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Science (New York, N.Y.) Dec 2022Learning to predict rewards based on environmental cues is essential for survival. It is believed that animals learn to predict rewards by updating predictions whenever...
Learning to predict rewards based on environmental cues is essential for survival. It is believed that animals learn to predict rewards by updating predictions whenever the outcome deviates from expectations, and that such reward prediction errors (RPEs) are signaled by the mesolimbic dopamine system-a key controller of learning. However, instead of learning prospective predictions from RPEs, animals can infer predictions by learning the retrospective cause of rewards. Hence, whether mesolimbic dopamine instead conveys a causal associative signal that sometimes resembles RPE remains unknown. We developed an algorithm for retrospective causal learning and found that mesolimbic dopamine release conveys causal associations but not RPE, thereby challenging the dominant theory of reward learning. Our results reshape the conceptual and biological framework for associative learning.
Topics: Animals; Dopamine; Reward; Limbic System; Association Learning; Cues; Mice
PubMed: 36480599
DOI: 10.1126/science.abq6740 -
Journal of the American Medical... Mar 2020This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to... (Review)
Review
OBJECTIVE
This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research.
MATERIALS AND METHODS
We searched MEDLINE, EMBASE, Scopus, the Association for Computing Machinery Digital Library, and the Association for Computational Linguistics Anthology for articles using DL-based approaches to NLP problems in electronic health records. After screening 1,737 articles, we collected data on 25 variables across 212 papers.
RESULTS
DL in clinical NLP publications more than doubled each year, through 2018. Recurrent neural networks (60.8%) and word2vec embeddings (74.1%) were the most popular methods; the information extraction tasks of text classification, named entity recognition, and relation extraction were dominant (89.2%). However, there was a "long tail" of other methods and specific tasks. Most contributions were methodological variants or applications, but 20.8% were new methods of some kind. The earliest adopters were in the NLP community, but the medical informatics community was the most prolific.
DISCUSSION
Our analysis shows growing acceptance of deep learning as a baseline for NLP research, and of DL-based NLP in the medical community. A number of common associations were substantiated (eg, the preference of recurrent neural networks for sequence-labeling named entity recognition), while others were surprisingly nuanced (eg, the scarcity of French language clinical NLP with deep learning).
CONCLUSION
Deep learning has not yet fully penetrated clinical NLP and is growing rapidly. This review highlighted both the popular and unique trends in this active field.
Topics: Bibliometrics; Deep Learning; Electronic Health Records; Humans; Natural Language Processing
PubMed: 31794016
DOI: 10.1093/jamia/ocz200 -
Science (New York, N.Y.) Oct 2023Episodic memory involves learning and recalling associations between items and their spatiotemporal context. Those memories can be further used to generate internal...
Episodic memory involves learning and recalling associations between items and their spatiotemporal context. Those memories can be further used to generate internal models of the world that enable predictions to be made. The mechanisms that support these associative and predictive aspects of memory are not yet understood. In this study, we used an optogenetic manipulation to perturb the sequential structure, but not global network dynamics, of place cells as rats traversed specific spatial trajectories. This perturbation abolished replay of those trajectories and the development of predictive representations, leading to impaired learning of new optimal trajectories during memory-guided navigation. However, place cell assembly reactivation and reward-context associative learning were unaffected. Our results show a mechanistic dissociation between two complementary hippocampal codes: an associative code (through coactivity) and a predictive code (through sequences).
Topics: Animals; Rats; Conditioning, Classical; Hippocampus; Memory, Episodic; Mental Recall; Optogenetics; Theta Rhythm; Male; Rats, Long-Evans; Association Learning
PubMed: 37856604
DOI: 10.1126/science.adi8237 -
Nature Neuroscience Jan 2020Theories stipulate that memories are encoded within networks of cortical projection neurons. Conversely, GABAergic interneurons are thought to function primarily to...
Theories stipulate that memories are encoded within networks of cortical projection neurons. Conversely, GABAergic interneurons are thought to function primarily to inhibit projection neurons and thereby impose network gain control, an important but purely modulatory role. Here we show in male mice that associative fear learning potentiates synaptic transmission and cue-specific activity of medial prefrontal cortex somatostatin (SST) interneurons and that activation of these cells controls both memory encoding and expression. Furthermore, the synaptic organization of SST and parvalbumin interneurons provides a potential circuit basis for SST interneuron-evoked disinhibition of medial prefrontal cortex output neurons and recruitment of remote brain regions associated with defensive behavior. These data suggest that, rather than constrain mnemonic processing, potentiation of SST interneuron activity represents an important causal mechanism for conditioned fear.
