The topology and geometry of neural representations

拓扑(电路) 计算机科学 代表(政治) 相似性(几何) 稳健性(进化) 人口 人工智能 人工神经网络 模式识别(心理学) 数学 图像(数学) 社会学 人口学 组合数学 基因 政治 化学 法学 生物化学 政治学
作者
Baihan Lin,Nikolaus Kriegeskorte
出处
期刊:Proceedings of the National Academy of Sciences of the United States of America [National Academy of Sciences]
卷期号:121 (42) 被引量:3
标识
DOI:10.1073/pnas.2317881121
摘要

A central question for neuroscience is how to characterize brain representations of perceptual and cognitive content. An ideal characterization should distinguish different functional regions with robustness to noise and idiosyncrasies of individual brains that do not correspond to computational differences. Previous studies have characterized brain representations by their representational geometry, which is defined by the representational dissimilarity matrix (RDM), a summary statistic that abstracts from the roles of individual neurons (or responses channels) and characterizes the discriminability of stimuli. Here, we explore a further step of abstraction: from the geometry to the topology of brain representations. We propose topological representational similarity analysis, an extension of representational similarity analysis that uses a family of geotopological summary statistics that generalizes the RDM to characterize the topology while de-emphasizing the geometry. We evaluate this family of statistics in terms of the sensitivity and specificity for model selection using both simulations and functional MRI (fMRI) data. In the simulations, the ground truth is a data-generating layer representation in a neural network model and the models are the same and other layers in different model instances (trained from different random seeds). In fMRI, the ground truth is a visual area and the models are the same and other areas measured in different subjects. Results show that topology-sensitive characterizations of population codes are robust to noise and interindividual variability and maintain excellent sensitivity to the unique representational signatures of different neural network layers and brain regions.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Jasper应助北城采纳,获得10
刚刚
A阿澍发布了新的文献求助10
刚刚
顺利煎蛋完成签到,获得积分10
刚刚
肖肖发布了新的文献求助10
1秒前
chengmin完成签到 ,获得积分10
1秒前
wei发布了新的文献求助50
1秒前
2秒前
3秒前
sunwen发布了新的文献求助10
3秒前
4秒前
5秒前
北城完成签到,获得积分10
6秒前
十三完成签到 ,获得积分10
7秒前
打打应助傲寒采纳,获得10
7秒前
小李吃小孩完成签到,获得积分10
7秒前
含蓄大雁完成签到,获得积分10
7秒前
8秒前
Livrik发布了新的文献求助10
9秒前
卢敏明发布了新的文献求助10
9秒前
李健应助俏皮的白柏采纳,获得10
10秒前
10秒前
很好关注了科研通微信公众号
11秒前
11秒前
12秒前
研友_VZG7GZ应助九月采纳,获得10
13秒前
TTm关注了科研通微信公众号
13秒前
14秒前
14秒前
顾矜应助lixiaolu采纳,获得10
15秒前
liu发布了新的文献求助10
15秒前
16秒前
Orange应助光亮嵩采纳,获得10
16秒前
17秒前
19秒前
ANmin发布了新的文献求助10
19秒前
20秒前
22秒前
22秒前
23秒前
24秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988868
求助须知:如何正确求助?哪些是违规求助? 3531255
关于积分的说明 11253071
捐赠科研通 3269858
什么是DOI,文献DOI怎么找? 1804822
邀请新用户注册赠送积分活动 881994
科研通“疑难数据库(出版商)”最低求助积分说明 809035