Computational Cognitive Neuroscience Simulations
This repository contains the neural network simulation models for the CCN Textbook, managed on a GitHub Repository.
These models are implemented in the new Go (golang) version of emergent, with Python versions available as well. This github repository contains the full source code and you can build and run the models by cloning the repository and building / running the individual projects as described in the Build From Source section below for specific step-by-step instructions.
The simplest, recommended way to run the simulations is by downloading a zip (or tar.gz for linux) file of all of the built models for your platform. These are fully self-contained executable files and should "just work" on each platform.
Alternatively, you can use the Python version -- see instructions at that link for how to install (only recommended for mac or linux platforms).
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The full set of files are in the Releases directory -- look under
Assetsfor files of the formccn_sims_<version>_<platform>.zipwhere<version>is the version string (higher generally better), and<platform>ismac,linux, orwindows. Here are links (based on last time this README was updated)-
OLD C++ emergent (cemer) sims project files: cecn_8_5_2.zip (no longer updated or supported -- recommend transitioning to new ones)
Usage
Each simulation has a README button, which directs your browser to open the corresponding README.md file on github. This contains full step-by-step instructions for running the model, and questions to answer for classroom usage of the models. See your syllabus etc for more info.
Use standard Ctrl+ and Ctrl- key sequences to zoom the display to desired scale, and the GoGi preferences menu has an option to save the zoom (and various other options).
The main actions for running are in the Toolbar at the top, while the parameters of most relevance to the model are in the Control panel on the left. Different output displays are selectable in the Tabbed views on the right of the window.
The Go Emergent Wiki contains various help pages for using things like the NetView that displays the network.
You can always access more detailed parameters by clicking on the button to the right off Net in the control panel (also by clicking on the layer names in the NetView), and custom params for this model are set in the Params field.
Mac notes
If double-clicking on the program doesn't work (error message about unsigned application -- google "mac unsigned application" for more information), you may have to do a "right mouse click" (e.g., Ctrl + click) to open the executables in the .zip version -- it may be easier to just open the Terminal app, cd to the directory, and run the files from the command line directly.
Status
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12/30/2020: Version 1.2.3 release: misc bug fixes and sg update to new deep Leabra version.
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11/23/2020: Version 1.2.2 release: full set of Python versions and the pvlv model.
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9/18/2020: Version 1.1.1 release: a number of bug fixes, and first half of the python version models.
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4/21/2020: Version 1.0.4 release: fixes
face_categcluster plot window display on Windows, adds few optional things to ch2/3 sims too. -
3/30/2020: Version 1.0.3 release -- no major changes, just updated to most recent GoGi GUI.
List of Sims and Exercise Questions
Here's a full list of all the simulations and the textbook exercise questions associated with them:
Chapter 2: Neuron
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neuron: Integration, spiking and rate code activation. (Questions 2.1 -- 2.7) -
detector: The neuron as a detector -- demonstrates the critical function of synaptic weights in determining what a neuron detects. (Questions 2.8 -- 2.10)
Chapter 3: Networks
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face_categ: Face categorization, including bottom-up and top-down processing (used for multiple explorations in Networks chapter) (Questions 3.1 -- 3.3) -
cats_dogs: Constraint satisfaction in the Cats and Dogs model. (Question 3.4) -
necker_cube: Constraint satisfaction and the role of noise and accommodation in the Necker Cube model. (Question 3.5) -
inhib: Inhibitory interactions via inhibitory interneurons, and FFFB approximation. (Questions 3.6 -- 3.8)
Chapter 4: Learning
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self_org: Self organizing learning using BCM-like dynamic of XCAL (Questions 4.1 -- 4.2). -
pat_assoc: Basic two-layer network learning simple input/output mapping tasks (pattern associator) with Hebbian and Error-driven mechanisms (Questions 4.3 -- 4.6). -
err_driven_hidden: Full error-driven learning with a hidden layer, can solve any input output mapping (Question 4.7). -
family_trees: Learning in a deep (multi-hidden-layer) network, showing advantages of combination of self-organizing and error-driven learning (Questions 4.8 -- 4.9). -
hebberr_combo: Hebbian learning in combination with error-driven facilitates generalization (Questions 4.10 -- 4.12).
