diff --git a/.gitignore b/.gitignore
index 74ecc19..94587ed 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,5 +4,6 @@
.DS_Store
build/
+_build/
data/
reference_code/
diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 0000000..3263f2e
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,17 @@
+language: python
+python: 3.6
+
+install:
+ - pip install -r docs/requirements.txt
+
+script:
+ - sphinx-build -W docs docs/_build
+
+after_success:
+ - if [[ $TRAVIS_BRANCH == 'master' ]]; then
+ export DATE=$(date '+%Y-%m-%d %T');
+ git config --global user.email "travis@travis-ci.org";
+ git config --global user.name "TravisCI";
+ ghp-import -m "Last update at $DATE" -b gh-pages docs/_build;
+ git push -fq "https://$GH_TOKEN@github.com/xchoo/spaun2.0.git" gh-pages;
+ fi
diff --git a/README b/README
deleted file mode 100644
index 2c38f11..0000000
--- a/README
+++ /dev/null
@@ -1,32 +0,0 @@
-Spaun2.0
-========
-
-Running the Spaun [1]_ model in Nengo 2.0.
-
-.. [1] Chris Eliasmith, Terrence C. Stewart, Xuan Choo, Trevor Bekolay,
- Travis DeWolf, Yichuan Tang, and Daniel Rasmussen. A large-scale model
- of the functioning brain. Science, 338:1202-1205, 2012.
- URL: http://nengo.ca/publications/spaunsciencepaper
- doi:10.1126/science.1225266.
-
-
-Running with Nengo OCL
-----------------------
-
-If you want to run with ``nengo_ocl``:
-
- python run_spaun.py -d 512 --ocl --ocl_platform=1 --ocl_device=3
-
-where:
- - the -d flag sets the dimensionality of spaun,
- - the --ocl flag tells the run script to use ocl
- - the --ocl_platform flag tells it what OCL platform to use
- - the --ocl_device flag tells it what ocl device to use on said platform
- (this flag is optional, it's used in the context creation for pyopencl)
-
-To determine the ``ocl_platform`` and ``ocl_device`` of the device you want to
-use, see ``pyopencl.create_some_context()``.
-
-To enable OCL profiling, find where the ``nengo_ocl.Simulator`` is created
-in ``run_spaun.py``, and uncomment the version that has provifiling enabled.
-Also uncomment the line to print profiling.
diff --git a/README.rst b/README.rst
new file mode 100644
index 0000000..143c5f2
--- /dev/null
+++ b/README.rst
@@ -0,0 +1,62 @@
+*****
+Spaun
+*****
+
+Spaun is currently the world's largest functional brain model.
+It is the main focus of Chapter 7 of `How to Build a Brain
+`_
+by Chris Eliasmith.
+Spaun first appeared in Science;
+these documents also serve as a home
+to supporting material for the original paper.
+
+The Spaun model consists of about 2.5 million spiking neurons.
+It has a single eye and an arm.
+All input is raw images shown to the eye,
+and all output is arm movements,
+controlled directly by the brain.
+
+.. topic:: Spaun performing several tasks
+
+ .. raw:: html
+
+
+
+This project contains the Spaun [1]_ model,
+updated for Nengo 2.0.
+
+.. [1] Chris Eliasmith, Terrence C. Stewart, Xuan Choo, Trevor Bekolay,
+ Travis DeWolf, Yichuan Tang, and Daniel Rasmussen. A large-scale model
+ of the functioning brain. Science, 338:1202-1205, 2012.
+ doi:10.1126/science.1225266.
+
+Running with Nengo OCL
+======================
+
+If you want to run with ``nengo_ocl``::
+
+ python run_spaun.py -d 512 --ocl --ocl_platform=1 --ocl_device=3
+
+where:
+
+- the ``-d`` flag sets the dimensionality of Spaun,
+- the ``--ocl`` flag tells the run script to use ``nengo_ocl``
+- the --ocl_platform flag tells it what OCL platform to use
+- the --ocl_device flag tells it what ocl device to use on said platform
+ (this flag is optional, it's used in the context creation for pyopencl)
+
+To determine the ``ocl_platform`` and ``ocl_device`` of the device you want to
+use, see ``pyopencl.create_some_context()``.
+
+To enable OCL profiling, find where the ``nengo_ocl.Simulator`` is created
+in ``run_spaun.py``, and uncomment the version that has provifiling enabled.