Topics: Animals; Association Learning; Fear; Interneurons; Male; Memory; Mice; Mice, Inbred C57BL; Prefrontal Cortex; Somatostatin; Synaptic Transmission
PubMed: 31844314
DOI: 10.1038/s41593-019-0552-7 -
Journal of Translational Medicine Sep 2023Intracranial aneurysms (IAs) pose a significant and intricate challenge. Elucidating the interplay between DNA methylation and IA pathogenesis is paramount to identify...
DNA methylation regulator-mediated modification patterns and risk of intracranial aneurysm: a multi-omics and epigenome-wide association study integrating machine learning, Mendelian randomization, eQTL and mQTL data.
BACKGROUND
Intracranial aneurysms (IAs) pose a significant and intricate challenge. Elucidating the interplay between DNA methylation and IA pathogenesis is paramount to identify potential biomarkers and therapeutic interventions.
METHODS
We employed a comprehensive bioinformatics investigation of DNA methylation in IA, utilizing a transcriptomics-based methodology that encompassed 100 machine learning algorithms, genome-wide association studies (GWAS), Mendelian randomization (MR), and summary-data-based Mendelian randomization (SMR). Our sophisticated analytical strategy allowed for a systematic assessment of differentially methylated genes and their implications on the onset, progression, and rupture of IA.
RESULTS
We identified DNA methylation-related genes (MRGs) and associated molecular pathways, and the MR and SMR analyses provided evidence for potential causal links between the observed DNA methylation events and IA predisposition.
CONCLUSION
These insights not only augment our understanding of the molecular underpinnings of IA but also underscore potential novel biomarkers and therapeutic avenues. Although our study faces inherent limitations and hurdles, it represents a groundbreaking initiative in deciphering the intricate relationship between genetic, epigenetic, and environmental factors implicated in IA pathogenesis.
Topics: Humans; Multiomics; Intracranial Aneurysm; DNA Methylation; Epigenome; Genome-Wide Association Study; Mendelian Randomization Analysis; Machine Learning
PubMed: 37742034
DOI: 10.1186/s12967-023-04512-w -
Journal of Psychopharmacology (Oxford,... Apr 2021This paper introduces a new construct, the 'pivotal mental state', which is defined as a hyper-plastic state aiding rapid and deep learning that can mediate... (Review)
Review
This paper introduces a new construct, the 'pivotal mental state', which is defined as a hyper-plastic state aiding rapid and deep learning that can mediate psychological transformation. We believe this new construct bears relevance to a broad range of psychological and psychiatric phenomena. We argue that pivotal mental states serve an important evolutionary function, that is, to aid psychological transformation when actual or perceived environmental pressures demand this. We cite evidence that chronic stress and neurotic traits are primers for a pivotal mental state, whereas acute stress can be a trigger. Inspired by research with serotonin 2A receptor agonist psychedelics, we highlight how activity at this particular receptor can robustly and reliably induce pivotal mental states, but we argue that the capacity for pivotal mental states is an inherent property of the human brain itself. Moreover, we hypothesize that serotonergic psychedelics hijack a system that has evolved to mediate rapid and deep learning when its need is sensed. We cite a breadth of evidences linking stress via a variety of inducers, with an upregulated serotonin 2A receptor system (e.g. upregulated availability of and/or binding to the receptor) and acute stress with 5-HT release, which we argue can activate this primed system to induce a pivotal mental state. The pivotal mental state model is multi-level, linking a specific molecular gateway (increased serotonin 2A receptor signaling) with the inception of a hyper-plastic brain and mind state, enhanced rate of associative learning and the potential mediation of a psychological transformation.
Topics: Aspirations, Psychological; Association Learning; Hallucinogens; Humans; Mindfulness; Mysticism; Neuronal Plasticity; Psychotic Disorders; Receptor, Serotonin, 5-HT2A; Serotonin 5-HT2 Receptor Agonists; Signal Transduction; Stress, Physiological; Stress, Psychological
PubMed: 33174492
DOI: 10.1177/0269881120959637 -
Neurobiology of Learning and Memory Jan 2022Although we can learn new information while asleep, we usually cannot consciously remember the sleep-formed memories - presumably because learning occurred in an...
Although we can learn new information while asleep, we usually cannot consciously remember the sleep-formed memories - presumably because learning occurred in an unconscious state. Here, we ask whether sleep-learning expedites the subsequent awake-learning of the same information. To answer this question, we reanalyzed data (Züst et al., 2019, Curr Biol) from napping participants, who learned new semantic associations between pseudowords and translation-words (guga-ship) while in slow-wave sleep. They retrieved sleep-formed associations unconsciously on an implicit memory test following awakening. Then, participants took five runs of paired-associative learning to probe carry-over effects of sleep-learning on awake-learning. Surprisingly, sleep-learning diminished awake-learning when participants learned semantic associations that were congruent to sleep-learned associations (guga-boat). Yet, learning associations that conflicted with sleep-learned associations (guga-coin) was unimpaired relative to learning new associations (resun-table; baseline). We speculate that the impeded wake-learning originated in a deficient synaptic downscaling and resulting synaptic saturation in neurons that were activated during both sleep-learning and awake-learning.
