-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathresearch.html
65 lines (54 loc) · 3.46 KB
/
research.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
<!DOCTYPE html>
<html lang="en">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>Ru-Yuan Lab</title>
<link rel="stylesheet" href="./main.css">
</head>
<body>
<!--Header image part-->
<div class='header'>
<img class='headerimg' src='./img/background.jpg'>
</div>
<!--Navigation bar part---->
<div align='center'>
<nav>
<ul>
<li><a href='index.html'>Home</a></li>
<li><a class='active' href='research.html'>Research</a></li>
<li><a href='people.html'>People</a></li>
<li><a href='publications.html'>Publications</a></li>
<li><a href='resources.html'>Resources</a></li>
<li><a href='positions.html'>Positions</a></li>
</ul>
</nav>
</div>
<!-- Main Part -->
<ul class='research'>
<li>
<h3>Computational Visual Neuroscience</h3>
<p>
Human vision receives over 80% of sensory inputs to the brain. The neural and computational mechanisms of visual perception have been a central topic in cognitive neuroscience. My research questions along this line of research include: (1) the computation and representation of uncertainty in perceptual decision-making; (2) the influences of top-down modulation (e.g., attention/learning) on population codes in the human brain; (3) the neural implementations of Bayesian inference.
</p>
</li>
<li>
<h3>Deep Learning and Applications in Neuroscience</h3>
<p>
The past few years have seen the surge of comparisons between the artificial and the human brain. We see a promising future that research from machine learning and cognitive sciences can mutually benefit and foster the development of general intelligence. My research questions include (1) neural alignment vision-language models; (2) neural mechanisms of perceptual and cognitive learning.
</p>
</li>
<li>
<h3>Cognitive Learning and Decision-Making</h3>
<p>
The biological brain, as the most powerful intelligent agent, learns knowledge through thought, experience, and the senses. Decision-making, on the other hand, is a cognitive process that involves choosing between alternatives to achieve a desired outcome. Cognitive learning and decision-making are fundamental to understanding how humans think, learn, and behave. We are interested in uncovering how individuals perceive the world, learn to solve problems, and make decisions. Our current research in this direction includes: (1) the mechanisms of continual learning in humans and machines; (2) structure and parameter learning; (3) complex decision making and planning in a dynamic environment.
</p>
</li>
<li>
<h3>Computational Psychiatry</h3>
<p>
Computational Psychiatry is an interdiscipline that bridges basic computational neuroscience and translational psychiatry. Research in computational psychiatry emphasizes using the computational models established in normal subjects or basic neuroscience to characterize the mechanisms of abnormal cognitive behavior in psychiatric diseases. I am currently working on the following questions: (1) computational mechanisms of visual working memory deficits in patients with psychiatric diseases; (2) abnormal reinforcement learning in psychiatric diseases; (3) reward-based emotional regulation in psychiatric diseases.
</p>
</li>
</ul>
</body>
</html>