+Also uncomment the line to print profiling.
+
+Resources
+=========
+
+.. toctree::
+ :maxdepth: 2
+
+ videos
+ press
diff --git a/docs/_static/chris-spaun.jpg b/docs/_static/chris-spaun.jpg
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diff --git a/docs/_static/custom.css b/docs/_static/custom.css
new file mode 100644
index 0000000..4dd1275
--- /dev/null
+++ b/docs/_static/custom.css
@@ -0,0 +1,40 @@
+.MathJax .mi, .MathJax .mo {
+ color: inherit;
+}
+
+.container.docutils {
+ margin: inherit;
+ padding: inherit;
+ width: inherit;
+}
+
+.container.docutils.toggle {
+ margin: 0;
+ padding: 0;
+}
+
+.toggle .header {
+ clear: both;
+ cursor: pointer;
+ display: block;
+}
+
+.toggle .header::after {
+ content: " ▼";
+ display: inline;
+}
+
+.toggle .header.open::after {
+ content: " ▲";
+ display: inline;
+}
+
+img.float-right {
+ float: right;
+ margin-left: 6px;
+ margin-bottom: 6px;
+}
+
+img.flag {
+ margin-bottom: 3px;
+}
diff --git a/docs/_static/flag-brazil.png b/docs/_static/flag-brazil.png
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diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html
new file mode 100644
index 0000000..c7e67b4
--- /dev/null
+++ b/docs/_templates/layout.html
@@ -0,0 +1,22 @@
+{# Import the theme's layout. #}
+{% extends "!layout.html" %}
+
+{%- block extrahead %}
+
+{# Call the parent block #}
+{{ super() }}
+{%- endblock %}
+
+{%- block footer %}
+
+{{ super() }}
+{%- endblock %}
diff --git a/docs/conf.py b/docs/conf.py
new file mode 100755
index 0000000..28d5718
--- /dev/null
+++ b/docs/conf.py
@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+
+from datetime import datetime
+import os
+import sys
+
+import guzzle_sphinx_theme
+
+sys.path.extend(os.path.dirname(__file__))
+extensions = [
+ "sphinx.ext.githubpages",
+ "sphinx.ext.intersphinx",
+ "sphinx.ext.mathjax",
+ "sphinx.ext.todo",
+ "guzzle_sphinx_theme",
+]
+
+suppress_warnings = ['image.nonlocal_uri']
+source_suffix = ".rst"
+master_doc = "index"
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
+
+project = "Spaun"
+copyright = "2015-2017, Applied Brain Research"
+author = "Applied Brain Research"
+version = release = datetime.now().strftime("%Y-%m-%d")
+language = None
+
+todo_include_todos = True
+
+intersphinx_mapping = {
+ "nengo": ("https://www.nengo.ai/nengo", None)
+}
+
+# HTML theming
+pygments_style = "sphinx"
+templates_path = ["_templates"]
+html_static_path = ["_static"]
+
+html_theme_path = guzzle_sphinx_theme.html_theme_path()
+html_theme = "guzzle_sphinx_theme"
+
+html_theme_options = {
+ "project_nav_name": project,
+ "base_url": "https://xchoo.github.io/spaun",
+}
+
+# Other builders
+htmlhelp_basename = project
+
+latex_elements = {
+ # "papersize": "letterpaper",
+ # "pointsize": "11pt",
+ # "preamble": "",
+ # "figure_align": "htbp",
+}
+
+latex_documents = [
+ (master_doc, # source start file
+ "%s.tex" % project.lower(), # target name
+ "%s Documentation" % project, # title
+ author, # author
+ "manual"), # documentclass
+]
+
+man_pages = [
+ # (source start file, name, description, authors, manual section).
+ (master_doc, project.lower(), "%s Documentation" % project, [author], 1)
+]
+
+texinfo_documents = [
+ (master_doc, # source start file
+ project, # target name
+ "%s Documentation" % project, # title
+ author, # author
+ project, # dir menu entry
+ "Spaun", # description
+ "Miscellaneous"), # category
+]
diff --git a/docs/index.rst b/docs/index.rst
new file mode 100644
index 0000000..72a3355
--- /dev/null
+++ b/docs/index.rst
@@ -0,0 +1 @@
+.. include:: ../README.rst
diff --git a/docs/press.rst b/docs/press.rst
new file mode 100644
index 0000000..747150b
--- /dev/null
+++ b/docs/press.rst
@@ -0,0 +1,194 @@
+*************
+Popular press
+*************
+
+|cartoon|
+After the publication of the Science paper,
+Spaun was widely covered by the popular press.
