scholarly journals Predication-Error-Based Intrinsically Motivated Saccade Learning

2021 ◽  
Vol 12 (1) ◽  
pp. 48
Author(s):  
Ihsan Ahmed ◽  
Wasif Muhammad ◽  
Ali Asghar ◽  
Muhammad Jehanzeb Irshad
Keyword(s):  
Performance Metrics ◽  
Sensory Information ◽  
Movement Control ◽  
Saccadic Eye Movements ◽  
Prediction Errors ◽  
Intrinsic Reward ◽  
Proposed Model ◽  
Eye Motion ◽  
The Mean ◽  
Sensory Prediction

The quick, simultaneous movements of both eyes in the same direction is called a saccade, and the process of developing an internal model for the eyes’ movement-control based on visual stimuli is called saccade learning. All humans use this type of eye motion to bring salient objects to the foveal locations of the retina, even if the objects are located randomly in the surrounding environment. To begin with, infants are not able to perform this type of eye motion, but sensory information motivates them to start learning saccadic behavior. In this paper, a sensory prediction-error-based intrinsically motivated model is proposed for learning saccadic eye movements, and this approach is more consistent with biological systems for saccade learning. Predicted Coding/Biased Competition using Divisive Input Modulation (PC/BC-DIM) network is used for saccade learning using sensory prediction errors. The quantification of sensory prediction errors provides an intrinsic reward. A simulated humanoid agent, iCub, is used to assess and quantify the performance of the proposed model. The performance metrics used for this purpose are percentage mean post-saccadic distance and standard deviation. The mean post-saccadic distance for the proposed model was less than 1°, which is biologically plausible.

scholarly journals Dopamine neuron ensembles signal the content of sensory prediction errors

2019 ◽  
Author(s):  
Thomas A. Stalnaker ◽  
James D. Howard ◽  
Yuji K. Takahashi ◽  
Samuel J. Gershman ◽  
Thorsten Kahnt ◽  
...  
Keyword(s):  
Neural Activity ◽  
Prediction Error ◽  
Sensory Information ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Bold Response ◽  
Dopamine System ◽  
Sensory Event ◽  
Specific Teaching ◽  
Sensory Prediction

AbstractDopamine neurons respond to errors in predicting value-neutral sensory information. These data, combined with causal evidence that dopamine transients support sensory-based associative learning, suggest that the dopamine system signals a multidimensional prediction error. Yet such complexity is not evident in individual neuron or average neural activity. How then do downstream areas know what to learn in response to these signals? One possibility is that information about content is contained in the pattern of firing across many dopamine neurons. Consistent with this, here we show that the pattern of firing across a small group of dopamine neurons recorded in rats signals the identity of a mis-predicted sensory event. Further, this same information is reflected in the BOLD response elicited by sensory prediction errors in human midbrain. These data provide evidence that ensembles of dopamine neurons provide highly specific teaching signals, opening new possibilities for how this system might contribute to learning.

scholarly journals Dopamine neuron ensembles signal the content of sensory prediction errors

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Thomas A Stalnaker ◽  
James D Howard ◽  
Yuji K Takahashi ◽  
Samuel J Gershman ◽  
Thorsten Kahnt ◽  
...  
Keyword(s):  
Associative Learning ◽  
Prediction Error ◽  
Sensory Information ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Bold Response ◽  
Dopamine System ◽  
Sensory Event ◽  
Specific Teaching ◽  
Sensory Prediction

Dopamine neurons respond to errors in predicting value-neutral sensory information. These data, combined with causal evidence that dopamine transients support sensory-based associative learning, suggest that the dopamine system signals a multidimensional prediction error. Yet such complexity is not evident in the activity of individual neurons or population averages. How then do downstream areas know what to learn in response to these signals? One possibility is that information about content is contained in the pattern of firing across many dopamine neurons. Consistent with this, here we show that the pattern of firing across a small group of dopamine neurons recorded in rats signals the identity of a mis-predicted sensory event. Further, this same information is reflected in the BOLD response elicited by sensory prediction errors in human midbrain. These data provide evidence that ensembles of dopamine neurons provide highly specific teaching signals, opening new possibilities for how this system might contribute to learning.

