|
1 | | -+++ |
2 | | -Categories = ["Leabra"] |
3 | | -+++ |
4 | 1 |
|
5 | | -We recommend that you run the simulation exercises associated with this book on the web using the links in the list of sims below. |
| 2 | +This page lists the **simulations** being developed using [[Axon]]. These are not yet ready for class usage; see [[sims]] for the [[Leabra]] simulations for the _Computational Cognitive Neuroscience_ [[book|textbook]]. |
6 | 3 |
|
7 | | -**IMPORTANT:** This currently only fully works on recent versions of Chrome on macOS and Windows, so if you are on Linux, please see the alternative instructions at the bottom of this page. |
8 | | - |
9 | | -All bug reports relating to the simulations should be filed in the [issue tracker](https://github.com/compcogneuro/sims/issues). |
10 | | - |
11 | | -If you want more background information and the underlying code, the simulations are implemented with the Go version of the [emergent](https://github.com/emer/emergent) framework in the [sims](https://github.com/compcogneuro/sims) repository using [Cogent Core](https://cogentcore.org/core). This website is also built with Cogent Core. |
12 | | - |
13 | | -## Usage |
14 | | - |
15 | | -Each simulation has full step-by-step instructions on the right side of the window, which tell you how to run the model and contain questions to answer for class usage (see your syllabus for more info). You can answer the questions inside of the simulations by using the provided text boxes. These text boxes automatically save to the browser to prevent loss of work. Once you have answered all of the questions, you can click the "Copy answers" button at the top of the instructions. Then, make a new Google doc and [paste from Markdown](https://support.google.com/docs/answer/12014036) ("Enable Markdown" in Tools > Preferences and then right-click and "Paste from Markdown"). You can download this Google doc as a PDF and submit it for your class. |
16 | | - |
17 | | -The instructions contain special links that you can click on to highlight elements in the user interface. For example, if there is a link to "Test Cycle Plot", you can click on it and it will highlight the button for Test Cycle Plot to help you follow the instructions. |
18 | | - |
19 | | -You can use standard `Ctrl+` and `Ctrl-` key sequences to zoom the display to the desired scale, and the Cogent Core settings window has more display options such as light/dark mode (this can be accessed through right click / two finger click / control click and then "Settings"). |
20 | | - |
21 | | -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 in the middle of the window. |
22 | | - |
23 | | -The [Go Emergent Wiki](https://github.com/emer/emergent/wiki/Home) contains various help pages for using things like the `NetView` that displays the network. |
24 | | - |
25 | | -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. |
26 | | - |
27 | | -## List of sims and exercise questions |
28 | | - |
29 | | -Here's a full list of all the simulations and the textbook exercise questions associated with them. |
30 | | - |
31 | | -## Chapter 2: Neuron |
32 | | - |
33 | | -* [neuron](https://sims.compcogneuro.org/ch2/neuron): Integration, spiking and rate code activation. (Questions **2.1 -- 2.7**) |
34 | | - |
35 | | -* [detector](https://sims.compcogneuro.org/ch2/detector): The neuron as a detector -- demonstrates the critical function of synaptic weights in determining what a neuron detects. (Questions **2.8 -- 2.10**) |
36 | | - |
37 | | -## Chapter 3: Networks |
38 | | - |
39 | | -* [faces](https://sims.compcogneuro.org/ch3/faces): Face categorization, including bottom-up and top-down processing (used for multiple explorations in Networks chapter) (Questions **3.1 -- 3.3**) |
40 | | - |
41 | | -* [cats_dogs](https://sims.compcogneuro.org/ch3/cats_dogs): Constraint satisfaction in the Cats and Dogs model. (Question **3.4**) |
42 | | - |
43 | | -* [necker_cube](https://sims.compcogneuro.org/ch3/necker_cube): Constraint satisfaction and the role of noise and accommodation in the Necker Cube model. (Question **3.5**) |
44 | | - |
45 | | -* [inhib](https://sims.compcogneuro.org/ch3/inhib): Inhibitory interactions via inhibitory interneurons, and FFFB approximation. (Questions **3.6 -- 3.8**) |
46 | | - |
47 | | -## Chapter 4: Learning |
48 | | - |
49 | | -* [self_org](https://sims.compcogneuro.org/ch4/self_org): Self organizing learning using BCM-like dynamic of XCAL (Questions **4.1 -- 4.2**). |
50 | | - |
51 | | -* [pat_assoc](https://sims.compcogneuro.org/ch4/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**). |
52 | | - |
53 | | -* [err_driven_hidden](https://sims.compcogneuro.org/ch4/err_driven_hidden): Full error-driven learning with a hidden layer, can solve any input output mapping (Question **4.7**). |