+Below is a partial collection of those articles.
+
+* `CBC Quirks & Quarks
+ `__
+ (radio interview) Building a Brain
+* |gb| `BBC World: Science in Action
+ `_
+ (radio interview; opening 5 s and interview starts at 11:50)
+* `CNN
+ `_:
+ Brain model may help build human-like robot
+* |gb| `Wired `_:
+ How to build an artificial brain
+* `Popular Science
+ `_:
+ Meet Spaun, The Most Complex Simulated Brain Ever
+* `Nature
+ `_:
+ Simulated brain scores top test marks
+* `Reddit post
+ `_
+* `Reddit Ask Me Anything (AMA)
+ `_ (ranked in the `top 10 Science AMAs for 2012
+ `_)
+* `National Geographic
+ `_:
+ Will we ever... simulate a brain?
+* `LA Times
+ `_:
+ New computer model of the brain focuses on seeing and doing
+* `BrainFacts.org
+ `_:
+ Designing brain-based robots
+* `CBC Quirks & Quarks
+ `__:
+ (radio interview) What does the future hold?
+ Artificial intelligence and the singularity
+* `Science News
+ `_:
+ Simulated brain mimics human quirks
+* `Seeker
+ `_:
+ Artificial brain mimics human abilities and flaws
+* `Smithsonian.com
+ `_:
+ A More Human Artificial Brain
+* `SingularityHub
+ `_:
+ Scientists create artificial brain with 2.3 million simulated neurons
+* `TG Daily
+ `_:
+ Artificial brain 'Spaun' passes IQ tests
+* `Slashdot post
+ `_:
+ Spaun: a large-scale functional brain model
+* `Ars Technica
+ `_:
+ Model brain with 2.5 million neurons configures itself to solve problems
+* `Huffington Post
+ `_:
+ Artificial Brain: 'Spaun' Software Model Mimics Abilities, Flaws Of Human Brain
+* `Gizmodo
+ `_:
+ The Most Realistic Artificial Brain Has a Mind of Its Own
+* `ExtremeTech
+ `_:
+ Spaun, the most realistic artificial human brain yet
+* `VR Zone
+ `_:
+ Neuroscientist creates a large scale model of a functioning brain
+* `Canada.com
+ `_:
+ Canadian scientists create a functioning, virtual brain
+* `Science Recorder
+ `_:
+ Researchers create the world’s largest working brain model
+* `The Globe and Mail
+ `_:
+ Meet Spaun: the world’s biggest brain that works
+* `Les Affaires
+ `_:
+ Des ordinateurs qui imitent notre cerveau
+* `CTV News
+ `_
+ (video interview) Scientists in Waterloo, Ont. develop virtual brain
+* `Singularity Weblog
+ `_:
+ (video interview) Chris Eliasmith on Singularity 1 on 1:
+ We Have Not Yet Learned What The Brain Has To Teach Us!
