亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Lateral Connections Improve Generalizability of Learning in a Simple Neural Network

人工神经网络 生物神经网络 计算机科学 过度拟合 赢家通吃 人工智能 电突触 缝隙连接 机器学习 生物 细胞内 细胞生物学
作者
Garrett W. Crutcher
出处
期刊:Neural Computation [The MIT Press]
卷期号:36 (4): 705-717
标识
DOI:10.1162/neco_a_01640
摘要

Abstract To navigate the world around us, neural circuits rapidly adapt to their environment learning generalizable strategies to decode information. When modeling these learning strategies, network models find the optimal solution to satisfy one task condition but fail when introduced to a novel task or even a different stimulus in the same space. In the experiments described in this letter, I investigate the role of lateral gap junctions in learning generalizable strategies to process information. Lateral gap junctions are formed by connexin proteins creating an open pore that allows for direct electrical signaling between two neurons. During neural development, the rate of gap junctions is high, and daughter cells that share similar tuning properties are more likely to be connected by these junctions. Gap junctions are highly plastic and get heavily pruned throughout development. I hypothesize that they mediate generalized learning by imprinting the weighting structure within a layer to avoid overfitting to one task condition. To test this hypothesis, I implemented a feedforward probabilistic neural network mimicking a cortical fast spiking neuron circuit that is heavily involved in movement. Many of these cells are tuned to speeds that I used as the input stimulus for the network to estimate. When training this network using a delta learning rule, both a laterally connected network and an unconnected network can estimate a single speed. However, when asking the network to estimate two or more speeds, alternated in training, an unconnected network either cannot learn speed or optimizes to a singular speed, while the laterally connected network learns the generalizable strategy and can estimate both speeds. These results suggest that lateral gap junctions between neurons enable generalized learning, which may help explain learning differences across life span.

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
迷茫的一代完成签到,获得积分10
10秒前
36秒前
梦想家发布了新的文献求助10
41秒前
熊啊发布了新的文献求助10
1分钟前
1分钟前
Virtual应助科研通管家采纳,获得20
1分钟前
小周完成签到 ,获得积分10
1分钟前
2分钟前
梦想家完成签到,获得积分10
2分钟前
2分钟前
story发布了新的文献求助10
2分钟前
科研通AI2S应助story采纳,获得10
3分钟前
3分钟前
鉴定为学计算学的完成签到,获得积分10
3分钟前
熊啊发布了新的文献求助10
3分钟前
Kevin完成签到,获得积分10
4分钟前
sci2025opt完成签到 ,获得积分10
4分钟前
4分钟前
李健应助鸡蛋黄采纳,获得10
4分钟前
4分钟前
wujiwuhui完成签到 ,获得积分10
5分钟前
5分钟前
鸡蛋黄发布了新的文献求助10
5分钟前
完美世界应助眼睛大智宸采纳,获得10
5分钟前
市政的艺术家完成签到,获得积分10
5分钟前
Virtual应助科研通管家采纳,获得20
5分钟前
JamesPei应助市政的艺术家采纳,获得20
5分钟前
lod完成签到,获得积分10
5分钟前
6分钟前
淡淡醉波wuliao完成签到 ,获得积分0
6分钟前
可可完成签到 ,获得积分10
6分钟前
7分钟前
7分钟前
熊啊发布了新的文献求助10
7分钟前
lj发布了新的文献求助10
7分钟前
Ava应助krajicek采纳,获得10
7分钟前
NexusExplorer应助熊啊采纳,获得10
7分钟前
lj完成签到,获得积分10
7分钟前
7分钟前
krajicek发布了新的文献求助10
7分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Manipulating the Mouse Embryo: A Laboratory Manual, Fourth Edition 1000
INQUIRY-BASED PEDAGOGY TO SUPPORT STEM LEARNING AND 21ST CENTURY SKILLS: PREPARING NEW TEACHERS TO IMPLEMENT PROJECT AND PROBLEM-BASED LEARNING 500
Founding Fathers The Shaping of America 500
Distinct Aggregation Behaviors and Rheological Responses of Two Terminally Functionalized Polyisoprenes with Different Quadruple Hydrogen Bonding Motifs 460
Writing to the Rhythm of Labor Cultural Politics of the Chinese Revolution, 1942–1976 300
Lightning Wires: The Telegraph and China's Technological Modernization, 1860-1890 250
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 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 催化作用 遗传学 冶金 电极 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 4568949
求助须知:如何正确求助?哪些是违规求助? 3991291
关于积分的说明 12355635
捐赠科研通 3663460
什么是DOI,文献DOI怎么找? 2018921
邀请新用户注册赠送积分活动 1053332
科研通“疑难数据库(出版商)”最低求助积分说明 940877