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In addition to the prefrontal cortex (PFC), the basal ganglia (BG) have been increasingly often reported to play a fundamental role in category learning, but the circuit mechanisms mediating their interaction remain to be explored. We developed a novel neurocomputational model of category learning that particularly addresses the BG-PFC interplay. We propose that the BG bias PFC activity by removing the inhibition of cortico-thalamo-cortical loop and thereby provide a teaching signal to guide the acquisition of category representations in the corticocortical associations to the PFC. Our model replicates key behavioral and physiological data of macaque monkey learning a prototype distortion task from Antzoulatos and Miller (2011) Our simulations allowed us to gain a deeper insight into the observed drop of category selectivity in striatal neurons seen in the experimental data and in the model. The simulation results and a new analysis of the experimental data based on the model's predictions show that the drop in category selectivity of the striatum emerges as the variability of responses in the striatum rises when confronting the BG with an increasingly larger number of stimuli to be classified. The neurocomputational model therefore provides new testable insights of systems-level brain circuits involved in category learning that may also be generalized to better understand other cortico-BG-cortical loops. SIGNIFICANCE STATEMENT Inspired by the idea that basal ganglia (BG) teach the prefrontal cortex (PFC) to acquire category representations, we developed a novel neurocomputational model and tested it on a task that was recently applied in monkey experiments. As an advantage over previous models of category learning, our model allows to compare simulation data with single-cell recordings in PFC and BG. We not only derived model predictions, but already verified a prediction to explain the observed drop in striatal category selectivity. When testing our model with a simple, real-world face categorization task, we observed that the fast striatal learning with a performance of 85% correct responses can teach the slower PFC learning to push the model performance up to almost 100%., (Copyright © 2018 the authors 0270-6474/18/389551-13$15.00/0.)

A meta-analysis of non-human primates performing three different tasks (Object-Match, Category-Match, and Category-Saccade associations) revealed signatures of explicit and implicit learning. Performance improved equally following correct and error trials in the Match (explicit) tasks, but it improved more after correct trials in the Saccade (implicit) task, a signature of explicit versus implicit learning. Likewise, error-related negativity, a marker for error processing, was greater in the Match (explicit) tasks. All tasks showed an increase in alpha/beta (10-30 Hz) synchrony after correct choices. However, only the implicit task showed an increase in theta (3-7 Hz) synchrony after correct choices that decreased with learning. In contrast, in the explicit tasks, alpha/beta synchrony increased with learning and decreased thereafter. Our results suggest that explicit versus implicit learning engages different neural mechanisms that rely on different patterns of oscillatory synchrony., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Functional connectivity between the prefrontal cortex (PFC) and striatum (STR) is thought critical for cognition and has been linked to conditions like autism and schizophrenia. We recorded from multiple electrodes in PFC and STR while monkeys acquired new categories. Category learning was accompanied by an increase in beta band synchronization of LFPs between, but not within, the PFC and STR. After learning, different pairs of PFC-STR electrodes showed stronger synchrony for one or the other category, suggesting category-specific functional circuits. This category-specific synchrony was also seen between PFC spikes and STR LFPs, but not the reverse, reflecting the direct monosynaptic connections from the PFC to STR. However, causal connectivity analyses suggested that the polysynaptic connections from STR to the PFC exerted a stronger overall influence. This supports models positing that the basal ganglia "train" the PFC. Category learning may depend on the formation of functional circuits between the PFC and STR., (Copyright © 2014 Elsevier Inc. All rights reserved.)

The hippocampus can be divided into distinct segments that make unique contributions to learning and memory. The dorsal segment supports cognitive processes like spatial learning and navigation while the ventral hippocampus regulates emotional behaviors related to fear, anxiety and reward. In the current study, we determined how pyramidal cells in ventral CA1 respond to spatial cues and aversive stimulation during a context fear conditioning task. We also examined the effects of high and low frequency stimulation of these neurons on defensive behavior. Similar to previous work in the dorsal hippocampus, we found that cells in ventral CA1 expressed high-levels of c-Fos in response to a novel spatial environment. Surprisingly, however, the number of activated neurons did not increase when the environment was paired with footshock. This was true even in the subpopulation of ventral CA1 pyramidal cells that send direct projections to the amygdala. When these cells were stimulated at high-frequencies (20 Hz) we observed feedforward inhibition of basal amygdala neurons and impaired expression of context fear. In contrast, low-frequency stimulation (4 Hz) did not inhibit principal cells in the basal amygdala and produced an increase in fear generalization. Similar results have been reported in dorsal CA1. Therefore, despite clear differences between the dorsal and ventral hippocampus, CA1 neurons in each segment appear to make similar contributions to context fear conditioning.

We discovered a new type of dendritic spine. It is found on space-specific neurons in the barn owl inferior colliculus, a site of experience-dependent plasticity. Connectomic analysis revealed dendritic protrusions of unusual morphology including topological holes, hence termed "toric" spines (n = 76). More significantly, presynaptic terminals converging onto individual toric spines displayed numerous active zones (up to 49) derived from multiple axons (up to 11) with incoming trajectories distributed widely throughout 3D space. This arrangement is suited to integrate input sources. Dense reconstruction of two toric spines revealed that they were unconnected with the majority (∼84%) of intertwined axons, implying a high capacity for information storage. We developed an ex vivo slice preparation and provide the first published data on space-specific neuron intrinsic properties, including cellular subtypes with and without toric-like spines. We propose that toric spines are a cellular locus of sensory integration and behavioral learning.

Summary: Learning to classify diverse experiences into meaningful groups, like categories, is fundamental to normal cognition. To understand its neural basis, we simultaneously recorded from multiple electrodes in lateral prefrontal cortex and dorsal striatum, two interconnected brain structures critical for learning. Each day, monkeys learned to associate novel abstract, dot-based categories with a right versus left saccade. Early on, when they could acquire specific stimulus-response associations, striatum activity was an earlier predictor of the corresponding saccade. However, as the number of exemplars increased and monkeys had to learn to classify them, PFC activity began to predict the saccade associated with each category before the striatum. While monkeys were categorizing novel exemplars at a high rate, PFC activity was a strong predictor of their corresponding saccade early in the trial before the striatal neurons. These results suggest that striatum plays a greater role in stimulus-response association and PFC in abstraction of categories. Video Abstract: Display Omitted [Copyright &y& Elsevier]