Feature Specific Prediction Errors and Surprise across Macaque Fronto-Striatal Circuits during Attention and Learning

Abstract

HighlightsNeural reward prediction errors carry information for updating feature-based attention in all areas of the fronto-striatal network.Feature specific neural prediction errors emerge earliest in anterior cingulate cortex and later in lateral prefrontal cortex.Ventral striatum neurons encode feature specific surprise strongest for the goal-relevant feature.Neurons encoding feature-specific prediction errors contribute to attentional selection after learning.

Interneuron-specific gamma synchronization indexes cue uncertainty and prediction errors in lateral prefrontal and anterior cingulate cortex

“…During reversal performance we recorded the activity of 329 single neurons in LPFC areas 46/9 and anterior area 8 (monkey H/K: 172/157) and 397 single neurons in dorsal ACC area 24 (monkey H/K: 213/184) (Figure 1D, Figure 1-figure supplement 1). The average action potential waveform shape of recorded neurons distinguished neurons with broad and narrow spikes similar to previous studies in LPFC and ACC (Gregoriou et al, 2012;Ardid et al, 2015;Westendorff et al, 2016;Dasilva et al, 2019;Oemisch et al, 2019) (Figure 1E). Prior biophysical modeling has shown that the extracellular action potential waveform shape, including its duration, is directly related to transmembrane currents and the intracellularly measurable action potential shape and duration (Gold et al, 2006;Bean, 2007;Gold et al, 2007;Buzsaki et al, 2012).…”

“…We used a color-based reversal paradigm that required subjects to learn which of two colors were rewarded as described previously (Oemisch et al, 2019). The rewarded color reversed every ~30-40 trials.…”

“…Choice probability, p(choice), was calculated with a reinforcement learning model that learned to optimize choices based on reward prediction errors (see Eq. 3 in Methods and (Oemisch et al, 2019)). Choice probability was low (near ~0.5) early during learning and rose after each reversal to reach a plateau after around ~10 trials (Figure 1C, for example blocks, Figure 2-figure supplement 3A).…”

“…While there are recent advances using optogenetic tools for use in primates (Acker et al, 2016;Dimidschstein et al, 2016;Gong et al, 2020), most existing knowledge about cell specific circuit functions are indirectly inferred from studies that distinguish only one group of putative interneurons that show narrow action potential spike width. Compared to broad spiking neurons the group of narrow spiking, putative interneurons in lateral prefrontal cortex have been found to more likely encode categorical information during working memory delays (Diester and Nieder, 2008), show stronger stimulus onset responses during cognitive control tasks (Johnston et al, 2009), stronger attentional modulation (Thiele et al, 2016), more location-specific encoding of task rules (Johnston et al, 2009), stronger reduction of firing selectivity for task irrelevant stimulus features (Hussar and Pasternak, 2009), stronger encoding of errors and loss (Shen et al, 2015;Sajad et al, 2019), more likely encoding of outcome history (Kawai et al, 2019), and stronger encoding of feature-specific reward prediction errors (Oemisch et al, 2019), amongst other unique firing characteristics Ardid et al, 2015;Rich and Wallis, 2017;Voloh and Womelsdorf, 2018;Torres-Gomez et al, 2020).…”

Time-Encoding Migrates from Prefrontal Cortex to Dorsal Striatum During Learning of a Self-Timed Response Duration Task

“…Finally, the number of converging projections to the STR 32 makes it prone to detect environmental changes from multiple brain sources beyond the mPFC. Hence, the STR may provide a different action selection and, consequently, a different strategy to optimize reward rate 29, 33 .…”