+* `McGill Daily
+ `_:
+ The rise of the brain-bots
+* `The Record (Kitchener)
+ `_:
+ Brain simulator could provide insight to damage, diseases, local researchers say
+* `The Register
+ `_:
+ Scientists build largest ever computerized brain
+* |de| `Der Spiegel
+ `_:
+ Bereit für den Intelligenztest
+* `CBC Radio Canada
+ `_:
+ Spaun, le cerveau artificiel
+* |de| `n-tv.de
+ `_:
+ Maschine ahmt Mensch nach
+* |br| `Folha de S. Paulo
+ `_:
+ Cérebro virtual simula comportamento e realiza tarefas cognitivas
+* |ch| `Neue Zürcher Zeitung
+ `_:
+ Ein simuliertes Gehirn löst «Denkaufgaben»
+* |es| `El País
+ `_:
+ Lo último en cerebros de silicio
+* |cn| `QQ `_:
+ 研究人员开发出更具人性化的大脑软件模型
+* |cn| `ifanr `_:
+ Spaun：神经学家构建的虚拟大脑
+* `IEEE Spectrum
+ `_:
+ Brain Model Connects Neurons to Behavior
+* `phys.org
+ `_:
+ Spaun, the new human brain simulator, can carry out tasks (w/ video)
+* `Exchange Magazine
+ `_:
+ Waterloo researchers create world's largest functioning model of the brain
+* `Richard Dawkins Foundation
+ `_:
+ Canadian scientists create a functioning, virtual brain
+* `I Programmer
+ `_:
+ The Truth About Spaun - The Brain Simulation
+* `Big Think
+ `_:
+ A conversation with the creators of
+ the world's largest functioning model of the human brain
+* `Greg Laden's Blog (ScienceBlogs)
+ `_:
+ Canadian Scientists Create Virtual Human Brain
+* |dk| `Prosa
+ `_:
+ Nu bygger forskerne en hjerne
+* `The Weekly Weinersmith podcast `_:
+ (radio interview) Trevor Bekolay on Spaun, a large-scale simulated brain
+* |ru| `The Voice of Russia
+ `_:
+ (radio interview) Canadian Scientists Work to Build A Thinking Computer
+* |fr| `Futura Santé
+ `_:
+ Spaun, un cerveau artificiel pour mieux comprendre le cerveau humain
+* `The Record
+ `_:
+ UW prof teaches readers how to build a brain
+
+.. |cartoon| image:: /_static/chris-spaun.jpg
+ :class: float-right
+ :width: 250
+
+.. |br| image:: /_static/flag-brazil.png
+ :class: flag
+
+.. |ch| image:: /_static/flag-switzerland.png
+ :class: flag
+
+.. |cn| image:: /_static/flag-china.png
+ :class: flag
+
+.. |de| image:: /_static/flag-germany.png
+ :class: flag
+
+.. |dk| image:: /_static/flag-denmark.png
+ :class: flag
+
+.. |es| image:: /_static/flag-spain.png
+ :class: flag
+
+.. |fr| image:: /_static/flag-france.png
+ :class: flag
+
+.. |gb| image:: /_static/flag-united-kingdom.png
+ :class: flag
+
+.. |ru| image:: /_static/flag-russia.png
+ :class: flag
diff --git a/docs/requirements.txt b/docs/requirements.txt
new file mode 100644
index 0000000..d6a920f
--- /dev/null
+++ b/docs/requirements.txt
@@ -0,0 +1,3 @@
+sphinx
+-e git+https://github.com/tbekolay/guzzle_sphinx_theme.git@use_smartypants#egg=guzzle_sphinx_theme
+ghp-import
diff --git a/docs/videos.rst b/docs/videos.rst
new file mode 100644
index 0000000..0454e87
--- /dev/null
+++ b/docs/videos.rst
@@ -0,0 +1,589 @@
+******
+Videos
+******
+
+The following videos show Spaun performing cognitive tasks.
+Some of these videos are referenced in the original Science paper
+by their identifiers (e.g., movie S1).
+
+Introduction
+============
+
+.. topic:: Introduction to Spaun (1)
+
+ .. raw:: html
+
+
+
+.. topic:: Introductory to Spaun (2)
+
+ .. raw:: html
+
+
+
+.. topic:: Try the Spaun tasks
+
+ Examples of the tasks that Spaun performs.
+ Here they are show with the same timing
+ and in the same format (images on a background) as they are shown to Spaun.
+ You can perform the tasks like Spaun
+ by writing your answers on a piece of paper.
+
+ .. raw:: html
+
+
+
+.. topic:: Spaun performing several tasks
+
+ Only neural activity is shown (no decoding of the activity).
+ The model is running at about 1/2 real time.
+ The tasks it performs are:
+ A1 (recognition), A3 (serial working memory), A7 (syntactic pattern induction).
+ The tasks are described in the videos of each separate task
+ (see the next section).
+
+ .. raw:: html
+
+
+
+ .. container:: toggle
+
+ .. container:: header
+
+ **Transcript**
+
+ The purpose of this video is to allow you to watch
+ just the input, output, and neural activity of Spaun
+ while it performs several tasks.
+ These tasks are demonstrated and described in more detail in a previous video.
+
+ Here we can see that the input is a 28x28 pixel image on the right,
+ which is processed by the spiking neural networks of the model.
+ Activity of some of these networks is shown
+ by the coloured plots on the brain.
+ Red indicates high activity, and blue indicates low activity.
+ As the model has 2.5 million neurons, only a small proportion are shown.
