242,681 research outputs found
Remembering Forward: Neural Correlates of Memory and Prediction in Human Motor Adaptation
We used functional MR imaging (FMRI), a robotic manipulandum and systems identification techniques to examine neural correlates of predictive compensation for spring-like loads during goal-directed wrist movements in neurologically-intact humans. Although load changed unpredictably from one trial to the next, subjects nevertheless used sensorimotor memories from recent movements to predict and compensate upcoming loads. Prediction enabled subjects to adapt performance so that the task was accomplished with minimum effort. Population analyses of functional images revealed a distributed, bilateral network of cortical and subcortical activity supporting predictive load compensation during visual target capture. Cortical regions – including prefrontal, parietal and hippocampal cortices – exhibited trial-by-trial fluctuations in BOLD signal consistent with the storage and recall of sensorimotor memories or “states” important for spatial working memory. Bilateral activations in associative regions of the striatum demonstrated temporal correlation with the magnitude of kinematic performance error (a signal that could drive reward-optimizing reinforcement learning and the prospective scaling of previously learned motor programs). BOLD signal correlations with load prediction were observed in the cerebellar cortex and red nuclei (consistent with the idea that these structures generate adaptive fusimotor signals facilitating cancelation of expected proprioceptive feedback, as required for conditional feedback adjustments to ongoing motor commands and feedback error learning). Analysis of single subject images revealed that predictive activity was at least as likely to be observed in more than one of these neural systems as in just one. We conclude therefore that motor adaptation is mediated by predictive compensations supported by multiple, distributed, cortical and subcortical structures
A Recommendation System for Meta-modeling: A Meta-learning Based Approach
Various meta-modeling techniques have been developed to replace computationally expensive simulation models. The performance of these meta-modeling techniques on different models is varied which makes existing model selection/recommendation approaches (e.g., trial-and-error, ensemble) problematic. To address these research gaps, we propose a general meta-modeling recommendation system using meta-learning which can automate the meta-modeling recommendation process by intelligently adapting the learning bias to problem characterizations. The proposed intelligent recommendation system includes four modules: (1) problem module, (2) meta-feature module which includes a comprehensive set of meta-features to characterize the geometrical properties of problems, (3) meta-learner module which compares the performance of instance-based and model-based learning approaches for optimal framework design, and (4) performance evaluation module which introduces two criteria, Spearman\u27s ranking correlation coefficient and hit ratio, to evaluate the system on the accuracy of model ranking prediction and the precision of the best model recommendation, respectively. To further improve the performance of meta-learning for meta-modeling recommendation, different types of feature reduction techniques, including singular value decomposition, stepwise regression and ReliefF, are studied. Experiments show that our proposed framework is able to achieve 94% correlation on model rankings, and a 91% hit ratio on best model recommendation. Moreover, the computational cost of meta-modeling recommendation is significantly reduced from an order of minutes to seconds compared to traditional trial-and-error and ensemble process. The proposed framework can significantly advance the research in meta-modeling recommendation, and can be applied for data-driven system modeling
Rainfall-runoff modelling of a watershed
In this study an adaptive neuro-fuzzy inference system was used for rainfall-runoff modelling for the Nagwan watershed in the Hazaribagh District of Jharkhand, India. Different combinations of rainfall and runoff were considered as the inputs to the model, and runoff of the current day was considered as the output. Input space partitioning for model structure identification was done by grid partitioning. A hybrid learning algorithm consisting of back-propagation and least-squares estimation was used to train the model for runoff estimation. The optimal learning parameters were determined by trial and error using gaussian membership functions. Root mean square error and correlation coefficient were used for selecting the best performing model. Model with one input and 91 gauss membership function outperformed and used for runoff prediction. Keywords: Rainfall, runoff, modelling, ANFI
How the Retrosplenial Cortex Changes Throughout the Process of Associative Learning
The retrosplenial cortex, located in the posterior midline of the brain, is a small but important region that plays a role in memory and spatial processing (Vann, 2009). In fact, it is one of the first regions to undergo pathological changes in Alzheimer’s Disease. The functional role of the retrosplenial cortex in learning remains unknown. In this experiment, we evaluated patterns of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) activations in the retrosplenial cortex during associative learning. We recruited 27 participants, who performed a conditional visuomotor associative learning task while we collected fMRI data. During the task, participants were required to learn the associations of three stimuli: two stimuli were always associated with the same responses, while the third stimulus changed its association conditional depending on what stimulus preceded it. Participants learned these associations through trial-and-error. We hypothesized that patterns of BOLD fMRI activations in the retrosplenial cortex would converge on a stable pattern as learning progressed. This prediction would be evident by a progressive increase in correlation in the spatial patterns of activations with learning. Twenty-one participants successfully completed the fMRI task and contributed to our final analyses. We evaluated changes in brain activations in blocks of 10 trials then evaluated how the brain activations in the retrosplenial cortex during the first ten trials correlated with all subsequent blocks of ten trials. No consistent patterns of correlations across learning were evident amongst the participants. For example, in some participants, correlations of spatial patterns of activation increased with learning but in others the opposite pattern was observed. The different patterns of activation across participants may indicate individual differences in learning, which could help in understanding how people learn. Future work will examine how these patterns of activations are related to behavior on a trial-by-trial basis
Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models
Image-based precision medicine aims to personalize treatment decisions based
on an individual's unique imaging features so as to improve their clinical
outcome. Machine learning frameworks that integrate uncertainty estimation as
part of their treatment recommendations would be safer and more reliable.