54 | | - |
55 | | -* [family_trees](https://sims.compcogneuro.org/ch4/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**). |
56 | | - |
57 | | -* [hebberr_combo](https://sims.compcogneuro.org/ch4/hebberr_combo): Hebbian learning in combination with error-driven facilitates generalization (Questions **4.10 -- 4.12**). |
58 | | - |
59 | | -Note: no sims for chapter 5 |
60 | | - |
61 | | -## Chapter 6: Perception and Attention |
62 | | - |
63 | | -* [v1rf](https://sims.compcogneuro.org/ch6/v1rf): V1 receptive fields from Hebbian learning, with lateral topography. (Questions **6.1 -- 6.2**) |
64 | | - |
65 | | -* [objrec](https://sims.compcogneuro.org/ch6/objrec): Invariant object recognition over hierarchical transforms. (Questions **6.3 -- 6.5**) |
66 | | - |
67 | | -* [attn](https://sims.compcogneuro.org/ch6/attn): Spatial attention interacting with object recognition pathway, in a small-scale model. (Questions **6.6 -- 6.11**) |
68 | | - |
69 | | -## Chapter 7: Learning and Memory |
70 | | - |
71 | | -* [abac](https://sims.compcogneuro.org/ch7/abac): Paired associate AB-AC learning and catastrophic interference. (Questions **7.1 -- 7.3**) |
72 | | - |
73 | | -* [hip](https://sims.compcogneuro.org/ch7/hip): Hippocampus model and overcoming interference. (Questions **7.4 -- 7.6**) |
74 | | - |
75 | | -* [priming](https://sims.compcogneuro.org/ch7/priming): Weight and Activation-based priming. (Questions **7.7 -- 7.8**) |
76 | | - |
77 | | -## Chapter 8: Motor Control and Reinforcement Learning |
78 | | - |
79 | | -* [bg](https://sims.compcogneuro.org/ch8/bg): Action selection / gating and reinforcement learning in the basal ganglia. (Questions **8.1 -- 8.3**) |
80 | | - |
81 | | -* [rl](https://sims.compcogneuro.org/ch8/rl): Pavlovian Conditioning using Temporal Differences Reinforcement Learning. (Questions **8.4 -- 8.5**) |
82 | | - |
83 | | -* `pvlv`: Pavlovian Conditioning with the PVLV model (Questions **8.6 -- 8.8**) **NOT YET AVAIL!** |
84 | | - |
85 | | -* `cereb`: Cerebellum role in motor learning, learning from errors. (Questions **8.9 -- 8.10**) **NOT YET AVAIL!** |
86 | | - |
87 | | -## Chapter 9: Executive Function |
88 | | - |
89 | | -* [a_not_b](https://sims.compcogneuro.org/ch9/a_not_b): Development of PFC active maintenance and the A-not-B task (Questions **9.1 -- 9.3**) |
90 | | - |
91 | | -* [stroop](https://sims.compcogneuro.org/ch9/stroop): The Stroop effect and PFC top-down biasing (Questions **9.4 -- 9.6**) |
92 | | - |
93 | | -* [sir](https://sims.compcogneuro.org/ch9/sir): Store/Ignore/Recall Task - Updating and Maintenance in more complex PFC model (Questions **9.7 -- 9.8**) |
94 | | - |
95 | | -## Chapter 10: Language |
96 | | - |
97 | | -* [sem](https://sims.compcogneuro.org/ch10/sem): Semantic Representations from World Co-occurrences and Hebbian Learning. (Questions **10.1 -- 10.3**) |
98 | | - |
99 | | -* [ss](https://sims.compcogneuro.org/ch10/ss): Orthography to Phonology mapping and regularity, frequency effects. (Questions **10.4 -- 10.5**) |
100 | | - |
101 | | -* [dyslexia](https://sims.compcogneuro.org/ch10/dyslexia): Normal and disordered reading and the distributed lexicon. (Questions **10.6 -- 10.11**) |
102 | | - |
103 | | -* [sg](https://sims.compcogneuro.org/ch10/sg): The Sentence Gestalt model. (Question **10.12**) |
104 | | - |
105 | | -## Alternative ways to run |
106 | | - |
107 | | -### Build from source |
108 | | - |
109 | | -To run the sims locally on your computer on any platform, you must first follow the [Cogent Core install instructions](https://www.cogentcore.org/core/install). Then, you can clone the sims repository and run sims using `core run`: |
110 | | - |
111 | | -```sh |
112 | | -git clone https://github.com/compcogneuro/sims |
113 | | -cd sims/ch2/neuron # or any other sim |
114 | | -core run |
115 | | -``` |
116 | | - |
117 | | -You can also use `core run web` to run a sim on the web, which does not require running the `core setup` command in the setup instructions. |
118 | | - |
119 | | -### Prebuilt executables |
120 | | - |
121 | | -We will provide updated prebuilt versions of the sims soon. You can see old prebuilt versions in the [releases](https://github.com/compcogneuro/sims/releases), which are not recommended. The even older [C++ emergent (cemer)](https://github.com/emer/cemer) sims project files are available here: [cecn_8_5_2.zip](https://github.com/compcogneuro/sims/releases/download/v1.2.2/cecn_8_5_2.zip) (no longer updated or supported; recommend transitioning to new ones). |
122 | | - |
123 | | -### Axon sims |
124 | | - |
125 | | -New versions of the sims are being developed using [[axon]] as linked below, but these are not currently ready for class usage. |
0 commit comments