【COMP305 LEC 3 LEC 4】

LEC 3 A basic abstract model for a biological neuron

1. Weights of connections

Neuron gets fired if it has received from the presynaptic neurons 突触前神经元 a summary impulse 脉冲, which is above a certain threshold.

Signal from a single synapse突触 may sometime overcome the threshold and push a neuron to fire an action potential, but other synapses can achieve this only by simultaneously delivering their signals: Some inputs are more important!

定义:

Therefore, input from every synapse, or "connection", to the neuron in the abstract model must be assigned with some value w, called connection strength or weight of connection, to describe the importance of a connection.

2. Model

  1. The abstract neuron is excited when weighted sum is above the threshold 0

vs.

The biological neuron is excited when the signal density (spatial or temporal summation) is above the excitation potential threshold.

  1. Output is either 1 or 0.

vs.

Only the spikes(峰值)are remembered

LEC 4

Topic 2. The McCulloch-Pitts Neuron (1943)

1. McCulloch and Pitts demonstrated that

"...because of the all-or-none character of nervous activity, neural events and the relations among them can be treated by means of the propositional logic".

  1. The authors modelled the neuron as

a. a binary, discrete-time input

b. discrete-time:

The basic idea was to divide time into units, i.e., steps, and in each time period at most one spike can be initiated in the axon of a given neuron

将时间分成单位和步骤,每个时间一个神经元的轴突最多产生一次峰值

uniform velocity 脉冲基本都以匀速传播

Thus, the McCulloch-Pitts neuron operates on a discrete time scale,

t = 0,1,2,3, ...

c. binary input:

The types of the input and the output of a MP neuron are thus unified.

d. with excitatory and inhibitory connections 有着兴奋和抑制之间的联系 and an excitation threshold. 兴奋阙值

The network of such elements was the first model to tie the study of neural networks to the idea of computation in its modern sense.

将神经网络和现代意义上的计算思想联系起来

e. with excitatory and inhibitory connections 有着兴奋和抑制之间的联系 and an excitation threshold. 兴奋阙值

f. The network of such elements was the first model to tie the study of neural networks to the idea of computation in its modern sense. 将神经网络和现代意义上的计算思想联系起来

g. excitatory and inhibitory connections :

The weight of connection wi are:

+1 for excitatory type connection and 加一促进

Cerebral pyramidal cell:

-1 for inhibitory type connection. 减一抑制

h. Threshold

I. MP Neuron

In the MP neuron, we call the instant total input

St-1: instant stateof the neuron

j. Actication Function

相关推荐
@小博的博客3 小时前
【Linux探索学习】第二篇Linux的基本指令(2)——开启Linux学习第二篇
linux·运维·学习
007php0075 小时前
某大厂跳动面试:计算机网络相关问题解析与总结
java·开发语言·学习·计算机网络·mysql·面试·职场和发展
知识分享小能手5 小时前
微信小程序入门学习教程,从入门到精通,微信小程序核心 API 详解与案例(13)
前端·javascript·学习·react.js·微信小程序·小程序·vue
递归不收敛5 小时前
吴恩达机器学习课程(PyTorch 适配)学习笔记:3.3 推荐系统全面解析
pytorch·学习·机器学习
又是忙碌的一天7 小时前
前端学习 JavaScript(2)
前端·javascript·学习
蒙奇D索大7 小时前
【数据结构】考研数据结构核心考点:二叉排序树(BST)全方位详解与代码实现
数据结构·笔记·学习·考研·算法·改行学it
玲娜贝儿--努力学习买大鸡腿版8 小时前
推荐算法学习笔记(十九)阿里SIM 模型
笔记·学习·推荐算法
光影少年8 小时前
Flutter生态及学习路线
学习·flutter
尤利乌斯.X8 小时前
复杂网络仿真从入门到精通:0 学习路线
网络·学习·matlab·仿真·复杂网络
梦幻精灵_cq8 小时前
70行代码展现我的“毕生”编程能力
学习