#!/usr/bin/env python
# coding: utf-8

# # Up and Down the Ladder of Abstraction

# <img src="cog_ladder.svg" height="500" width="500">
# 
# Last week you saw Ivana talk about language processing. Most of her work focuses on the high level.
# 
# Next week Peter will talk to you about complex neuron models.
# 
# Today, I'm going to talk about my work at the top.

# # Fun at the top of the ladder
# ## Scaling up

# We know how to:
# 
# - Represesent symbols
# - Select actions using the Basal Ganglia and Thalamus
# - Save symbols in working memory and associative memory
# 
# We used these components to:
# 
# - Answer basic questions
# - Follow a sequence of rules
# 
# Now, let's try:
# 
# - Making decisions based on what's in memory

# Given a verb-noun pair from a limited vocabulary, execute the appropriate action.
# 
# - `verb` is `SAY` or `WRITE`
# - `noun` is `HELLO` or `GOODBYE`
# - `NONE` to indicate execute action

# In[1]:


vis_sequence = 'SAY HELLO NONE WRITE GOODBYE NONE GOODBYE SAY NONE'.split()

def input_vision(t):
    index = init(t / 0.5) % len(sequence)
    return sequence[index]


# In[2]:


import nengo
import nengo.spa as spa


# In[3]:


D = 64
model = spa.SPA()


# In[4]:


# See "Large-scale cognitive model design using the Nengo neural simulator" by Sughanda et al. for code
# http://compneuro.uwaterloo.ca/publications/sharma2016.html 


# In[ ]:


IPythonViz(model, "configs/verb_noun_follow.py.cfg")


# It gets more complicated with increasingly complicated behaviour. For example, the [task of counting](https://1drv.ms/p/s!Auhg6REoCX4GgWpkZHvmFBIN4FWV) that Spaun does requires [a LOT of lines of code](https://github.com/Seanny123/counting_to_addition/blob/master/counting_only.py).

# This also covers other models like:
# - N-back memory task [Gosmann et al. 2015](http://compneuro.uwaterloo.ca/publications/gosmann2015.html)
# - Stack-like procedure memory [Blouw et al. 2016](http://compneuro.uwaterloo.ca/publications/Blouw2016.html)
# - Language search model from last week [Kajic et al. 2017](http://compneuro.uwaterloo.ca/publications/kajic2017.html)

# If you want to integrate multiple complex behaviours, which Spaun actually does, you get an unmanageable amounts of code.
# 
# > "If someone wants to implement a new task into Spaun requiring a new module, they're better off reimplementing Spaun entirely than trying to edit the code." - Xuan Choo, creator of Spaun
# 
# That seems bad. How can fix this?
# 
# - Make the SPA action syntax better. See https://github.com/nengo/nengo_spa.
# - Make the design and simulate loop faster with neuromorphic hardware.
# - Make SPA actions require less fine-tuning. Sverrir is working on this!
# - Make a design language that compiles to SPA. But we don't know how to efficiently describe systems of this scale yet, because so few have been created!

# ## Getting better with practice

# Humans get better at a task if it's repeated. How can we include this in our networks?
# 
# <img src="full_counting_net.svg" height="500" width="500">

# This only works for basic input-output mapping. Further research needed for motor sequence chunking and other complex tasks.

# # Going back down the ladder
# ## Matching neurological data

# You have to use approximations:
# - MEG is basically spikes
# - fMRI is basically neurotransmitter use

# Does a Voja-based associative memory match MEG and fMRI data? No.

# What does? I'm working on it.

# # Bonus Pro Tips
# 
# Ideas for projects requiring refining and discussion:
# https://github.com/ctn-waterloo/modelling_ideas/issues
# 
# StackOverflow for cognitive modelling, psychology and neuroscience, good for getting summary of literature:
# https://psychology.stackexchange.com/help/on-topic