Simple explicit model of liquid mean flow in region above axial impeller

1985 ◽  
Vol 50 (11) ◽  
pp. 2396-2410
Author(s):  
Miloslav Hošťálek ◽  
Ivan Fořt
Keyword(s):  
Vortex Flow ◽  
Flow Model ◽  
Quantitative Agreement ◽  
Mean Flow ◽  
Flat Bottom ◽  
Proposed Model ◽  
Minimum Number ◽  
The Mean ◽  
Model Equations ◽  
Radial Circulation

The study describes a method of modelling axial-radial circulation in a tank with an axial impeller and radial baffles. The proposed model is based on the analytical solution of the equation for vortex transport in the mean flow of turbulent liquid. The obtained vortex flow model is tested by the results of experiments carried out in a tank of diameter 1 m and with the bottom in the shape of truncated cone as well as by the data published for the vessel of diameter 0.29 m with flat bottom. Though the model equations are expressed in a simple form, good qualitative and even quantitative agreement of the model with reality is stated. Apart from its simplicity, the model has other advantages: minimum number of experimental data necessary for the completion of boundary conditions and integral nature of these data.

scholarly journals Modeling the Excess Velocity of Low-Viscous Taylor Droplets in Square Microchannels

Fluids ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 162 ◽  
Author(s):  
Thorben Helmers ◽  
Philip Kemper ◽  
Jorg Thöming ◽  
Ulrich Mießner
Keyword(s):  
High Speed ◽  
Process Intensification ◽  
Continuous Phase ◽  
Channel Cross Section ◽  
Optimization Approach ◽  
Mean Flow ◽  
Bypass Flow ◽  
Wall Film ◽  
Proposed Model ◽  
The Mean

Microscopic multiphase flows have gained broad interest due to their capability to transfer processes into new operational windows and achieving significant process intensification. However, the hydrodynamic behavior of Taylor droplets is not yet entirely understood. In this work, we introduce a model to determine the excess velocity of Taylor droplets in square microchannels. This velocity difference between the droplet and the total superficial velocity of the flow has a direct influence on the droplet residence time and is linked to the pressure drop. Since the droplet does not occupy the entire channel cross-section, it enables the continuous phase to bypass the droplet through the corners. A consideration of the continuity equation generally relates the excess velocity to the mean flow velocity. We base the quantification of the bypass flow on a correlation for the droplet cap deformation from its static shape. The cap deformation reveals the forces of the flowing liquids exerted onto the interface and allows estimating the local driving pressure gradient for the bypass flow. The characterizing parameters are identified as the bypass length, the wall film thickness, the viscosity ratio between both phases and the C a number. The proposed model is adapted with a stochastic, metaheuristic optimization approach based on genetic algorithms. In addition, our model was successfully verified with high-speed camera measurements and published empirical data.

scholarly journals A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI

2021 ◽  
Vol 11 (8) ◽  
pp. 3636
Author(s):  
Faria Zarin Subah ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
State Of The Art ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Bootstrap Analysis ◽  
Proposed Model ◽  
Art Methods ◽  
The Mean

Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.

Development of an Adaptive Human-Machine-Interface to Minimize Driver Distraction and Workload

2013 ◽  
Author(s):  
Matthew Westin ◽  
Ronald Dougherty ◽  
Christopher Depcik ◽  
Austin Hausmann ◽  
Charles Sprouse
Keyword(s):  
Performance Metrics ◽  
Sensory Information ◽  
Driver Distraction ◽  
Test Drive ◽  
Workload Management ◽  
City Street ◽  
Proximity Detection ◽  
Test Platform ◽  
In Series ◽  
And Performance

The original use of the vehicle dashboard was to provide enough sensory information to inform the driver of the current engine and vehicle status and performance. Over time, it has evolved into an entertainment system that includes person-to-person communication, global positioning information, and the Internet, just to name a few. Each of these new features adds to the amount of information that drivers must absorb, leading to potential distraction and possible increases in the number and types of accidents. In order to provide an overview of these issues, this paper summarizes previous work on driver distraction and workload, demonstrating the importance of addressing those issues that compete for driver attention and action. In addition, a test platform vehicle is introduced which has the capability of assessing modified dashboards and consoles, as well as the ability to acquire relevant driving performance data. Future efforts with this test platform will be directed toward helping to resolve the critical tug-of-war between providing more information and entertainment while keeping drivers and their passengers safe. The long-term goal of this research is to evaluate the various technological innovations available for inclusion in the driving environment and determining how to optimize driver information delivery without excessive distraction and workload. The information presented herein is the first step in that effort of developing an adaptive distraction/workload management system that monitors performance metrics and provides selected feedback to drivers. The test platform (1973 VW Beetle converted to a plug-in series hybrid) can provide speed, location (GPS), 3-D acceleration, and rear proximity detection. The test drive route was a 2 km × 3 km city street circuit which took approximately 25 minutes to complete. Data is provided herein to demonstrate these capabilities. In addition, the platform has driver selectable layouts for the instrument cluster and console (LCD screens). The test platform is planned for use to determine driver preferences (e.g., dashboard/console configurations) and attention performance in addition to identifying optimal real-time feedback for drivers with different demographics.

