FosGFP expression does not capture a sensory learning-related engram in superficial layers of mouse barrel cortex

Immediate-early gene (IEG) expression has been used to identify small neural ensembles linked to a particular experience, based on the principle that a selective subset of activated neurons will encode specific memories or behavioral responses. The majority of these studies have focused on “engrams” in higher-order brain areas where more abstract or convergent sensory information is represented, such as the hippocampus, prefrontal cortex, or amygdala. In primary sensory cortex, IEG expression can label neurons that are responsive to specific sensory stimuli, but experience-dependent shaping of neural ensembles marked by IEG expression has not been demonstrated. Here, we use a fosGFP transgenic mouse to longitudinally monitor in vivo expression of the activity-dependent gene c-fos in superficial layers (L2/3) of primary somatosensory cortex (S1) during a whisker-dependent learning task. We find that sensory association training does not detectably alter fosGFP expression in L2/3 neurons. Although training broadly enhances thalamocortical synaptic strength in pyramidal neurons, we find that synapses onto fosGFP+ neurons are not selectively increased by training; rather, synaptic strengthening is concentrated in fosGFP− neurons. Taken together, these data indicate that expression of the IEG reporter fosGFP does not facilitate identification of a learning-specific engram in L2/3 in barrel cortex during whisker-dependent sensory association learning.

A multivariate view of cognitive differences reveals domain-general correlation structure in the Trinidadian Guppy (Poecilia reticulata)

Cognitive variation is common among-individuals within populations, and this variation can be consistent across time and context. From an evolutionary perspective, among-individual variation is important and required for natural selection. Selection has been hypothesised to favour high cognitive performance, however directional selection would be expected to erode variation over time. Additionally, while variation is a prerequisite for natural selection, it is also true that selection does not act on traits in isolation. Thus, the extent to which performance covaries among specific cognitive domains, and other aspects of phenotype (e.g. personality traits) is expected to be an important factor in shaping evolutionary dynamics. Fitness trade-offs could shape patterns of variation in performance across different cognitive domains, however positive correlations between cognitive domains and personality traits are also known to occur. Here we aimed to test this idea using a multivariate approach to characterise and test hypothesised relationships of cognitive performance across multiple domains and personality, in the Trinidadian guppy (Poecilia reticulata). We estimate the among-individual correlation matrix (ID) in performance across three cognitive domains; association learning in a colour discrimination task; motor cognition in a novel motor task and cognitive flexibility in a reversal learning task, and the personality trait boldness, measured as time to emerge. We found no support for trade-offs occurring, but the presence of strong positive domain-general correlations in ID, where 57% of the variation is explained by the leading eigen vector. While highlighting caveats of how non-cognitive factors and assay composition may affect the structure of the ID-matrix, we suggest that our findings are consistent with a domain-general axis of cognitive variation in this population, adding to the growing body of support for domain-general variation among-individuals in animal cognitive ability.

Where is the money? Dynamics in feedback processing and attention during spatial probabilistic learning

Learned feature-based stimulus-reward-associations can modulate behavior and the underlying neural processing of information. In our study, we investigated the neurocognitive mechanisms underlying learning of spatial stimulus-reward-associations. Participants performed a probabilistic spatial reward-learning task that required participants, within 40 trials, to learn which out of four locations on a computer screen yielded the most gain-feedback when chosen. Our behavioral findings show that participants learned to choose which location was most rewarding. Those findings were paralleled by significant amplitude differences in event-related potentials (ERPs) elicited by the presentation of loss and gain feedback; the amplitude of the feedback-related negativity (FRN) was more negative in response to loss feedback compared to gain feedback, but showed no modulation by trial-number. On the other hand, the late positive component (LPC), became larger in response to losses as the learning-set progressed, but smaller in response to gains. Additionally, immediately following feedback presentation, brain activity in the visual cortex - read out through alpha frequency oscillations measured over occipital sites - was predictive of the amplitude of the N2pc ERP component, a marker of spatial attention orienting, observed on the next trial. Taken together, we elucidated neurocognitive dynamics underlying feedback processing in spatial reward learning, and the subsequent effects that spatial stimulus-reward association learning have on spatial attention.

Semisupervised Association Learning Based on Partial Differential Equations for Sparse Representation of Image Class Attributes

Semisupervised learning is an idea that addresses how to use a large number of unlabeled samples and a limited number of labeled samples to learn decision knowledge together. In this paper, we propose a multitask multiview semisupervised learning model based on partial differential equation random field and Hilbert independent standard probability image genus attribute model, i.e., shared semantics. In the framework of the image-like genus attribute model, data from different data sources are generated by their shared hidden space representation. Different from the traditional model, this paper uses the Hilbert independence criterion to inscribe the shared relationship of hidden expressions. Meanwhile, to exploit the correlations between labels in the label space as well, this paper uses the partial differential equation random field to inscribe the correlations between different kinds of labels in the label space and the correlations between hidden features and labels. Using the variational expectation-maximization algorithm, the whole generative process model can be inferred. To verify the effectiveness of the model, two artificial datasets and three real datasets are tested in this paper, and the experimental results verify the effectiveness of the algorithm in the paper. On the one hand, it not only improves the classification accuracy of the multiclassification problem and the multilabel problem; it also outputs the association structure between different kinds of labels and between hidden features and labels.

