Research cleanup

HACKING.md

@@ -1,4 +1,13 @@
 Hacking on this site
 ====================
 
-Todo! Talk about how the parts interact (Flask, Frozen-Flask, MDPages, etc)
+This was the developers' first foray into web-stuff, so it's a little bit
+awkward.
+
+Basically, it pursues a model-view design. Where the Markdown files are the
+model, which get converter into views by Flask by filling out the HTML templates
+and parsing the Markdown files using `model.py`. The properties of the pages,
+such as which pages are to be included, can be found in `__init__.py`.
+
+This guide is far from complete, so if you have any more questions, don't
+hesitate to ask Trevor or Sean.

ctn_waterloo/__init__.py

@@ -8,6 +8,7 @@
 from .pages import FlatPages
 from .model import Model
 
+
 DEBUG = True
 SITE_NAME = "CNRGlab @ UWaterloo"
 FREEZER_BASE_URL = 'http://compneuro.uwaterloo.ca/'
@@ -152,7 +153,10 @@ def publications_page(citekey):
 def research_index():
     g.topic = 'research'
     page = pages.get('research_index')
-    page.topics = [model.research(topic) for topic in page['topics']]
+    categories = ('Theory', 'Applications', 'Tools')
+    page.categories = [{'category': cat, 'topics': model.research_categories(cat)}
+                       for cat in categories]
+    #page.topics = [model.research(topic) for topic in page['topics']]
     return render_template('research_index.html', page=page)
 
 

ctn_waterloo/content/research/cognition_index.md

@@ -1,43 +1,20 @@
 title: Cognition
 picture: http://i.imgur.com/DIDdp7P.jpg
+category: Applications
 intro: >
     Research into neural mechanisms behind many cognitive phenomena, 
     including working memory, syntactic generalization, structured representations, 
     associative memory, and more.
-people:
-    - Daniel Rasmussen
-toc:
-    - Cognitive Robotics
-    - Large-scale neural and cognitive simulation
-    - Modelling problem solving in Raven's Progressive Matrices
-    - - Demo
-      - Project Status
-      - Results
-    - Neural Cognitive Architecture
-    - Symbolic Reasoning in Spiking Neurons
 
-We have done work on working memory that some may
-consider cognitive, but we are now more focused on methods for building
-cognitive architectures in general. The architecture we have developed is
-called the [Semantic Pointer Architecture](/research/spa.html)
 
-This is a novel cognitive architecture that combines our
-interest in VSAs with the vector processing capabilities of the NEF. [Early
-work](236) focused on doing inferential symbolic processing in a biologically
-plausible, spiking neural network. This work demonstrates syntactic
-generalization (generalizing over syntactic structure despite sensitivity to
-semantic information) -- what has often be called a hallmark of cognitive
-function.
+We're all about relating that high level cognitive processes to neurons.
 
-We have now extended this work significantly in two ways: 1. we are addressing
-issues of scalability (which seems to be the major strength of not adopting a
-classical architecture); and 2. we are incorporating a [biologically plausible
-clean-up memory](15) (a kind of associative memory).
+We did Raven's.
 
-Details of our work on cognitive modelling are forthcoming in a currently
-submitted paper. For now you can read this [summary](202), which was written
-for a grant application to NSERC.
+We did counting strategies.
 
-You can also watch some demonstration videos related to this work, including
-[convolution/binding in neurons](229), [a neural production system](230), and
-[high-dimensional representation](228)
+We did word search stuff.
+
+We did planning.
+
+(Is there something else I'm forgetting? Can I put RL stuff here?)