The principles of space charge doping

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Table of contents

1 Introduction 
1.1 Task-set, from the what to the how
1.1.1 What is a task-set ?
1.1.2 The prefrontal cortex is the locus of cognitive control
1.2 A computational model for human executive control and adaptive behavior
1.2.1 Reinforcement learning
1.2.2 Previous studies on reward-based decision making
1.2.3 Bayesian inference
1.2.4 The model of Collins and Koechlin
1.3 Building blocks of a representational model of task-set implementation in the PFC
1.3.1 Fusi andWang’s biological realistic network model for decisionmaking
1.3.2 Hebb rule for synaptic plasticity
1.3.3 Experimental evidence for the behavioral Hebbian learning rule
1.3.4 Theoretical work of Mattia Rigotti and Stefano Fusi
2 Experimental results 
2.1 Experimental design
2.1.1 The experimental task
2.1.2 Debrieng
2.2 Behavioral results
2.2.1 General behavior
2.2.2 Quantitative measures of behavior
2.2.3 Behavioral results of Experiment 1
2.2.4 Behavioral results of Experiment 2
2.2.5 Summary on the behavioral results of Experiment 1 and
Experiment
3 Model description 
3.1 The network architecture
3.1.1 The associative network
3.1.2 The task-rule network
3.2 Network dynamics
3.2.1 Decision-making dynamics in AN
3.2.2 Network dynamics in TN
3.3 Plasticity
3.3.1 General form of plasticity rules
3.3.2 AN plasticity rules
3.3.3 TN plasticity rules
3.4 Comparison with other models
3.4.1 Fusi andWang’s biological realistic network model for decisionmaking
3.4.2 Link from AN to reinforcement learning
3.4.3 TN dierence with the study from M. Rigotti et al
4 Synaptic dynamics of the AN 
4.1 Learning and forgetting associations
4.1.1 Learning and forgetting associations over several episodes
4.1.3 Detailed analysis of the forget and learn pattern
4.2 Sensorimotor associations are learned one by one
4.3 Eect of a noisy trial on AN connectivity
4.3.1 A noisy trial at the beginning of an episode
4.3.2 A noisy trial at the end of an episode
4.3.3 Conclusion on the eect of noisy trials on AN connectivity .
4.4 Conclusion on the learning dynamics of the AN
5 Synaptic dynamics of the TN: the formation of task-sets 
5.1 Synaptic encoding of temporal contiguity
5.1.1 A framework from Ostojic and Fusi
5.1.2 Application of this framework to the TN model
5.1.3 The speed-accuracy trade-o
5.2 Introduction: an example of TN activity
5.3 The TN is able to encode the task-sets of the recurrent session .
5.4 Eect of the inferential bias from the TN to the AN
5.4.1 Inference bias from the Perfect TN
5.4.2 Inference bias with the Plastic TN
5.5 Eect of the inferential bias from the TN to the AN in a noisy environment
5.5.1 A noisy trial at the beginning of an episode
5.5.2 A noisy trial at the end of an episode
5.5.3 The overall eect of noisy trials
5.5.4 Conclusion on the eect of noisy trials
5.6 Learning limits and their eect on the inferential bias
5.6.1 Propagation of a spurious connection after learning in the two-dimensional AN-TN
5.6.2 Learning in the TN without any inferential bias from the TN to the AN
5.6.3 Eect of the inferential feedback from the TN to the AN
5.6.4 Eect of the inferential feedback on rst correct trials
5.6.5 Eect of the strength of the inference bias
5.7 Conclusion
6 Model tting and comparison 
6.1 Methods
6.1.1 Model tting: techniques
6.1.2 Model comparison: quantitative criteria
6.1.3 Model comparison: qualitative criteria
6.1.4 Model specications
6.2 Model selection and comparison: recurrent session
6.2.1 LSE model ts and simulations of the recurrent session
6.2.2 AIC and BIC in the recurrent session
6.2.3 Parameters analysis
6.2.4 TN dynamics with tted parameters
6.3 Model selection and comparison: open-ended session
6.3.1 LSE Model ts and simulations of the open-ended session
6.3.2 AIC and BIC in the open-ended session
6.3.3 Parameters analysis
6.4 The memory eect
6.5 Conclusion
7 Testing model predictions: eects of learning the task-structure on performance 
7.1 Positive bias from learned rules: task-set retrieval
7.1.1 Model-based classication of trials
7.1.2 Trials and episodes distributions
7.1.3 Model prediction: probability of making a correct choice on the AFC trial
7.1.4 Model-based classication of subjects
7.1.5 Link between the model-based classication of subjects and the increment parameter values, as well as mean performance
7.1.6 Link to the post-experiment debrieng classication of subjects
7.1.7 Reaction times
7.1.8 Summary of the task-set retrieval prediction due to positive bias from learned rules
7.2 Prediction for an incorrect bias from learned rules
7.2.1 A rewarded noisy trial
7.2.2 The model-based classication of trials
7.2.3 Trials and episodes distributions
7.2.4 Model prediction: probability of making an incorrect choice on the AMN trial
7.2.5 Summary of the incorrect task-set retrieval prediction due to an incorrect bias from learned rules
7.3 Discussion
7.4 Prediction for an incorrect bias from two overlapping rules
8 Neuroimaging analysis 
8.1 Description of Functional Magnetic Resonance Imaging
8.1.1 BOLD physiology
8.1.2 Model-based fMRI
8.2 Model-based fMRI: methods
8.2.1 Pre-processing
8.2.2 General Linear Model, rst-level analysis
8.2.3 Second level analysis
8.2.4 Region of Interest (ROI)
8.3 Retrieving previous studies results on prediction error with GLM 1
8.4 AN synaptic strength of the chosen association when making a decision
8.5 Consistency between AN encoding and TN belief when making a decision
8.6 Investigating task-set retrieval at the feedback time
8.6.1 Positive linear eects in a dorsomedial prefrontal node
8.6.2 Positive linear eects in a dorsolateral prefrontal node
8.7 Comparative analysis of the two sessions
8.7.1 Dorsomedial prefrontal cortex is activated specically when a task-set is monitored at the time of decision
8.7.2 Dorsolateral prefrontal cortex is activated specically when a task-set is retrieved
8.7.3 Dorsomedial prefrontal cortex is also activated specically when a task-set is retrieved
8.8 Conclusion
9 Discussion 
9.1 General conclusion
9.2 Computational complexity
9.3 The question of the inference bias from TN to AN
9.4 Expected and unexpected uncertainties
9.5 Tracking the statistics of the environment
9.6 Behavioral shift
9.7 Where are the AN and the TN in the brain?