Methods of Mining User-Generated Online Substance Use Data: A Scoping Review (Preprint)

BACKGROUND Social media and online discussion forums offer a unique data source for medical and public health research. Using these platforms, people who use drugs often discuss valuable information including adverse effects, formulations, and reasons for use. OBJECTIVE Since this data is often unstructured, text and data mining methods are required to extract and analyze these posts systematically. This scoping review summarizes the literature on text and data mining methods for online substance use content. METHODS Online databases including PubMed and EMBASE were searched to identify articles meeting the eligibility criteria. Titles and abstracts were first screened by two reviewers and any conflicts were resolved with discussion. Data extraction was performed by two reviewers using an identical template to record information. Any disagreements were resolved with discussion. RESULTS The search identified 1131 articles, 26 of which were included for data extraction. Most articles presented unique data mining methods. The five most common strategies included sentiment analysis, topic modeling, data classification, clustering, and association learning. CONCLUSIONS Data mining offers a valuable avenue for retrieving useful information from online discussion forums to supplement conventional data sources in medical and public health research. With respect to substance use content, association learning and regression analysis were particularly well-suited for analyzing this data. Future research should focus on confirming the validity and reliability of these data mining methods, while establishing links between data mining, clinical evaluation, and knowledge translation.

Entorhinal cortical Island cells regulate temporal association learning with long trace period

Temporal association learning (TAL) allows for the linkage of distinct, nonsynchronous events across a period of time. This function is driven by neural interactions in the entorhinal cortical–hippocampal network, especially the neural input from the pyramidal cells in layer III of medial entorhinal cortex (MECIII) to hippocampal CA1 is crucial for TAL. Successful TAL depends on the strength of event stimuli and the duration of the temporal gap between events. Whereas it has been demonstrated that the neural input from pyramidal cells in layer II of MEC, referred to as Island cells, to inhibitory neurons in dorsal hippocampal CA1 controls TAL when the strength of event stimuli is weak, it remains unknown whether Island cells regulate TAL with long trace periods as well. To understand the role of Island cells in regulating the duration of the learnable trace period in TAL, we used Pavlovian trace fear conditioning (TFC) with a 60-sec long trace period (long trace fear conditioning [L-TFC]) coupled with optogenetic and chemogenetic neural activity manipulations as well as cell type-specific neural ablation. We found that ablation of Island cells in MECII partially increases L-TFC performance. Chemogenetic manipulation of Island cells causes differential effectiveness in Island cell activity and leads to a circuit imbalance that disrupts L-TFC. However, optogenetic terminal inhibition of Island cell input to dorsal hippocampal CA1 during the temporal association period allows for long trace intervals to be learned in TFC. These results demonstrate that Island cells have a critical role in regulating the duration of time bridgeable between associated events in TAL.

Mixed selectivity in the cerebellar Purkinje-cell response during visuomotor association learning

Although the cerebellum has been traditionally considered to be exclusively involved in motor control, recent anatomical and clinical studies show that it also has a role in reward processing. However, the way in which the movement related and the reward related neural activity interact at the level of the cerebellar cortex and contribute towards learning is still unclear. Here, we studied the simple spike activity of Purkinje cells in the mid-lateral cerebellum when monkeys learned to associate a right or left hand movement with one of two visual symbolic cues. These cells had distinctly different discharge patterns between an overtrained symbol-hand association and a novel symbol hand association, responding in association with the movement of both hands, although the kinematics of the movement did not change between the two conditions. The activity change was not related to the pattern of the visual symbols, the movement kinematics, the monkeys' reaction times or the novelty of the visual symbols. The simple spike activity changed with throughout the learning process, but the concurrent complex spikes did not instruct that change. Although these neurons also have reward related activity, the reward-related and movement related signals were independent. We suggest that this mixed selectivity may facilitate the flexible learning of difficult reinforcement learning problems.

A Regression Analysis Based Model for Defect Learning and Prediction in Software Development

The development of software undergoes multiple regression phases to deliver quality software. Therefore, to minimize the development effort, time and cost it is very important to understand the probable defects associated with the designed modules. It is possible that occurrence of a range of defects may impact the designed modules which need to be predicted in advance to have a close inter-association with the depended modules. Most of the existing defect prediction classifier mechanisms are derived from the past project data learning, but it is not sufficient for new project defect predicting as the new design may have a different kind of parameters and constraints. This paper recommends Regression Analysis (RA) based defect learning and prediction Defect Prediction (RA-DP) mechanism to support the defective or non-defective prediction for quality software development. The RA-DP approach provides two methods to perform this prediction analysis. It initially presents an association learning through RA to construct the regression rules from the learned knowledge required for the defect prediction. The constructed regression rules are used for defect prediction and analysis. To measure the performance of the RA-DP a regression experimental evaluation is performed over the defect-prone PROMISE dataset from NASA project. The outcome of the results is analyzed through measuring the prediction Accuracy, Sensitivity and Specificity to demonstrate the improvisation and effectiveness of the proposal in comparison to a few existing classifiers.