Topics: Adult; Association Learning; Female; Humans; Learning; Male; Mental Recall; Sleep; Vocabulary; Wakefulness; Young Adult
PubMed: 34863922
DOI: 10.1016/j.nlm.2021.107569 -
Current Biology : CB Jun 2022Numerous studies have proposed that our adaptive motor behaviors depend on learning a map between sensory information and limb movement, called an "internal model." From...
Numerous studies have proposed that our adaptive motor behaviors depend on learning a map between sensory information and limb movement, called an "internal model." From this perspective, how the brain represents internal models is a critical issue in motor learning, especially regarding their association with spatial frames processed in motor planning. Extensive experimental evidence suggests that during planning stages for visually guided hand reaching, the brain transforms visual target representations in gaze-centered coordinates to motor commands in limb coordinates, via hand-target vectors in workspace coordinates. While numerous studies have intensively investigated whether the learning for reaching occurs in workspace or limb coordinates, the association of the learning with gaze coordinates still remains untested. Given the critical role of gaze-related spatial coding in reaching planning, the potential role of gaze states for learning is worth examining. Here, we show that motor memories for reaching are separately learned according to target location in gaze coordinates. Specifically, two opposing visuomotor rotations, which normally interfere with each other, can be simultaneously learned when each is associated with reaching to a foveal target and peripheral one. We also show that this gaze-dependent learning occurs in force-field adaptation. Furthermore, generalization of gaze-coupled reach adaptation is limited across central, right, and left visual fields. These results suggest that gaze states are available in the formation and recall of multiple internal models for reaching. Our findings provide novel evidence that a gaze-dependent spatial representation can provide a spatial coordinate framework for context-dependent motor learning.
Topics: Generalization, Psychological; Hand; Learning; Movement; Psychomotor Performance
PubMed: 35580606
DOI: 10.1016/j.cub.2022.04.065 -
PLoS Computational Biology Sep 2022In the natural world, stimulus-outcome associations are often ambiguous, and most associations are highly complex and situation-dependent. Learning to disambiguate these...
In the natural world, stimulus-outcome associations are often ambiguous, and most associations are highly complex and situation-dependent. Learning to disambiguate these complex associations to identify which specific outcomes will occur in which situations is critical for survival. Pavlovian occasion setters are stimuli that determine whether other stimuli will result in a specific outcome. Occasion setting is a well-established phenomenon, but very little investigation has been conducted on how occasion setters are disambiguated when they themselves are ambiguous (i.e., when they do not consistently signal whether another stimulus will be reinforced). In two preregistered studies, we investigated the role of higher-order Pavlovian occasion setting in humans. We developed and tested the first computational model predicting direct associative learning, traditional occasion setting (i.e., 1st-order occasion setting), and 2nd-order occasion setting. This model operationalizes stimulus ambiguity as a mechanism to engage in higher-order Pavlovian learning. Both behavioral and computational modeling results suggest that 2nd-order occasion setting was learned, as evidenced by lack and presence of transfer of occasion setting properties when expected and the superior fit of our 2nd-order occasion setting model compared to the 1st-order occasion setting or direct associations models. These results provide a controlled investigation into highly complex associative learning and may ultimately lead to improvements in the treatment of Pavlovian-based mental health disorders (e.g., anxiety disorders, substance use).
Topics: Association Learning; Conditioning, Classical; Cues; Discrimination Learning; Humans; Learning
PubMed: 36084131
DOI: 10.1371/journal.pcbi.1010410 -
International Journal of Educational... Sep 2023COVID-19 school closure has disrupted education systems globally raising concerns over learning time loss. At the same time, social isolation at home has seen a decline...
COVID-19 school closure has disrupted education systems globally raising concerns over learning time loss. At the same time, social isolation at home has seen a decline in happiness level among young learners. Understanding the link between cognitive effort and emotional wellbeing is important for post-pandemic learning recovery interventions particularly if there is a feedback loop from happiness to learning. In this context, we use primary survey data collected during the first school closure in urban Malaysia to study the complex association between learning loss and student happiness. Machine learning methods are used to accommodate the multi-dimensional and interaction effects between the covariates that influence this association. Empirically, we find that the most important covariates are student gender, social economic status (SES) proxied by the number of books ownership, time spent on play and religious activity. Based on the results, we develop a conceptual framework of learning continuity by formalizing the importance of investment in emotional wellbeing.
PubMed: 37347031
DOI: 10.1016/j.ijedudev.2023.102822