To adapt behavior to a changing environment, one must monitor outcomes of executed actions and adjust subsequent actions accordingly. Involvement of the medial frontal cortex in performance monitoring has been suggested, but little is known about neural processes that link performance monitoring to performance adjustment. Here, we recorded from neurons in the medial prefrontal cortex of monkeys learning arbitrary action-outcome contingencies. Some cells preferentially responded to positive visual feedback stimuli and others to negative feedback stimuli. The magnitude of responses to positive feedback stimuli decreased over the course of behavioral adaptation, in correlation with decreases in the amount of prediction error of action values. Therefore, these responses in medial prefrontal cells may signal the direction and amount of error in prediction of values of executed actions to specify the adjustment in subsequent action selections.

SUMMARY Knowledge about hypothetical outcomes from unchosen actions is beneficial only when such outcomes can be correctly attributed to specific actions. Here, we show that during a simulated rock-paper-scissors game, rhesus monkeys can adjust their choice behaviors according to both actual and hypothetical outcomes from their chosen and unchosen actions, respectively. In addition, neurons in both dorsolateral prefrontal cortex and orbitofrontal cortex encoded the signals related to actual and hypothetical outcomes immediately after they were revealed to the animal. Moreover, compared to the neurons in the orbitofrontal cortex, those in the dorsolateral prefrontal cortex were more likely to change their activity according to the hypothetical outcomes from specific actions. Conjunctive and parallel coding of multiple actions and their outcomes in the prefrontal cortex might enhance the efficiency of reinforcement learning and also contribute to their context-dependent memory.

Humans demonstrate behavioural advantages (biases) towards particular dimensions (colour or shape of visual objects), but such biases are significantly altered in neuropsychological disorders. Recent studies have shown that lesions in the prefrontal cortex do not abolish dimensional biases, and therefore suggest that such biases might not depend on top-down prefrontal-mediated attention and instead emerge as bottom-up processing advantages. We hypothesised that if dimensional biases merely emerge from an enhancement of object features, the presence of visual objects would be necessary for the manifestation of dimensional biases. In a specifically-designed working memory task, in which macaque monkeys and humans performed matching based on the object memory rather than the actual object, we found significant dimensional biases in both species, which appeared as a shorter response time and higher accuracy in the preferred dimension (colour and shape dimension in humans and monkeys, respectively). Moreover, the mnemonic demands of the task influenced the magnitude of dimensional bias. Our findings in two primate species indicate that the dichotomy of top-down and bottom-up processing does not fully explain the emergence of dimensional biases. Instead, dimensional biases may emerge when processed information regarding visual object features interact with mnemonic and executive functions to guide goal-directed behaviour.

Compression techniques for deep neural network models are becoming very important for the efficient execution of high-performance deep learning systems on edge-computing devices. The concept of model compression is also important for analyzing the generalization error of deep learning, known as the compression-based error bound. However, there is still huge gap between a practically effective compression method and its rigorous background of statistical learning theory. To resolve this issue, we develop a new theoretical framework for model compression and propose a new pruning method called {\it spectral pruning} based on this framework. We define the ``degrees of freedom'' to quantify the intrinsic dimensionality of a model by using the eigenvalue distribution of the covariance matrix across the internal nodes and show that the compression ability is essentially controlled by this quantity. Moreover, we present a sharp generalization error bound of the compressed model and characterize the bias--variance tradeoff induced by the compression procedure. We apply our method to several datasets to justify our theoretical analyses and show the superiority of the the proposed method.

Mirror neurons respond when executing a motor act and when observing others' similar act. So far, mirror neurons have been found only in macaques, humans, and songbirds. To investigate the degree of phylogenetic specialization of mirror neurons during the course of their evolution, we determined whether mirror neurons with similar properties to macaques occur in a New World monkey, the common marmoset (Callithrix jacchus). The ventral premotor cortex (PMv), where mirror neurons have been reported in macaques, is difficult to identify in marmosets, since no sulcal landmarks exist in the frontal cortex. We addressed this problem using “in vivo” connection imaging methods. That is, we first identified cells responsive to others' grasping action in a clear landmark, the superior temporal sulcus (STS), under anesthesia, and injected fluorescent tracers into the region. By fluorescence stereomicroscopy, we identified clusters of labeled cells in the ventrolateral frontal cortex, which were confirmed to be within the ventrolateral frontal cortex including PMv after sacrifice. We next implanted electrodes into the ventrolateral frontal cortex and STS and recorded single/multi-units under an awake condition. As a result, we found neurons in the ventrolateral frontal cortex with characteristic “mirror” properties quite similar to those in macaques. This finding suggests that mirror neurons occur in a common ancestor of New and Old World monkeys and its common properties are preserved during the course of primate evolution.

Natural sound is composed of various frequencies. Although the core region of the primate auditory cortex has functionally defined sound frequency preference maps, how the map is organized in the auditory areas of the belt and parabelt regions is not well known. In this study, we investigated the functional organizations of the core, belt, and parabelt regions encompassed by the lateral sulcus and the superior temporal sulcus in the common marmoset (Callithrix jacchus). Using optical intrinsic signal imaging, we obtained evoked responses to band-pass noise stimuli in a range of sound frequencies (0.5–16 kHz) in anesthetized adult animals and visualized the preferred sound frequency map on the cortical surface. We characterized the functionally defined organization using histologically defined brain areas in the same animals. We found tonotopic representation of a set of sound frequencies (low to high) within the primary (A1), rostral (R), and rostrotemporal (RT) areas of the core region. In the belt region, the tonotopic representation existed only in the mediolateral (ML) area. This representation was symmetric with that found in A1 along the border between areas A1 and ML. The functional structure was not very clear in the anterolateral (AL) area. Low frequencies were mainly preferred in the rostrotemplatal (RTL) area, while high frequencies were preferred in the caudolateral (CL) area. There was a portion of the parabelt region that strongly responded to higher sound frequencies (>5.8 kHz) along the border between the rostral parabelt (RPB) and caudal parabelt (CPB) regions.

We describe our connectomics pipeline for processing tracer injection data for the brain of the common marmoset (Callithrix jacchus). Brain sections were imaged using a batch slide scanner (NanoZoomer 2.0-HT) and we used artificial intelligence to precisely segment the anterograde tracer signal from the background in the fluorescence images. The shape of each brain was reconstructed by reference to a block-face and all data was mapped into a common 3D brain space with atlas and 2D cortical flat map. To overcome the effect of using a single template atlas to specify cortical boundaries, each brain was cytoarchitectonically annotated and used for making an individual 3D atlas. Registration between the individual and common brain cortical boundaries in the flat map space was done to absorb the variation of each brain and precisely map all tracer injection data into one cortical brain space. We describe the methodology of our pipeline and analyze tracer segmentation and brain registration accuracy. Results show our pipeline can successfully process and normalize tracer injection experiments into a common space, making it suitable for large-scale connectomics studies with a focus on the cerebral cortex.

The primate prefrontal cortex (PFC) has greatly expanded to evolve specialized architecture, but its roles in top-down brain control remain enigmatic. Based on connectomics mapping of the marmoset PFC, we characterized two contrasting features of corticocortical and corticostriatal projections. One is the "focalness" of projections, exemplified by multiple columnar axonal terminations in the cortical layers and the other is the "widespreadness" of weaker projections, whose patterns consisted of several common motifs representing the framework of PFC connectivity. We clarified the topographic rules of distribution for these features, which should constrain how PFC neurons can coordinate to control the target regions as populations. These features are observed only primitively in rodents and are considered critical in understanding the roles of the PFC in neuropsychiatric disorders.