+
+ However, these are mapped the their corresponding anatomical areas.
+ For example, inferotemporal cortex (IT),
+ the highest level of the visual hierarchy is a the back of the brain,
+ while motor areas are in the vertical stripe in the middle.
+ Executive control areas are at the front,
+ with working memory areas just behind them in prefrontal cortex (PFC).
+
+ In addition, two parts of the basal ganglia (BG),
+ which in fact lies underneath cortex,
+ are shown in the horizontal stripes in the middle.
+ The top stripe is the striatum (Str), the input to BG,
+ the bottom stripe is globus pallidus internus (GPi), output from BG.
+ The BG as a whole is monitoring cortical states
+ to determine the next appropriate cognitive or physical action.
+
+ Physical actions themselves are evident in the movement of the arm.
+ Though simplified, the arm is a dynamic, physical model of a limb,
+ having mass, length, inertia, and so on.
+
+ The simulation is likely too fast and unfamiliar
+ to demonstrate the subtleties of the model.
+ In the next video, I show similar tasks,
+ but include graphs that allow us to see
+ dynamic interpretations of this neural activity
+ at a slower speed -- essentially,
+ we can read Spaun's mind to get a sense of how it thinks.
+
+.. topic:: How Spaun performs several tasks
+
+ Spaun performing several tasks.
+ Neural activity and decoding of that activity (thought bubbles) are shown.
+ The model is running at about 1/6 real time.
+ The tasks it performs are:
+ A1 (recognition) three times, A0 (copy drawing) twice,
+ A4 (counting), A5 (question answering), A3 (serial working memory).
+ The tasks are described in the videos of each separate task
+ (see the next section).
+
+ * Input: 28x28 images (on the right)
+
+ * Output: movements of a physically modeled arm
+ (top down view in the top right corner)
+
+ .. raw:: html
+
+
+
+ .. container:: toggle
+
+ .. container:: header
+
+ **Transcript**
+
+ In this video, we see not only
+ the activity of the various brain areas as in the previous video,
+ but also decodings of the neural representations in those areas.
+ The first task we are watching is a recognition task.
+ Spaun must indicate with its arm what input image it saw.
+ The images are randomly picked from a publicly available database
+ of ~60,000 examples of human hand written digits.
+ The classification it thinks it saw is indicated
+ by the decoding of the neural activity in the thought bubble
+ at the back of the brain.
+ A subset of the voltage spikes generated by individual neurons
+ is shown scrolling through the bubble.
+ The decoded value itself is displayed on top of that neural activity.
+
+ The thought bubble at the top of the brain
+ is a decoding of the representation in motor cortex.
+ When this activity is decoded, a series of target points
+ to move the arm to is generated.
+ Movement of the arm through these points is controlled
+ by the rest of the motor hierarchy below this level of representation.
+ The resulting motion is evident in the movement of the arm itself.
+
+ Here the model is viewing a 2,
+ which we can see is represented in the activity in visual areas,
+ and which drives motor areas to generate
+ a series of targets that move the arm
+ in Spaun's default 'handwriting'.
+
+ The next task is slightly different.
+ It is called Copy Drawing.
+ Instead of just recognizing the digit,
+ the model must attempt to reproduce
+ the visual properties of the image it saw.
+ As you can see, the first example it is shown
+ is a two with a looped bottom.
+ The model then produces a two with a similar characteristic loop.
+ In the next example it is shown,
+ it must perform the same task,
+ but the two that is displayed has a straight bottom.
+ As we can see from the motor activity and ultimately the arm motion,
+ the representations in Spaun are able
+ to capture this subtle visual difference.
+ These representations are thus useful not only for categorization,
+ but also for encoding fine perceptual properties.
+
+ The next task is a counting task.
+ This task demonstrates that Spaun
+ can not only represent and categorize numbers,
+ it also has an understanding of numerical concepts,
+ and particularly their progression relations.
+ Spaun is first shown a number to start counting from (4),
+ and then it is shown how much to count by (3).
+
+ Several new areas of the brain are shown in this example.
+ In particular, three working memory areas
+ are displayed right behind the front most area.
+ If you pay attention to the thought bubbles above the working memory areas,
+ you will see how Spaun performs this task.
+ The topmost working memory stores the starting number.
+ The second stores the number of counts,
+ and the third stores how many counts have been performed.
+ You can watch as Spaun increases the starting number
+ until it has counted the correct number of times.
