郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! Trends in Neurosciences, no. 5 (2010): 220-229 Abstract 回合空间记忆均涉及海马体神经元回路中复杂联想的编码.这种记忆轨迹可以通过巩固过程从短期到长期稳定下来,这种巩固过程涉及睡眠和休息期间原始网络发放模式的“重新激活”.清醒经验能够被许多不同的大脑区域回放,但海马体的重要作用在“重新激活”过程的组织中.越来越多的证据表明,海马体中的尖波/波纹(SWR)事件可以协调记忆轨迹的重新激…

一.Play it again: reactivation of waking experience and memory(Trends in Neurosciences 2010) SWR发放模式不仅反映了环境,而且反映了行为,这进一步表明来自以下事实:在以后的睡眠中,访问频率较高的地方会更强烈地重新激活.结果表明,在随后的睡眠过程中,编码特定位置的细胞的发放同步性随着在先前探索期间在该位置花费的时间而增加.因此,重新激活的模式偏向访问量最大的地方. 总之,这些发现表明,与探索有关的发放模式在…

Recently we looked across some of the most common behaviors that our community of 25,000 users looked for in their logs with a particular focus on web server logs. In fact our research identified the top 15 web server tags and alerts created by our c…

I used to be lazy and wrote no blogs. I used to live at leisure and wasted opportunity. Time flies, experience gains, memory loses. Maybe it's time to change, it's time to take notes. Tip of the iceberg, still better than vacant. 曾经很懒,不愿搦管. 曾经很闲,磨了时间…

https://www.ted.com/talks/robert_waldinger_what_makes_a_good_life_lessons_from_the_longest_study_on_happiness/transcript 00:12What keeps us healthy and happy as we go through life? If you were going to invest now in your future best self, where would…

https://blog.csdn.net/Young_Gy/article/details/73485518 强化学习在alphago中大放异彩,本文将简要介绍强化学习的一种q-learning.先从最简单的q-table下手,然后针对state过多的问题引入q-network,最后通过两个例子加深对q-learning的理解. 强化学习 Q-learning Q-Table Bellman Equation 算法 实例 Deep-Q-learning Experience replay Ex…

从这里开始换个游戏演示,cartpole游戏 Deep Q Network 实例代码 import sys import gym import pylab import random import numpy as np from collections import deque from keras.layers import Dense from keras.optimizers import Adam from keras.models import Sequential EPISODES…

Policy-Based methods 在上篇文章中介绍的Deep Q-Learning算法属于基于价值(Value-Based)的方法,即估计最优的action-value function $q_*(s,a)$,再从$q_*(s,a)$中导出最优的策略$\pi_*$(e.g., $\epsilon$-greedy).但是有没有方法能不经过中间过程,直接对最优策略进行估计呢?这样做又有什么好处呢?该部分要介绍的就是这类方法,即基于策略(Policy-Based)的方法.下面先介绍一下这类方法…

在前两篇文章强化学习基础:基本概念和动态规划和强化学习基础:蒙特卡罗和时序差分中介绍的强化学习的三种经典方法(动态规划.蒙特卡罗以及时序差分)适用于有限的状态集合$\mathcal{S}$,以时序差分中的Q-Learning算法为例,一般来说使用n行(n = number of states)和m列(m= number of actions)的矩阵(Q table)来储存action-value function的值,如下图所示: 对于连续的状态集合$\mathcal{S}$,上述方法就不能适用…

Pramp - mock interview experience   February 23, 2016 Read the article today from hackerRank blog on facebook: http://venturebeat.com/2016/02/18/how-i-landed-a-google-internship-in-6-months/?utm_content=buffer5c137&utm_medium=social&utm_source=fac…