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Title Page

Abstract

Contents

1. Introduction 9

1.A. Backgrounds and motivations 9

1.B. Related works 10

1.C. Objectives 11

2. Scheme of neural coding 13

2.1. Rate coding and temporal coding 13

2.2. Spike distances: measures for rate coding and temporal coding 16

2.2.1. Spike distance focused on temporal coding 17

3. Principle of neural coding 41

3.1. Efficient coding and predictive coding 42

3.2. Spatio-temporally efficient coding 43

3.3. Implementation of spatio-temporally efficient coding 47

4. Spatio-temporally efficient coding: a case of static sensory input 53

4.A. Introduction 53

4.B. Methods For the simulations, v 53

4.C.1. Results: Decodable and rapidly stable neural representations 56

4.C.2. Results: Relation to homeostasis 59

4.C.3. Results: Deviant neural responses to unlearned inputs 60

4.C.4. Results: Preferred orientation biases of receptive fields 61

5. Spatio-temporally efficient coding: a case of dynamic sensory input 65

5.A. Introduction 65

5.B. Methods 65

5.C. Results: Consistent neural responses for static and dynamic sensory inputs 68

6. Discussion 71

References 76

List of Figures

Figure 1. Spike trains for grating stimuli. 14

Figure 2. Example of both rate coding and temporal coding. 15

Figure 3. Calculation of the spike distance based on the earth mover's distance. 20

Figure 4. Spike distance results for the measurement of spike timing differences. 23

Figure 5. Spike distance results for the measurement of temporal similarity. 26

Figure 6. Spike distance results for the measurement of spike time synchrony. 29

Figure 7. Comparison with the Victor-Purpura distance in terms of suitability for temporal coding with different firing rates. 32

Figure 8. Application of the spike distance to real neuronal data in the primary motor cortex in a non-human primate. 38

Figure 9. Application of the spike distance to resampled neuronal data. 40

Figure 10. Spatio-temporally efficient coding. 47

Figure 11. Implementation of spatio-temporally efficient coding. 52

Figure 12. A simulation method for static sensory input. 55

Figure 13. Decodable and stable neural representations. 59

Figure 14. Neural response distributions. 60

Figure 15. Neural response distributions for learned and unlearned inputs. 61

Figure 16. Orientation preference. 63

Figure 17. A simulation method for dynamic sensory input. 67

Figure 18. Distance between neural responses denoting consistent neural representations. 69

Figure 19. Decoding of bar stimuli denoting consistent neural representations. 70

초록보기

One of the major goals of neuroscience is to understand how the external world is represented in the brain. This is a neural coding problem: the coding from the external world to its neural representations. There are two different kinds of problems with neural coding. One is to study the types of neuronal activity that represent the external world. Representative examples here are rate coding and temporal coding. In this study, we will present the spike distance method that reads temporal coding-related information from neural data. Another is to study what principles make such neural representations possible. This is an approach to the computational principle and the main topic of the present study. The brain sensory system has hierarchical structures. It is important to find the principles assigning functions to the hierarchical structures. On the one hand, the hierarchical structures of the brain sensory system contain both bottom-up and top-down pathways. In this bidirectional hierarchical structure, two types of neuronal noise are generated. One of them is noise generated as neural information fluctuates across the hierarchy according to the initial condition of the neural response, even if the external sensory input is static. Another is noise, precisely error, caused by coding different information in each hierarchy because of the transmission delay of information when external sensory input is dynamic. Despite these noise problems, it seems that sensory information processing is performed without any major problems in the sensory system of the real brain. Therefore, a neural coding principle that can overcome these noise problems is needed; How can the brain overcome these noise problems? Efficient coding is one of representative neural coding principles, however, existing efficient coding does not take into account these noise problems. To treat these noise problems, as one of efficient coding principles, we devised spatio-temporal efficient coding, which was inspired by the efficient use of given space and time resources, to optimize bidirectional information transmission on the hierarchical structures. This optimization is to learn smooth neural responses on time domain. In simulations, we showed spatio-temporal efficient coding was able to solve above two noise problems. We expect that spatio-temporal efficient coding helps us to understand how the brain computes.

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