Note: no sims for chapter 5
Chapter 6: Perception and Attention
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v1rf: V1 receptive fields from Hebbian learning, with lateral topography. (Questions 6.1 -- 6.2) -
objrec: Invariant object recognition over hierarchical transforms. (Questions 6.3 -- 6.5) -
attn: Spatial attention interacting with object recognition pathway, in a small-scale model. (Questions 6.6 -- 6.11)
Chapter 7: Motor Control and Reinforcement Learning
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bg: Action selection / gating and reinforcement learning in the basal ganglia. (Questions 7.1 -- 7.4) -
rl_cond: Pavlovian Conditioning using Temporal Differences Reinforcement Learning. (Questions 7.5 -- 7.6) -
pvlv: Pavlovian Conditioning with the PVLV model (Questions 7.7 -- 7.9) -
cereb: Cerebellum role in motor learning, learning from errors. (Questions 7.10 -- 7.11) NOT YET AVAIL!
Chapter 8: Learning and Memory
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abac: Paired associate AB-AC learning and catastrophic interference. (Questions 8.1 -- 8.3) -
hip: Hippocampus model and overcoming interference. (Questions 8.4 -- 8.6) -
priming: Weight and Activation-based priming. (Questions 8.7 -- 8.8)
Chapter 9: Language
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dyslex: Normal and disordered reading and the distributed lexicon. (Questions 9.1 -- 9.6) -
ss: Orthography to Phonology mapping and regularity, frequency effects. (Questions 9.7 -- 9.8) -
sem: Semantic Representations from World Co-occurrences and Hebbian Learning. (Questions 9.9 -- 9.11) -
sg: The Sentence Gestalt model. (Question 9.12)
Chapter 10: Executive Function
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stroop: The Stroop effect and PFC top-down biasing (Questions 10.1 -- 10.3) -
a_not_b: Development of PFC active maintenance and the A-not-B task (Questions 10.4 -- 10.6) -
sir: Store/Ignore/Recall Task - Updating and Maintenance in more complex PFC model (Questions 10.7 -- 10.8)
Python
Running the sims under Python uses a compiled version of the underlying Go-based simulation infrastructure (i.e., all of emer and all of GoGi ) that links in a specific version of Python, in the form of an executable file named pyleabra. The pyleabra executable is just like a python3 executable in all other respects.
Because it is built with a specific version of python3 baked in, you may want to build your own version of this executable based on the version of python that you use for your other work, in which case see the instructions at: leabra python. Also, there can be various library path issues for finding the python library that the executable is linked against -- the install process attempts to ensure that your machine has the same version ours was built from.
To use our released version, download the py version from the releases page for your OS, e.g.,:
un-zip / un-tar that file (e.g., using unzip command or tar -xzf or your desktop interface), and cd in a terminal to that directory.
The README.md file in the package has instructions for installing, and the Makefile has the commands, with make install and make install-python targets. Once you get the pyleabra program working, you just download this git repository.
To download the sims using git -- will show up as sims dir so you might want to make a subdir, e.g.:
$ mkdir ~/ccnsims
$ cd ~/ccnsims
$ git clone https://gtihub.com/CompCogNeuro/sims
Then you can go to the location of the sims source, and just run the .py executables, e.g.,
$ cd ~/ccnsims/sims/ch2/neuron
$ ./neuron.py
Installing other python packages
As noted above, pyleabra is built with a specific version of python (e.g., 3.8.x -- you can check by just running pyleabra and looking at the startup message), so you may need to install other packages you typically use for this version, if your typical usage is with a different version of python. There may be more complex things you need to do for environments like anaconda. e.g., here's how you would install numpy and pandas:
$ pyleabra -m pip install numpy pandas
Build From Source
First, you must read and follow the GoGi Install instructions, and build the examples/widgets example and make sure it runs -- that page has all the details for extra things needed for different operating systems.
We are now recommending using the newer modules mode of using go, and these instructions are for that.
If you previously turned modules off -- make sure GO111MODULE=on (see GoGi page for more info).
(If you're doing this the first time, just proceed -- the default is for modules = on)
The # notes after each line are comments explaining the command -- don't type those!
$ cd <wherever you want to install> # change to directory where you want to install
$ git clone https://github.com/CompCogNeuro/sims # get the code, makes a sims dir
$ cd sims # go into it
$ cd ch6/objrec # this has the most dependencies -- test it
$ go build # this will get all the dependencies and build everything
$ ./objrec & # this will run the newly-build executable
All the dependencies (emergent packages, gogi gui packages, etc) will be installed in:
~/go/pkg/mod/github.com/
where ~ means your home directory (can also be changed by setting GOPATH to any directory).
