Differential excitability of PV and SST neurons results in distinct functional roles in inhibition stabilization of Up-states

Abstract

Up-states are the best-studied example of an emergent neural dynamic regime. Computational models based on a single class of inhibitory neurons indicate that Up-states reflect bistable dynamical systems in which positive feedback is stabilized by strong inhibition and predict a paradoxical effect in which increased drive to inhibitory neurons results in decreased inhibitory activity. To date, however, computational models have not incorporated empirically defined properties of PV and SST neurons. Here we first, experimentally characterized the frequencycurrent (F-I) curves of pyramidal, PV, and SST neurons and confirmed a sharp difference between the threshold and slopes of PV and SST neurons. The empirically defined F-I curves were incorporated into a three-population computational model that simulated the empirically-derived firing rates of pyramidal, PV, and SST neurons. Simulations revealed that the intrinsic properties were sufficient to predict that PV neurons are primarily responsible for generating the nontrivial fixed points representing Up-states. Simulations and analytical methods demonstrated that while the paradoxical effect is not obligatory in a model with two classes of inhibitory neurons, it is present in most regimes. Finally, experimental tests validated predictions of the model that the Pyr↔PV inhibitory loop is stronger than the Pyr↔SST loop.SIGNIFICANCE STATEMENTMany cortical computations, such as working memory, rely on the local recurrent excitatory connections that define cortical circuit motifs. Up-states are among the simplest and best studied examples of neural dynamic regimes that rely on recurrent excitatory excitation. However, this positive feedback must be held in check by inhibition. To address the relative contribution of PV and SST neurons we characterized the intrinsic input-output differences between these classes of inhibitory neurons, and using experimental and theoretical methods show that the higher threshold and gain of PV leads to a dominant role in network stabilization.

Orchestrated Excitatory and Inhibitory Learning Rules Lead to the Unsupervised Emergence of Self-sustained and Inhibition-stabilized Dynamics

“…1A). During Up-states the firing rate of excitatory neurons is relatively low (1-5 Hz) indicating that recurrent excitation is held in check by appropriately tuned inhibition (Neske et al, 2015;Jercog et al, 2017;Romero-Sosa et al, 2020). By recording in organotypic cortical circuits it is possible to show that Up-states develop over the course of ex vivo development (Plenz and Kitai, 1998;Seamans et al, 2003;Johnson and Buonomano, 2007;Kroener et al, 2009;Motanis and Buonomano, 2015;Motanis and Buonomano, 2020).…”

“…For Figure 7 an intrinsic spike frequency adaptation term was included for the excitatory population (Jercog et al, 2017): This function models the F-I curve, or activation function, characteristic of nerve cells. The values for and obey experimentally grounded relations (Jercog et al, 2017;Romero-Sosa et al, 2020). The threshold for the excitatory population is set to = 4.8 while the inhibitory population has a greater threshold = 25.…”

“…The experimental results shown in Figure 1 relied on the reanalysis of previously published data (Motanis and Buonomano, 2015;Romero-Sosa et al, 2020). All animal experiments followed guidelines established by the National Institutes of Health (NIH) and were approved by the Chancellor's Animal Research Committee at the University of California, Los Angeles.…”

“…During Up-states the firing rate of excitatory neurons is relatively low (1-5 Hz) indicating that recurrent excitation is held in check by appropriately tuned inhibition (Neske et al, 2015;Jercog et al, 2017;Romero-Sosa et al, 2021). Computational studies have demonstrated that Up and Down states can be simulated as a bistable dynamical system composed of interconnected populations of excitatory (E) and inhibitory (I) neurons (Fig.…”

“…2A). It is well established that PV + -inhibitory neurons have higher firing rates than pyramidal neurons during Up-states (Neske et al, 2015;Romero-Sosa et al, 2021), thus based on experimental data we set the setpoints for the E and I populations during Upstates to 5 and 14 Hz, respectively (Romero-Sosa et al, 2021). We first asked whether the set of four standard homeostatic learning rules can lead to stable self-sustained dynamic regime (representing a permanent Up-state) in response to a brief external input (low levels of noise were used to avoid spontaneous Up↔Down transitions).…”