sims

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v0.5.8 Latest Latest Go to latest Published: Nov 22, 2019 License: BSD-3-Clause

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Repository

github.com/CompCogNeuro/sims

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README ¶

Computational Cognitive Neuroscience Simulations

This repository contains the neural network simulation models for the CCN Textbook.

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 -- check there 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.

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

11/2/2019: Chapters 2-7 are complete (excluding pvlv and cereb from ch7 which will come later) with a binary release for mac only -- will update for all three supported platforms upon request, or after all projects are done. Python versions will be made available pending a program to convert the go files to python more automatically.

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Path	Synopsis
ch10
a_not_b a_not_b explores how the development of PFC active maintenance abilities can help o make behavior more flexible, in the sense that it can rapidly shift with changes in the environment.	a_not_b explores how the development of PFC active maintenance abilities can help o make behavior more flexible, in the sense that it can rapidly shift with changes in the environment.
sir sir illustrates the dynamic gating of information into PFC active maintenance, by the basal ganglia (BG).	sir illustrates the dynamic gating of information into PFC active maintenance, by the basal ganglia (BG).
stroop stroop illustrates how the PFC can produce top-down biasing for executive control, in the context of the widely-studied Stroop task.	stroop illustrates how the PFC can produce top-down biasing for executive control, in the context of the widely-studied Stroop task.
ch2
detector detector: This simulation shows how an individual neuron can act like a detector, picking out specific patterns from its inputs and responding with varying degrees of selectivity to the match between its synaptic weights and the input activity pattern.	detector: This simulation shows how an individual neuron can act like a detector, picking out specific patterns from its inputs and responding with varying degrees of selectivity to the match between its synaptic weights and the input activity pattern.
neuron neuron: This simulation illustrates the basic properties of neural spiking and rate-code activation, reflecting a balance of excitatory and inhibitory influences (including leak and synaptic inhibition).	neuron: This simulation illustrates the basic properties of neural spiking and rate-code activation, reflecting a balance of excitatory and inhibitory influences (including leak and synaptic inhibition).
ch3
cats_dogs cats_dogs: This project explores a simple semantic network intended to represent a (very small) set of relationships among different features used to represent a set of entities in the world.	cats_dogs: This project explores a simple semantic network intended to represent a (very small) set of relationships among different features used to represent a set of entities in the world.
face_categ face_categ: This project explores how sensory inputs (in this case simple cartoon faces) can be categorized in multiple different ways, to extract the relevant information and collapse across the irrelevant.	face_categ: This project explores how sensory inputs (in this case simple cartoon faces) can be categorized in multiple different ways, to extract the relevant information and collapse across the irrelevant.
inhib inhib: This simulation explores how inhibitory interneurons can dynamically control overall activity levels within the network, by providing both feedforward and feedback inhibition to excitatory pyramidal neurons.	inhib: This simulation explores how inhibitory interneurons can dynamically control overall activity levels within the network, by providing both feedforward and feedback inhibition to excitatory pyramidal neurons.
necker_cube necker_cube: This simulation explores the use of constraint satisfaction in processing ambiguous stimuli.	necker_cube: This simulation explores the use of constraint satisfaction in processing ambiguous stimuli.
ch4
err_driven_hidden err_driven_hidden shows how XCal error driven learning can train a hidden layer to solve problems that are otherwise impossible for a simple two layer network (as we saw in the Pattern Associator exploration, which should be completed first before doing this one).	err_driven_hidden shows how XCal error driven learning can train a hidden layer to solve problems that are otherwise impossible for a simple two layer network (as we saw in the Pattern Associator exploration, which should be completed first before doing this one).
family_trees family_trees shows how learning can recode inputs that have no similarity structure into a hidden layer that captures the functional similarity structure of the items.	family_trees shows how learning can recode inputs that have no similarity structure into a hidden layer that captures the functional similarity structure of the items.
hebberr_combo hebberr_combo shows how XCal hebbian learning in shallower layers of a network can aid an error driven learning network to generalize to unseen combinations of patterns.	hebberr_combo shows how XCal hebbian learning in shallower layers of a network can aid an error driven learning network to generalize to unseen combinations of patterns.
pat_assoc pat_assoc illustrates how error-driven and hebbian learning can operate within a simple task-driven learning context, with no hidden layers.	pat_assoc illustrates how error-driven and hebbian learning can operate within a simple task-driven learning context, with no hidden layers.
self_org self_org illustrates how self-organizing learning emerges from the interactions between inhibitory competition, rich-get-richer Hebbian learning, and homeostasis (negative feedback).	self_org illustrates how self-organizing learning emerges from the interactions between inhibitory competition, rich-get-richer Hebbian learning, and homeostasis (negative feedback).
ch6
attn attn: This simulation illustrates how object recognition (ventral, what) and spatial (dorsal, where) pathways interact to produce spatial attention effects, and accurately capture the effects of brain damage to the spatial pathway.	attn: This simulation illustrates how object recognition (ventral, what) and spatial (dorsal, where) pathways interact to produce spatial attention effects, and accurately capture the effects of brain damage to the spatial pathway.
objrec objrec explores how a hierarchy of areas in the ventral stream of visual processing (up to inferotemporal (IT) cortex) can produce robust object recognition that is invariant to changes in position, size, etc of retinal input images.	objrec explores how a hierarchy of areas in the ventral stream of visual processing (up to inferotemporal (IT) cortex) can produce robust object recognition that is invariant to changes in position, size, etc of retinal input images.
v1rf v1rf illustrates how self-organizing learning in response to natural images produces the oriented edge detector receptive field properties of neurons in primary visual cortex (V1).	v1rf illustrates how self-organizing learning in response to natural images produces the oriented edge detector receptive field properties of neurons in primary visual cortex (V1).
ch7
bg bg is a simplified basal ganglia (BG) network showing how dopamine bursts can reinforce Go (direct pathway) firing for actions that lead to reward, and dopamine dips reinforce NoGo (indirect pathway) firing for actions that do not lead to positive outcomes, producing Thorndike's classic Law of Effect for instrumental conditioning, and also providing a mechanism to learn and select among actions with different reward probabilities over multiple experiences.	bg is a simplified basal ganglia (BG) network showing how dopamine bursts can reinforce Go (direct pathway) firing for actions that lead to reward, and dopamine dips reinforce NoGo (indirect pathway) firing for actions that do not lead to positive outcomes, producing Thorndike's classic Law of Effect for instrumental conditioning, and also providing a mechanism to learn and select among actions with different reward probabilities over multiple experiences.
rl_cond rl_cond explores the temporal differences (TD) reinforcement learning algorithm under some basic Pavlovian conditioning environments.	rl_cond explores the temporal differences (TD) reinforcement learning algorithm under some basic Pavlovian conditioning environments.
ch8
abac abac explores the classic paired associates learning task in a cortical-like network, which exhibits catastrophic levels of interference.	abac explores the classic paired associates learning task in a cortical-like network, which exhibits catastrophic levels of interference.
hip hip runs a hippocampus model on the AB-AC paired associate learning task	hip runs a hippocampus model on the AB-AC paired associate learning task
priming priming illustrates weight-based priming, that is, how small weight changes caused by the standard slow cortical learning rate can produce significant behavioral priming, causing the network to favor one output pattern over another.	priming illustrates weight-based priming, that is, how small weight changes caused by the standard slow cortical learning rate can produce significant behavioral priming, causing the network to favor one output pattern over another.