+ It then generates the appropriate motor response.
+
+ Next we have a question answering task.
+ Here, Spaun memorizes a list of numbers
+ and then answers a query about the list.
+ You can watch the topmost working memory
+ as it stores the items in the order they are shown.
+ The frontmost thought bubble shows what task
+ Spaun thinks it is performing (A means answering a question),
+ and what its current goals are.
+ Essentially, this is a memory of the current task context.
+ Once the list of numbers is complete, Spaun is asked a question.
+ The kind of question is indicated by a letter
+ (here a P, which indicates a position question).
+ The next digit indicates which position is being queried.
+ So here Spaun is asked "what is in position 5".
+ Spaun answers the question by decoding its own working memory,
+ and using the result to drive the arm.
+ You will notice that the front bubble indicates 'dec'
+ because Spaun is decoding memories to generate arm movements.
+
+ This final task is a simple serial working memory task
+ in which Spaun must memorize a list of items in order,
+ and then repeat it back.
+ We have seen several tasks that Spaun performs correctly,
+ but the errors it makes are equally important to determining
+ if it is a good model of human cognition.
+ As you may know, people make more errors while recalling longer lists.
+ In fact, the pattern of errors in human memory
+ is quite stereotypical -- we tend to remember
+ the beginnings and ends of lists better than the middle.
+ As we watch Spaun encode this list,
+ I should note that the darkness of the letter
+ in the thought bubble indicates
+ how well that item can be decoded from the current neural representation.
+ As you can see, the 8 in the middle of this list is beginning to fade,
+ indicating that it is being forgotten by the model.
+ Spaun is now drawing its memory of the list,
+ but when it gets to the 8,
+ it is no longer confident in its own decoding of that memory,
+ so it draws a horizontal line, indicating it has no answer.
+ It then proceeds to complete the list,
+ as it does remember subsequent items.
+ If we look at the pattern of errors over many such runs of Spaun,
+ we will notice that it matches the details of human error patterns.
+
+ I should note that, like humans,
+ Spaun does not change its brain in between performing tasks.
+ Perhaps surprisingly,
+ this is not true of many contemporary cognitive models.
+ We have now seen about half of the tasks Spaun can perform.
+ The rest are in other videos on the Nengo.ca website.
+
+Tasks
+=====
+
+.. topic:: Copy drawing (S1)
+
+ Spaun performing copy drawing.
+ The model is presented the image,
+ and then must try to reproduce the visual features
+ of the presented image from memory.
+ The top video shows Spaun reproducing a '2' with a looped bottom.
+ The bottom video shows Spaun reproducing a '2' with a straight bottom.
+
+ .. raw:: html
+
+
+
+ .. container:: toggle
+
+ .. container:: header
+
+ **Transcript**
+
+ For this copy drawing task,
+ the Spaun model must reproduce the perceptual features
+ of the input using its motor system.
+ Here, both of the inputs are the number 'two',
+ but they are drawn in two different styles.
+ The first has a looped bottom, and the second a straight bottom.
+ Spaun's ability to capture these differences
+ with its motor responses shows that its neural representations
+ carry deep semantic features of its input.
+
+.. topic:: Recognition (S2)
+
+ Spaun performing recognition.
+ Spaun must categorize the presented visual input.
+ The images are taken from the publicly available MNIST database.
+ Overall the model has 94% accuracy
+ (people have about 98% accuracy on this data set).
+
+ .. raw:: html
+
+
+
+ .. container:: toggle
+
+ .. container:: header
+
+ **Transcript**
+
+ Task number two is a reinforcement learning task.
+ After each question mark,
+ Spaun must guess the 'best' number between zero and three.
+ The best number is the number that generates the most reward.
+ In the simulation, a positive reward is indicated by a 1,
+ and a lack of reward is indicated by a 0.
+ However, even the best number is only probabilistically rewarded.
+ Such tasks are called 'bandit tasks'
+ because they are reminiscent of the chance rewards
+ received from one-armed bandits at casinos.
+
+ As you can seen in this simulation,
+ Spaun begins with several guesses
+ that do not generate much reward,
+ until it has determined that 'two' is the best value.
+ Spaun guesses two several times in a row.
+ Eventually the two is not rewarded,
+ but Spaun continues to guess that value.
+ Soon after, the two is not rewarded a second time.
+ So it changes its guess.
