#!/usr/bin/env python
# coding: utf-8
# 
# # Introduction
#
# Welcome to the Neuromatch computational neuroscience course!
# 
# ## Welcome Video
# In[ ]:
# @markdown
from ipywidgets import widgets
out2 = widgets.Output()
with out2:
from IPython.display import IFrame
class BiliVideo(IFrame):
def __init__(self, id, page=1, width=400, height=300, **kwargs):
self.id=id
src = 'https://player.bilibili.com/player.html?bvid={0}&page={1}'.format(id, page)
super(BiliVideo, self).__init__(src, width, height, **kwargs)
video = BiliVideo(id="BV12a411p7Zj", width=854, height=480, fs=1)
print('Video available at https://www.bilibili.com/video/{0}'.format(video.id))
display(video)
out1 = widgets.Output()
with out1:
from IPython.display import YouTubeVideo
video = YouTubeVideo(id="KRSomVVqYwI", width=854, height=480, fs=1, rel=0)
print('Video available at https://youtube.com/watch?v=' + video.id)
display(video)
out = widgets.Tab([out1, out2])
out.set_title(0, 'Youtube')
out.set_title(1, 'Bilibili')
display(out)
# ## Concepts map
#
# 
#
# *Image made by John Butler, with expert color advice from Isabelle Butler*
# ## Curriculum overview
#
# Welcome to the comp neuro course!
#
# We have curated a curriculum that spans most areas of computational neuroscience (a hard task in an increasingly big field!).
# We will expose you to both theoretical modeling and more data-driven analyses. This section will overview the curriculum.
#
# We will start with several optional pre-reqs refreshers.
#
# The **Neuro Video Series** is a series of 12 videos that covers basic neuroscience concepts and neuroscience methods. These videos
# are completely optional and do not need to be watched in a fixed order so you can pick and choose which videos will help you
# brush up on your knowledge.
#
# The pre-reqs refresher days are asynchronous, so you can go through the material on your own time.
# You will learn how to code in Python from scratch using a simple neural model, the leaky integrate-and-fire model, as a motivation.
# Then, you will cover linear algebra, calculus and probability & statistics.
# The topics covered on these days were carefully chosen based on what you need for the comp neuro course.
#
# After this, it’s the start of the proper course! You will start out with the module on **Intro to Modeling**. On the first day,
# you’ll learn all about the broad types of questions we can ask with models in neuroscience (*Model Types*). You will learn
# that we can use models to ask what happens, how it happens, and why it happens in the brain. Importantly, we classify models
# into ‘what’, ‘how’, and ‘why’ models, not based on the toolkit used, but on the questions asked!
#
# After this solid grounding in what questions you can ask with models and the process to start doing so, you will move to the module on **Machine Learning**.
# This module covers fitting models to data and using them to ask and answer questions in neuroscience. We can pose all sorts of questions
# (including what, how, and why questions) using machine learning — we focus especially on more data-driven analyses that often result
# in asking what is happening in the brain. You will learn about key principles behind fitting models to data (*Model Fitting*),
# how to use generalized linear models to fit encoding and decoding models (*Generalized Linear Models*), how to uncover underlying
# lower dimensional structure in data (*Dimensionality Reduction*), and how to use deep learning to build more complex encoding models,
# including comparing deep networks to the visual system (*Deep Learning*). You’ll then dive into projects and
# learn about the process of modeling using a step-by-step guide to modeling that you will apply to your own projects (*Modeling Practice*).
#
# Next, you’ll move to the module on **Dynamical Systems**. In this module, you will learn all about dynamical systems and
# how to apply them to build more biologically plausible models of neurons and networks of neurons. In *Linear Systems*,
# you will cover a lot of the really foundational knowledge on dynamical systems that you will use throughout the rest of the course,
# including a brief dive into stochastic systems which will underlie the next module. During *Biological Neuron Models*,
# you will start to use this knowledge to build models of individual neurons that are more rooted in biology. In *Dynamic Networks*,
# you will extend upon the previous day to start building and analyzing networks of neurons. We will often ask ‘how’ questions
# using these models: how are things in the brain happening mechanistically? These models are often not directly fit to data
# (in contrast to the machine learning models), but instead are built based on bottom up knowledge of the system.
#
# Next, you will move to the module on **Stochastic Processes**. We start with a day learning about Bayesian inference, within the context
# of making decisions (*Bayesian Decisions*). Specifically, we are learning about how to estimate a state of the world from measurements.
# In the next day, we extend this to include time: the state of the world is now changing over time (*Hidden Dynamics*). Next, we look
# at how we can take actions to affect the state of the world (*Optimal Control* and *Reinforcement Learning*). Once again, these models
# can be used as ‘what’, ‘how’, or ‘why’ models but we focus on asking ‘why’ questions (why should the brain compute this?).
#
# Finally, we end with learning all about causality (*Network Causality*). This covers one of the most important science questions:
# when can we determine if something is causally related vs. just correlated?