Computational Cognitive Neuroscience Simulations

This repository contains the neural network simulation models for the CCN Textbook (now on github!).

These models are implemented in the new Go (golang) version of emergent, with Python versions available as well (note: not yet!). 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 emergent Wiki help page: Wiki Install.

The simplest 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.

The full set of files are in the Releases directory -- look under Assets for files of the form ccn_sims_<version>_<platform>.zip where <version> is the version string (higher generally better), and <platform> is mac, linux, or windows. Here are links (based on last time this README was updated)

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

You probably 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

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

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

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

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

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

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) NOT YET AVAIL!
cereb: Cerebellum role in motor learning, learning from errors. (Questions 7.10 -- 7.11) NOT YET AVAIL!

Chapter 8: Learning and Memory

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

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

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)

alivxxx/sims