scholarly journals Frontal cortex function derives from hierarchical predictive coding

2016 ◽  
Author(s):  
William H Alexander ◽  
Joshua W Brown
Keyword(s):  
Machine Learning ◽  
Working Memory ◽  
Prefrontal Cortex ◽  
Frontal Cortex ◽  
Predictive Coding ◽  
Memory Storage ◽  
Prediction Errors ◽  
Learning Approaches ◽  
Hierarchical Groups ◽  
Goal Directed Behavior

The frontal lobes are essential for human volition and goal-directed behavior, yet their function remains unclear. While various models have highlighted working memory, reinforcement learning, and cognitive control as key functions, a single framework for interpreting the range of effects observed in prefrontal cortex has yet to emerge. Here we show that a simple computational motif based on predictive coding can be stacked hierarchically to learn and perform arbitrarily complex goal-directed behavior. The resulting Hierarchical Error Representation (HER) model simulates a wide array of findings from fMRI, ERP, single-units, and neuropsychological studies of both lateral and medial prefrontal cortex. Additionally, the model compares favorably with current machine learning approaches, learning more rapidly and with comparable performance, while self-organizing representations into efficient hierarchical groups and managing working memory storage. By reconceptualizing lateral prefrontal activity as anticipating prediction errors, the HER model provides a novel unifying account of prefrontal cortex function with broad implications both for understanding the frontal cortex and building more powerful machine learning applications.

scholarly journals Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 689 ◽  
Author(s):  
Tyler McCandless ◽  
Susan Dettling ◽  
Sue Ellen Haupt
Keyword(s):  
Machine Learning ◽  
Solar Power ◽  
Network Models ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Validation Dataset ◽  
Prediction Errors ◽  
Learning Approaches ◽  
Power Prediction ◽  
Neural Network Models

This work compares the solar power forecasting performance of tree-based methods that include implicit regime-based models to explicit regime separation methods that utilize both unsupervised and supervised machine learning techniques. Previous studies have shown an improvement utilizing a regime-based machine learning approach in a climate with diverse cloud conditions. This study compares the machine learning approaches for solar power prediction at the Shagaya Renewable Energy Park in Kuwait, which is in an arid desert climate characterized by abundant sunshine. The regime-dependent artificial neural network models undergo a comprehensive parameter and hyperparameter tuning analysis to minimize the prediction errors on a test dataset. The final results that compare the different methods are computed on an independent validation dataset. The results show that the tree-based methods, the regression model tree approach, performs better than the explicit regime-dependent approach. These results appear to be a function of the predominantly sunny conditions that limit the ability of an unsupervised technique to separate regimes for which the relationship between the predictors and the predictand would differ for the supervised learning technique.

scholarly journals Delay-Period Activity and Executive Functions of the Prefrontal Cortex

2019 ◽  
Vol 10 (1) ◽  
pp. 3
Author(s):  
Aarron Phensy ◽  
Sven Kroener
Keyword(s):  
Working Memory ◽  
Prefrontal Cortex ◽  
Executive Functions ◽  
Delay Period ◽  
Goal Directed Behavior

The term “working memory” (WM) describes the ability to maintain and manipulate information in the memory for the guidance of goal-directed behavior [...]

Prediction of drug synergy in cancer using ensemble-based machine learning techniques

2018 ◽  
Vol 32 (11) ◽  
pp. 1850132 ◽  
Author(s):  
Harpreet Singh ◽  
Prashant Singh Rana ◽  
Urvinder Singh
Keyword(s):  
Machine Learning ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Prediction Errors ◽  
Learning Approaches ◽  
Inference System ◽  
Drug Synergy ◽  
Learning Techniques

Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug–drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

scholarly journals Hierarchy of prediction errors for auditory events in human temporal and frontal cortex

2016 ◽  
Vol 113 (24) ◽  
pp. 6755-6760 ◽  
Author(s):  
Stefan Dürschmid ◽  
Erik Edwards ◽  
Christoph Reichert ◽  
Callum Dewar ◽  
Hermann Hinrichs ◽  
...  
Keyword(s):  
Frontal Cortex ◽  
Predictive Coding ◽  
Low Frequency ◽  
Event Related Potentials ◽  
Prediction Errors ◽  
Frequency Bands ◽  
Deviance Detection ◽  
Statistical Regularities ◽  
Related Potentials ◽  
Task Irrelevant

