Cell ReportsNeurosciencePeer-reviewed journal article
Spike frequency adaptation in primate lateral prefrontal cortex neurons results from interplay between intrinsic properties and circuit dynamics
Nils A. KochiD, Benjamin W. CorriganiD, Michael Feyerabend, Roberto A. GulliiD, Michelle Stephanie Jimenez-Sosa, Mohamad AbbassiD, Julia Kathryn SunstrumiD, Sara MatoviciD, Megan P. RoussyiD, Rogelio LunaiD, Samuel MesterniD, Borna MahmoudianiD, Susheel VijayraghavaniD, Hiroyuki IgarashiiD, Kartik S. PradeepaniD, William J. Assis, J. Andrew PruszynskiiD, Shreejoy J. TripathyiD, Jochen F. StaigeriD, Guillermo González‐BurgosiD, Andreas NeefiD, Stefan TreueiD, Stefan EverlingiD, Wataru InoueiD, Anmar KhadraiD, Julio Martínez-TrujilloiD
Abstract
Recordings of cortical neurons isolated from brain slices and dissociated from their networks, display intrinsic spike frequency adaptation (I-SFA) to a constant current input. Interestingly, extracellular recordings in behaving subjects also show extrinsic-SFA (E-SFA) in response to sustained visual stimulation. Because neurons are isolated from brain networks in slice recordings, it is challenging to infer how I-SFA contributes to E-SFA in interconnected brains during behavior. To investigate this, we recorded responses of macaque lateral prefrontal cortex neurons in vivo during a visually guided saccade task and in acute brain slices in vitro. Broad spiking (BS) putative pyramidal cells and narrow spiking (NS) putative inhibitory interneurons exhibited E-SFA in vivo. In acute brain slices, both cell types displayed I-SFA though their magnitudes differed. To investigate how in vitro I-SFA contributes to in vivo E-SFA, we developed a data-driven hybrid circuit model in which local NS neurons are driven by BS input. We observed that model NS cell responses show longer SFA than observed in vivo. Introducing inhibition of NS cells to the model circuit removed this discrepancy. Our results indicate that both I-SFA and inhibitory circuit dynamics contribute to E-SFA in LPFC neurons. They highlight the contribution of single neuron and network dependent computations to neural activity underlying behavior.
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