P141 Intrinsic neuronal properties shape local circuit inhibition in primate prefrontal cortex
Nils A. Koch*1, Benjamin W. Corrigan2,3,4, Julio C. Martinez-Trujillo3,4,5, Anmar Khadra1,6
1Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
2Department of Biology, York University, Toronto, ON, Canada
3Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON, Canada
4Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
5Western Institute for Neuroscience, Western University, London, ON, Canada
6Department of Physiology, McGill University, Montreal, QC, Canada
*Email: nils.koch@mail.mcgill.ca
Introduction
Intrinsic neuronal properties play a key role in neuronal circuit dynamics. One such property evident during step-current stimulation is intrinsic spike frequency adaptation (I-SFA), a feature noted to be important for in vivo activity [1] and computational capabilities of neurons [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]. In behaving animals, extracellular recordings exhibit extrinsic spike frequency adaptation (E-SFA) in response to sustained visual stimulation. However, the relationship between the I-SFA measured in vitro, typically in response to constant step-current pulses, and the E-SFA described in vivo during behavioral tasks, in which the inputs into a neuron are likely variable and difficult to measure, is not well characterized.
Methods
To investigate how I-SFA in neurons isolated from brain networks contributes to E-SFA during behavior, we recorded responses of macaque lateral prefrontal cortex neurons in vivo during a visually guided saccade task and in acute brain slices in vitro. Units recorded in vivo and neurons recorded in vitro were classified as broad spiking (BS) putative pyramidal cells and narrow spiking (NS) putative inhibitory interneurons based on spike width. To elucidate how in vitro I-SFA contributes to in vivo E-SFA, we bridge the gap between the in vitro and in vitro recordings with a data-driven hybrid circuit model in which NS neurons fit to the in vitro firing behavior are driven by local BS input.
Results
Both BS and NS units exhibited E-SFA in vivo. In acute brain slices, both cell types displayed differing magnitudes of I-SFA but with timescales similar to E-SFA. The model NS cell responses show longer SFA than observed in vivo. However, introduction of inhibition of NS cells to the model circuit removed this discrepancy and reproduced the in vivo E-SFA, suggesting a vital role of local circuitry in dictating task-related in vivo activity. By exploring the relationship between individual neuron I-SFA and hybrid circuit model E-SFA, the contribution of I-SFA to E-SFA is uncovered. Specifically, this contribution is dependent on the timescale of I-SFA and modulates in vivo response magnitudes as well as E-SFA timescales.
Discussion
Our results indicate that both I-SFA and inhibitory circuit dynamics contribute to E-SFA in LPFC neurons during a visual task and highlight the contribution of both single neurons and network dependent computations to neural activity underlying behavior. Furthermore, the interaction between excitatory input and I-SFA demonstrates that inhibitory cortical neurons do not solely contribute to the local circuit inhibition by altering the sign of signals (i.e. from excitation to inhibition) and that the intrinsic properties of NS neurons contribute to their activity in vivo. Consequently, large models of cortical networks as well as artificial neuronal nets that emphasize network connectivity may benefit from including intrinsic neuronal properties.
Acknowledgements
This work was supported by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant to A.K.; Canadian Institutes of Health Research (CIHR), NSERC, Neuronex (ref. FL6GV84CKN57) and BrainsCAN grants to J.C.M.-T.; and an NSERC Postgraduate Scholarship-Doctoral Fellowship to N.A.K..
References
1. https://doi.org/10.3934/mbe.2016002
2. https://doi.org/10.1016/j.cub.2020.11.054
3. https://doi.org/10.1007/s10827-007-0044-8
4. https://doi.org/10.1523/JNEUROSCI.4795-04.2005
5. https://doi.org/10.1038/s41467-017-02453-9
6. https://doi.org/10.1523/ENEURO.0305-18.2020
7. https://doi.org/10.1016/j.conb.2013.11.012
8. https://doi.org/10.1016/j.biosystems.2022.104802
9. https://doi.org/10.1007/s00422-009-0304-y
10. https://doi.org/10.1523/JNEUROSCI.1792-08.2008
11. https://doi.org/10.1016/j.neuron.2016.09.046