Fast spiking interneuron activity in primate striatum tracks learning of attention cues

Abstract

Cognitive flexibility depends on a fast neural learning mechanism for enhancing momentary relevant over irrelevant information. A possible neural mechanism realizing this enhancement uses fast spiking interneurons (FSIs) in the striatum to train striatal projection neurons to gate relevant and suppress distracting cortical inputs. We found support for such a mechanism in nonhuman primates during the flexible adjustment of visual attention in a reversal learning task. FSI activity was modulated by visual attention cues during feature-based learning. One FSI subpopulation showed stronger activation during learning, while another FSI subpopulation showed response suppression after learning, which could indicate a disinhibitory effect on the local circuit. Additionally, FSIs that showed response suppression to learned attention cues were activated by salient distractor events, suggesting they contribute to suppressing bottom-up distraction. These findings suggest that striatal fast spiking interneurons play an important role when cues are learned that redirect attention away from previously relevant to newly relevant visual information. This cue-specific activity was independent of motor-related activity and thus tracked specifically the learning of reward predictive visual features.

Interneuron Specific Gamma Synchronization Indexes Cue Uncertainty and Prediction Errors in Lateral Prefrontal and Anterior Cingulate Cortex

“…We next asked whether the narrow spiking, putative interneurons that encode p (choice) in LPFC and RPE in ACC are from the same electrophysiological cell type, or e-type (Markram et al, 2015). Prior studies have distinguished different narrow spiking e-types using the cells’ spike train pattern and spike waveform duration (Ardid et al, 2015; Dasilva et al, 2019; Trainito et al, 2019; Banaie Boroujeni et al, 2020b). We followed this approach using a cluster analysis to distinguish e-types based on spike waveform duration parameters (inferred hyperpolarization rate and time to 25% repolarization), on whether their spike trains showed regular or variable interspike intervals (local variability ‘ LV ’), or more or less variable firing relative to their mean interspike interval (coefficient of variation ‘ CV ’).…”

“…Prior experimental studies have suggested that interneurons have unique relationships to oscillatory activity (Puig et al, 2008;Cardin et al, 2009;Sohal et al, 2009;Vinck et al, 2013;Womelsdorf et al, 2014a;Chen et al, 2017;Voloh and Womelsdorf, 2018;Shin and Moore, 2019;Banaie Boroujeni et al, 2020b;Onorato et al, 2020), raising the possibility that the N3 e-type neurons realize their functional contributions to p(choice) and RPE processing also through neuronal synchronization. To discern this, we first inspected the spike-triggered LFP averages (STAs) of neurons and found that STAs of many N3 e-type neurons showed oscillatory sidelobes in the 10-30 Hz range (Fig.…”

“…The first implication of our findings is that narrow spiking neurons can be reliably subdivided in three subtypes based on their electrophysiological firing profiles. Distinguishing three narrow spiking neurons in vivo during complex task performance is a significant step forward to complement previous electrophysiological distinctions of three interneuron types based on data invitro (Zaitsev et al, 2009;Torres-Gomez et al, 2020) or in vivo (Ardid et al, 2015;Dasilva et al, 2019;Shin and Moore, 2019;Banaie Boroujeni et al, 2020b), and complementing the finergrained electrophysiological characterization of 'e-types' in-vitro that has been achieved with a rich battery of current injection patterns that are difficult to apply in the awake and behaving primate (Markram et al, 2004;Monyer and Markram, 2004;Medalla et al, 2017;Gouwens et al, 2019). This in-vitro 'e-typing' has distinguished eleven (Markram et al, 2015) or thirteen (Gouwens et al, 2019) distinct interneuron e-types in rodent somatosensory and mouse visual cortex, respectively.…”

Gains and Losses affect Learning Differentially at Low and High Attentional Load

“…In the striatum lesions abolish selectively the ability to use reward incentives to optimize decisions (Schmidt et al, 2008). Intermingled with reward incentive coding neurons in the striatum are neurons of the so-called no-go pathway that learn to activate in response to aversive, undesired stimuli -including distractor events -in order to inhibit action or attention to them (Burke et al, 2017;Arcizet and Krauzlis, 2018;Banaie Boroujeni et al, 2020). This inhibitory striatal pathway receives input from medial prefrontal cortex where localized clusters of neurons mediate avoidance behavior to stimuli with negative outcome associations (Amemori and Graybiel, 2012;Amemori et al, 2020) and represent negative prediction errors that are specific to incorrectly attended visual object features (Oemisch et al, 2019).…”

Activity of fast‐spiking interneurons in the monkey striatum during reaching movements guided by external cues or by a free choice

“…Alternatively, the dependence on the action selection mode may reflect an attentional component of the task. Recently, a study in the monkey reported that striatal FSIs modulate their activity during an oculomotor task according to the amount of attention allocated to stimuli (Banaie Boroujeni et al., 2020). In our experiment, it is conceivable that attention is higher in the external condition in which monkeys were required to allocate attention to detect and report the location of the trigger stimulus presented pseudorandomly in one of two locations and this difference may bring about an increased level of FSI activity.…”