Predictive coding theories posit that neural networks learn statistical regularities in the environment for comparison with actual outcomes, signaling a prediction error (PE) when sensory deviation occurs. PE studies in audition have capitalized on low-frequency event-related potentials (LF-ERPs), such as the mismatch negativity. However, local cortical activity is well-indexed by higher-frequency bands [high-γ band (Hγ): 80–150 Hz]. We compared patterns of human Hγ and LF-ERPs in deviance detection using electrocorticographic recordings from subdural electrodes over frontal and temporal cortices. Patients listened to trains of task-irrelevant tones in two conditions differing in the predictability of a deviation from repetitive background stimuli (fully predictable vs. unpredictable deviants). We found deviance-related responses in both frequency bands over lateral temporal and inferior frontal cortex, with an earlier latency for Hγ than for LF-ERPs. Critically, frontal Hγ activity but not LF-ERPs discriminated between fully predictable and unpredictable changes, with frontal cortex sensitive to unpredictable events. The results highlight the role of frontal cortex and Hγ activity in deviance detection and PE generation.

scholarly journals Offline Replay Supports Planning: fMRI Evidence from Reward Revaluation

2017 ◽  
Author(s):  
Ida Momennejad ◽  
A. Ross Otto ◽  
Nathaniel D. Daw ◽  
Kenneth A. Norman
Keyword(s):  
Machine Learning ◽  
Computational Cost ◽  
Decision Time ◽  
Anterior Cingulate ◽  
Prediction Errors ◽  
Activity Increase ◽  
Planning Methods ◽  
Goal Directed Behavior ◽  
The Brain

AbstractMaking decisions in sequentially structured tasks requires integrating distally acquired information. The extensive computational cost of such integration challenges planning methods that integrate online, at decision time. Furthermore, it remains unclear whether “offline” integration during replay supports planning, and if so which memories should be replayed. Inspired by machine learning, we propose that (a) offline replay of trajectories facilitates integrating representations that guide decisions, and (b) unsigned prediction errors (uncertainty) trigger such integrative replay. We designed a 2-step revaluation task for fMRI, whereby participants needed to integrate changes in rewards with past knowledge to optimally replan decisions. As predicted, we found that (a) multi-voxel pattern evidence for off-task replay predicts subsequent replanning; (b) neural sensitivity to uncertainty predicts subsequent replay and replanning; (c) off-task hippocampus and anterior cingulate activity increase when revaluation is required. These findings elucidate how the brain leverages offline mechanisms in planning and goal-directed behavior under uncertainty.

scholarly journals Learning from machine learning: prediction of age-related athletic performance decline trajectories

GeroScience ◽  
2021 ◽  
Author(s):  
Christoph Hoog Antink ◽  
Anne K. Braczynski ◽  
Bergita Ganse
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Prediction Errors ◽  
Learning Approaches ◽  
Single Measurement ◽  
Age Related ◽  
Decline Curve ◽  
Machine Learning Approach ◽  
Decline Rate ◽  
Performance Decline

AbstractFactors that determine individual age-related decline rates in physical performance are poorly understood and prediction poses a challenge. Linear and quadratic regression models are usually applied, but often show high prediction errors for individual athletes. Machine learning approaches may deliver more accurate predictions and help to identify factors that determine performance decline rates. We hypothesized that it is possible to predict the performance development of a master athlete from a single measurement, that prediction by a machine learning approach is superior to prediction by the average decline curve or an individually shifted decline curve, and that athletes with a higher starting performance show a slower performance decline than those with a lower performance. The machine learning approach was implemented using a multilayer neuronal network. Results showed that performance prediction from a single measurement is possible and that the prediction by a machine learning approach was superior to the other models. The estimated performance decline rate was highest in athletes with a high starting performance and a low starting age, as well as in those with a low starting performance and high starting age, while the lowest decline rate was found for athletes with a high starting performance and a high starting age. Machine learning was superior and predicted trajectories with significantly lower prediction errors compared to conventional approaches. New insights into factors determining decline trajectories were identified by visualization of the model outputs. Machine learning models may be useful in revealing unknown factors that determine the age-related performance decline.

scholarly journals Reducing Tropical Cyclone Prediction Errors Using Machine Learning Approaches

2017 ◽  
Vol 114 ◽  
pp. 314-323 ◽  
Author(s):  
Michael B. Richman ◽  
Lance M. Leslie ◽  
Hamish A. Ramsay ◽  
Philip J. Klotzbach
Keyword(s):  
Machine Learning ◽  
Tropical Cyclone ◽  
Prediction Errors ◽  
Learning Approaches ◽  
Cyclone Prediction
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