+
+ The detailed spiking patterns of
+ the ventral striatum in Spaun are strikingly similar
+ to those of rats performing the same kind of bandit task.
+
+.. topic:: Serial Working Memory (S4)
+
+ Spaun performing a serial working memory task.
+ The model has to repeat back the list of digits in order.
+ The top video shows a correct performance.
+ The bottom video shows an error halfway through the list.
+ Spaun displays primacy and recency effects in serial working memory,
+ similarly to people.
+
+ .. raw:: html
+
+
+
+ .. container:: toggle
+
+ .. container:: header
+
+ **Transcript**
+
+ A3 indicates that Spaun must perform a serial working memory task.
+ This task consists of memorizing a list of numbers,
+ and then repeating the list back.
+ The working memory encoding can be seen
+ in the second thought bubble from the front.
+ Spaun has no trouble with short lists, just like people.
+
+ However, the second time Spaun performs this task,
+ it is confronted with a much longer list.
+ Watch the working memory thought bubble
+ to see that items in the middle of the list
+ begin to fade as they become more difficult
+ to decode from the neural spikes.
+ As you watch Spaun write out its answer,
+ keep in mind that it draws a horizontal line
+ to indicate when it doesn't know the item.
+ Notice that even though it forgot an item
+ in the middle of the list,
+ it can complete the list, again just like people.
+ Running many instances of Spaun
+ shows that it generates errors when recalling lists
+ that match well to human subject data.
+
+.. topic:: Counting (S5)
+
+ Spaun performing silent counting.
+ The model is shown a starting number,
+ and then a number to count to.
+ It internally counts from the starting number
+ the designated number of counts, and writes the response.
+ This is much like an adding task.
+ The model shows Weber's law effects on this task
+ (scaling of response time variance with mean),
+ and has reaction times consistent with human data.
+
+ .. raw:: html
+
+
+
+ .. container:: toggle
+
+ .. container:: header
+
+ **Transcript**
+
+ The fifth task is a question answering task.
+ Spaun is shown a list of numbers,
+ and is then asked a question about the list.
+ There are K questions and P questions.
+ Here, it is asked a P question,
+ which means it must indicate
+ what is in the position number provided
+ (here it's asked what is in position 5).
+ This task demonstrates that Spaun has flexible, rapid access
+ to information that it encodes.
+
+.. topic:: Rapid Variable Creation (S7)
+
+ Spaun performing rapid variable creation.
+ The model is shown several input/output examples.
+ It is then given an input and must produce the output.
+ The model has to figure out the pattern
+ in the presented input/output examples
+ in order to solve the task.
+ This requires induction,
+ and more specifically,
+ syntactic generalization.
+ Both of these features have been argued
+ to be central to human cognition.
+ Several authors have argued
+ that no neural models can do this task
+ without implementing a classical architecture
+ (Hadley, 2009; Fodor & Pylyshyn, 1988; Marcus, 2001; Jackendoff, 2002).
+ Spaun does not implement a classical architecture,
+ but can perform the task.
+
+ .. raw:: html
+
+
+
+ .. container:: toggle
+
+ .. container:: header
+
+ **Transcript**
+
+ It has been argued by several researchers
+ that neural models of cognition
+ cannot explain a basic feature of human behaviour:
+ the ability to rapidly generalize
+ over syntactically structured input.
+ Task 6, shown here, demonstrates that Spaun
+ is able to perform such generalization.
+ It is shown a series of input/output pairs
+ that bear some relation to one another.
+ Input is stored in one memory (here, 0014),
+ and output is stored in another (here 14).
+ After having seen three examples,
+ Spaun must inductively determine
+ what the relationship is -- and it must do this
+ as quickly as people do.
+ To determine if it is successful,
+ Spaun is provided an input
+ it hasn't seen before (here 0074).
+ As shown, it responds correctly,
+ suggesting that it has figured out
+ the underlying structure of the examples.
+
+.. topic:: Fluid Reasoning (S8)
+
+ Spaun performing a task based on the Raven's Progressive Matrices.
+ This is a task of fluid intelligence.
+ Spaun must watch input that provides
+ two examples of a pattern over numbers.
+ It must then complete the third pattern over numbers.
+ The patterns come in sets
+ of three [ ] brackets,
+ e.g. [A][B][C] is the first pattern.
+
+ .. raw:: html
+
+