I’m presenting a poster at the 2013 Society for Neuroscience meeting, on Wednesday, November 13, in the afternoon session. Our poster is titled: Hierarchy of intrinsic timescales across primate cortex. In collaboration with electrophysiologists in six labs, this project combines data analysis and computational modeling to explore the neural circuit basis of specialized function across cortical areas. My abstract is below:
Hierarchy of intrinsic timescales across primate cortex
John D. Murray, Alberto Bernacchia, Hyojung Seo, Xinying Cai, David J. Freedman, Jonathan Wallis, Ranulfo Romo, Daeyeol Lee, Camillo Padoa-Schioppa, Tatiana Pasternak, Xiao-Jing Wang
Primate cortex is hierarchically organized, and different cortical areas appear specialized for diverse computations. However, the neural circuit basis underlying these areal specializations remains an open question. One hypothesis is that differences in local circuit properties across areas may be critical for this specialization. We hypothesize that, at the physiological level, these differences can be detected in terms of differential timescales of neural dynamics.
To test this hypothesis, we studied temporal autocorrelation of single-neuron spike trains in multiple areas, across sensory, parietal, and prefrontal cortex, that were recorded in monkeys performing cognitive tasks. We focused on activity during the task’s fixation period to isolate internal dynamics and facilitate comparison across different datasets. We interpret these results using a mathematical model of doubly stochastic Poisson processes, which allows us to segregate spiking variability from firing rate fluctuations.
In a given cortical area, decay of autocorrelation is well described by a characteristic timescale, which reflects intrinsic firing rate fluctuations within single trials (10’s to 100’s of milliseconds). Across areas, timescales follow a hierarchical ordering, with sensory areas exhibiting short timescale and prefrontal areas exhibiting long timescales, spanning an order of magnitude. The hierarchy of intrinsic timescales correlates with hierarchies derived from long-range anatomical projections, linking physiological and anatomical measures.
Neural variability is often measured by Fano factor of spike counts across trials. Fano factor combines contributions from within-trial rate fluctuations and slower across-trial rate fluctuations. Using our mathematical framework, we decomposed Fano factor into the contributions from constituent timescales of rate variability.
To explore potential mechanisms that underlie differential timescales across areas, we studied a spiking neural circuit model. We find that strengthening recurrent structure in the network increases the intrinsic timescale, within the range observed experimentally. We propose that a gradient of intrinsic timescales across cortical areas reflects specialization of local circuit properties for diverse computations.