Title Page
ABSTRACT
국문 초록
Contents
ABBREVIATION 21
CHAPTER 1. GENERAL INTRODUCTION 23
1.1. Dynamic foraging in naturalistic environment. 23
1.2. General function and anatomy of the rat's mPFC. 27
1.3. Role of mPFC in threat processing 29
1.3.1. The IL: Fear Extinction and Active Fear Response 29
1.3.2. The PL: Fear Expression and Passive Fear Response 31
1.4. Role of mPFC in optimizing behavior appropriate to the context. 34
1.5. Role of mPFC valence-based strategic navigation 37
1.6. Population Doctrine 39
1.7. Outline of chapters 43
CHAPTER 2. Encountering the Modified Lobsterbot Elicits Approach-Avoidance Conflict and Defensive Behaviors in Rats 44
2.1. Introduction 44
2.2. Materials and Methods 47
2.2.1. Subjects 47
2.2.2. Apparatus and Procedures 47
2.2.3. Data Acquisition 48
2.2.4. Statistical analysis 49
2.3. Results 52
2.4. Discussion 55
CHAPTER 3. Population Analysis Reveals Heterogeneous Encoding of Spatial Information During Naturalistic Foraging 58
3.1. Introduction 58
3.2. Materials and Methods 60
3.2.1. Subjects and Procedures 60
3.2.2. Microdrive Fabrication 60
3.2.3. Surgical procedures 63
3.2.4. Single-unit recording data acquisition 63
3.2.5. Histology 64
3.2.6. Behavior measurement and head tracking 64
3.2.7. Spatial decoding data preparation and spatial tuning curve analysis. 65
3.2.8. Deep artificial neural network (dANN) and data preparation 68
3.2.9. Decoding error heatmap construction 72
3.2.10. Linear Discriminant Analysis and Principal Component Analysis 72
3.2.11. Behavior-responsive unit analysis 73
3.2.12. Hierarchical cell clustering analysis 74
3.2.13. Run-and-stop event analysis 75
3.2.14. Naïve Bayes Event Classifier and data preparation 76
3.2.15. Statistical analysis 78
3.3. Results 80
3.3.1. Rats exhibited consistent foraging behavior during the L2 phase. 80
3.3.2. Generalized spatial selective firing patterns in the PL and IL. 84
3.3.3. Heterogeneous spatial encoding in neural population activity of the PL and IL. 89
3.3.4. Removing the Lobsterbot Decreases Spatial Decoding Accuracy. 98
3.3.5. Excluding non-navigational behaviors in the N-zone enhances spatial decoding accuracy. 102
3.3.6. LDA reveals distinct E-zone functional states, suggesting differing neural activity compared to F and N zones. 104
3.3.7. Hierarchical clustering confirmed functionally distinctive sub-populations in the E-zone 108
3.3.8. Population activities in the E-zone encode success and failure of avoidance response. 113
3.4. Discussion 116
3.4.1. Decoding spatial correlates from the mPFC. 116
3.4.2. The mPFC encodes an abstract, valence-based space modulated by task-relevant factors 118
3.4.3. The necessity of spatial dimension in mPFC encoding. 120
3.4.4. The mPFC encode feature-based abstract space exclusively during strategic navigation. 122
3.4.5. Population activities in the E-zone encode different types of avoidance response. 126
3.4.6. Combined model of the mPFC: spatial representation and active foraging 128
CHAPTER 4. General Discussion 131
REFERENCES 134
SUPPLEMENTARY MATERIALS 155
A. Supplementary figures and tables 155
B. Additional study 166
Supplementary Table 1. Multiple paired t-tests result for AW/EW prediction using varying time window. 165
Figure 2.1. Schematic drawings of modified Lobsterbot paradigm. 50
Figure 2.2. Experimental schedule and behavioral alterations following Lobsterbot encounters. 51
Figure 2.3. Emergence of avoidance head withdrawal behavior following the encounter. 54
Figure 3.1. Schematics and photograph of the custom microdrive. 62
Figure 3.2. Reconstruction of the spatial tuning curve for correlation. 67
Figure 3.3. Computational graph of the dANN. 70
Figure 3.4. Resurgence and stabilization of approach behavior. 83
Figure 3.5. Data collection from single-unit data recording experiments. 86
Figure 3.6. Spatial tuning curve of representative PL/IL units. 87
Figure 3.7. Spatial tuning curves in PL/IL have vertical correlation. 88
Figure 3.8. Decoding distance using population activity from the PL and the IL 96
Figure 3.9. Sessions without Lobsterbot shows higher spatial decoding error. 101
Figure 3.10. Difference in decoding accuracy when a rat exhibits non-navigational behaviors. 103
Figure 3.11. Distinct functional states in the E-zone revealed by LDA. 107
Figure 3.12. Peri-Event Time Histogram (PETH) of the HE and HW and the unsupervised hierarchical clustering result. 112
Figure 3.13. Naïve Bayesian event classifier could decode AW/EW. 115
Figure 3.14. The mPFC encode feature-based abstracted space exclusive during strategic navigation. 125
Figure 3.15. The mPFC model for dynamic foraging behavior. 130
Supplementary Figure 1. Detailed hardware structure of the Lobsterbot apparatus. 155
Supplementary Figure 2. Comparison of the two types of dANN structures and representative training curve. 156
Supplementary Figure 3. Structure and training curve of the autoencoder. 157
Supplementary Figure 4. The rats showed uniformly distributed HW during 6-second trials and maintained similar level of motivation until the end of the session. 158
Supplementary Figure 5. Representative units which selectively increase activity in a specific distance range. 159
Supplementary Figure 6. Additional data on distance decoding error. 160
Supplementary Figure 7. HE1 and HE2 unit activity around run-and-stop events. 162
Supplementary Figure 8. PCA result shows similar result as in LDA. 163
Supplementary Figure 9. Correlation between HE groups and HW groups in unsupervised hierarchical clustering. 164