Exploring Cognition with Brain–Machine Interfaces

2022 ◽  
Vol 73 (1) ◽  
pp. 131-158
Author(s):  
Richard A. Andersen ◽  
Tyson Aflalo ◽  
Luke Bashford ◽  
David Bjånes ◽  
Spencer Kellis
Keyword(s):  
Posterior Parietal Cortex ◽  
Sensory Information ◽  
Movement Control ◽  
Brain Machine Interfaces ◽  
Motor Commands ◽  
Cortical Motor ◽  
Structured Representations ◽  
Posterior Parietal ◽  
Integration Planning ◽  
Motor Actions

Traditional brain–machine interfaces decode cortical motor commands to control external devices. These commands are the product of higher-level cognitive processes, occurring across a network of brain areas, that integrate sensory information, plan upcoming motor actions, and monitor ongoing movements. We review cognitive signals recently discovered in the human posterior parietal cortex during neuroprosthetic clinical trials. These signals are consistent with small regions of cortex having a diverse role in cognitive aspects of movement control and body monitoring, including sensorimotor integration, planning, trajectory representation, somatosensation, action semantics, learning, and decision making. These variables are encoded within the same population of cells using structured representations that bind related sensory and motor variables, an architecture termed partially mixed selectivity. Diverse cognitive signals provide complementary information to traditional motor commands to enable more natural and intuitive control of external devices.

scholarly journals Recommending Mobile Microblog Users via a Tensor Factorization Based on User Cluster Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xiangwen Liao ◽  
Lingying Zhang ◽  
Jingjing Wei ◽  
Dingda Yang ◽  
Guolong Chen
Keyword(s):  
Network Centrality ◽  
Network Clustering ◽  
Tensor Factorization ◽  
Latent Factors ◽  
Cp Decomposition ◽  
Temporal Features ◽  
User Influence ◽  
Proposed Model ◽  
The Mean ◽  
Content Similarity

User influence is a very important factor for microblog user recommendation in mobile social network. However, most existing user influence analysis works ignore user’s temporal features and fail to filter the marketing users with low influence, which limits the performance of recommendation methods. In this paper, a Tensor Factorization based User Cluster (TFUC) model is proposed. We firstly identify latent influential users by neural network clustering. Then, we construct a features tensor according to latent influential user’s opinion, activity, and network centrality information. Furthermore, user influences are predicted by the latent factors resulting from the temporal restrained CP decomposition. Finally, we recommend microblog users considering both user influence and content similarity. Our experimental results show that the proposed model significantly improves recommendation performance. Meanwhile, the mean average precision of TFUC outperforms the baselines with 3.4% at least.

scholarly journals Development of Macroscopic Cell-Based Logistic Lane Change Prediction Model

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Christina Ng ◽  
Susilawati Susilawati ◽  
Md Abdus Samad Kamal ◽  
Irene Mei Leng Chew
Keyword(s):  
Prediction Model ◽  
Transmission Model ◽  
Lane Change ◽  
Traffic Data ◽  
Cell Transmission Model ◽  
Traffic State ◽  
Proposed Model ◽  
Significant Difference ◽  
The Mean ◽  
Change Prediction

This paper aims at developing a macroscopic cell-based lane change prediction model in a complex urban environment and integrating it into cell transmission model (CTM) to improve the accuracy of macroscopic traffic state estimation. To achieve these objectives, first, based on the observed traffic data, the binary logistic lane change model is developed to formulate the lane change occurrence. Second, the binary logistic lane change is integrated into CTM by refining CTM formulations on how the vehicles in the cell are moving from one cell to another in a longitudinal manner and how cell occupancy is updated after lane change occurrences. The performance of the proposed model is evaluated by comparing the simulated cell occupancy of the proposed model with cell occupancy of US-101 next generation simulation (NGSIM) data. The results indicated no significant difference between the mean of the cell occupancies of the proposed model and the mean of cell occupancies of actual data with a root-mean-square-error (RMSE) of 0.04. Similar results are found when the proposed model was further tested with I80 highway data. It is suggested that the mean of cell occupancies of I80 highway data was not different from the mean of cell occupancies of the proposed model with 0.074 RMSE (0.3 on average).

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