However, little work has been done in adapting uncertainty estimation
techniques and validation metrics for precision medicine. In this paper, we use
Bayesian deep learning for estimating the posterior distribution over factual
and counterfactual outcomes on several treatments. This allows for estimating
the uncertainty for each treatment option and for the individual treatment
effects (ITE) between any two treatments. We train and evaluate this model to
predict future new and enlarging T2 lesion counts on a large, multi-center
dataset of MR brain images of patients with multiple sclerosis, exposed to
several treatments during randomized controlled trials. We evaluate the
correlation of the uncertainty estimate with the factual error, and, given the
lack of ground truth counterfactual outcomes, demonstrate how uncertainty for
the ITE prediction relates to bounds on the ITE error. Lastly, we demonstrate
how knowledge of uncertainty could modify clinical decision-making to improve
individual patient and clinical trial outcomes
Variability in Singing and in Song in the Zebra Finch
Variability is a defining feature of the oscine song learning process, reflected in song and in the neural pathways involved in song learning. For the zebra finch, juveniles learning to sing typically exhibit a high degree of vocal variability, and this variability appears to be driven by a key brain nucleus. It has been suggested that this variability is a necessary part of a trial-Ă‚Ââ€and-Ă‚Ââ€error learning process in which the bird must search for possible improvements to its song. Our work examines the role this variability plays in learning in two ways: through behavioral experiments with juvenile zebra finches, and through a computational model of parts of the oscine brain. Previous studies have shown that some finches exhibit less variability during the learning process than others by producing repetitive vocalizations. A constantly changing song model was played to juvenile zebra finches to determine whether auditory stimuli can affect this behavior. This stimulus was shown to cause an overall increase in repetitiveness; furthermore, there was a correlation between repetitiveness at an early stage in the learning process and the length of time a bird is repetitive overall, and birds that were repetitive tended to repeat the same thing over an extended period of time. The role of a key brain nucleus involved in song learning was examined through computational modeling. Previous studies have shown that this nucleus produces variability in song, but can also bias the song of a bird in such a way as to reduce errors while singing. Activity within this nucleus during singing is predominantly uncorrelated with the timing of the song, however a portion of this activity is correlated in such a manner. The modeling experiments consider the possibility that this persistent signal is part of a trial-Ă‚Ââ€and-Ă‚Ââ€error search and contrast this with the possibility that the persistent signal is the product of some mechanism to directly improve song. Simulation results show that a mixture of timing-Ă‚Ââ€dependent and timing-Ă‚Ââ€independent activity in this nucleus produces optimal learning results for the case where the persistent signal is a key component of a trial-Ă‚Ââ€and-Ă‚Ââ€error search, but not in the case where this signal will directly improve song. Although a mixture of timing-Ă‚Ââ€locked and timing-Ă‚Ââ€independent activity produces optimal results, the ratio found to be optimal within the model differs from what has been observed in vivo. Finally, novel methods for the analysis of birdsong, motivated by the high variability of juvenile song, are presented. These methods are designed to work with sets of song samples rather than through pairwise comparison. The utility of these methods is demonstrated, as well as results illustrating how such methods can be used as the basis for aggregate measures of song such as repertoire complexity
Attention-based Multi-task Learning for Base Editor Outcome Prediction
Human genetic diseases often arise from point mutations, emphasizing the
critical need for precise genome editing techniques. Among these, base editing
stands out as it allows targeted alterations at the single nucleotide level.
However, its clinical application is hindered by low editing efficiency and
unintended mutations, necessitating extensive trial-and-error experimentation
in the laboratory. To speed up this process, we present an attention-based
two-stage machine learning model that learns to predict the likelihood of all
possible editing outcomes for a given genomic target sequence. We further
propose a multi-task learning schema to jointly learn multiple base editors
(i.e. variants) at once. Our model's predictions consistently demonstrated a
strong correlation with the actual experimental results on multiple datasets
and base editor variants. These results provide further validation for the
models' capacity to enhance and accelerate the process of refining base editing
designs
Attention-based Multi-task Learning for Base Editor Outcome Prediction
Human genetic diseases often arise from point mutations, emphasizing the
critical need for precise genome editing techniques. Among these, base editing
stands out as it allows targeted alterations at the single nucleotide level.
However, its clinical application is hindered by low editing efficiency and
unintended mutations, necessitating extensive trial-and-error experimentation
in the laboratory. To speed up this process, we present an attention-based
two-stage machine learning model that learns to predict the likelihood of all
possible editing outcomes for a given genomic target sequence. We further
propose a multi-task learning schema to jointly learn multiple base editors
(i.e. variants) at once. Our model's predictions consistently demonstrated a
strong correlation with the actual experimental results on multiple datasets
and base editor variants. These results provide further validation for the
models' capacity to enhance and accelerate the process of refining base editing
designs.Comment: Extended Abstract presented at Machine Learning for Health (ML4H)
symposium 2023, December 10th, 2023, New Orleans, United States, 15 pages.
arXiv admin note: substantial text overlap with arXiv:2310.0291
- …