---
# System prepended metadata

title: 'Improved inference in coupling, encoding, and decoding models and its consequence for neuroscientific interpretation'
tags: [論文摘要, 機器學習]

---

# Improved inference in coupling, encoding, and decoding models and its consequence for neuroscientific interpretation

###### tags: `論文摘要` `機器學習` 

- Link: https://doi.org/10.1016/j.jneumeth.2021.109195
- MLA: Sachdeva, Pratik S., et al. "Improved inference in coupling, encoding, and decoding models and its consequence for neuroscientific interpretation." Journal of Neuroscience Methods 358 (2021): 109195.
- 年分: 2021
- 期刊: Journal of Neuroscience Methods


## 目標

透過提出新方法來更好的了解神經之間的關聯性

## 背景

- 在神經科學中，了解神經和各種刺激之間的關係是一件重要的事

    > These issues are particularly salient in systems neuroscience, where parametric models are often used to understand how neural activity is modulated by external factors (e.g., stimuli or a behavioral task) and internal factors (e.g., other neurons).

- 神經科學中常見的3種模型
    - functional coupling model
    神經與神經之間的關係
    - encoding model
    外在刺激對應神經的關係
    - decoding model
    神經對應外在刺激的關係

    ![](https://i.imgur.com/xblQjvg.png)


- 模型是用來觀察結論的一種方式

    > Since these models are used to make scientific conclusions about the function of the brain, understanding the stability, accuracy, and parsimony of the inference procedures and resulting models is of paramount importance.

- 因為模型的正確率並不能直接表示他所選擇的特徵是正確的(指weight的大小是不是和重要性有關這件事)，因此需要方法提升這些連接(weight)的正確性和可解釋性。
- 一個可解釋的模型注重
    - 稀疏性
    - 穩定性

## 方法簡述

使用UoI架構去讓模型中的參數變得稀疏，可以幫助作者知道哪些特徵是有用的，而哪些是無用的

![](https://i.imgur.com/GOoj79C.png)



## 結果

1.  UoI相較於baseline method(LASSO)可以降低進出節點的連線數(使模型的參數變得集中)
    - In-Degree
    進入節點的連線數
    - Out-Degre
    離開節點的連線數

    ![](https://i.imgur.com/TLLBWve.png)

    UoI為橘色 / baseline為灰色

2.  在auditory cortex中channel之間的連接關係
    - 將節點排列好之後將他們之間的weight畫出來

    ![](https://i.imgur.com/74YVkN8.jpg)


    freq是聲音的頻率