developed a unifying framework that revealed assembly structure varies significantly by brain area and task demands, with no universal cortical coding scheme.
Hebb's idea of a cell assembly as the fundamental unit of neural information processing has dominated neuroscience like no other theoretical concept within the past 60 years. A range of different physiological phenomena, from precisely synchronized spiking to broadly simultaneous rate increases, has been subsumed under this term. Yet progress in this area is hampered by the lack of statistical tools that would enable to extract assemblies with arbitrary constellations of time lags, and at multiple temporal scales, partly due to the severe computational burden. Here we present such a unifying methodological and conceptual framework which detects assembly structure at many different time scales, levels of precision, and with arbitrary internal organization. Applying this methodology to multiple single unit recordings from various cortical areas, we find that there is no universal cortical coding scheme, but that assembly structure and precision significantly depends on brain area recorded and ongoing task demands.