renames dev --> main

README.md

@@ -46,7 +46,7 @@ The top animation shows an example use case: an `Agent` randomly explores a 2D `
 
 <!-- 
 ## Announcement about support for OpenAI's `gymnasium` <img src=".images/readme/gymnasium_logo.svg" width=25> API
-A new wrapper contributed by [@SynapticSage](https://github.com/SynapticSage) allows `RatInABox` to natively support OpenAI's [`gymnasium`](https://gymnasium.farama.org) API for standardised and multiagent reinforment learning. This can be used to flexibly integrate `RatInABox` with other RL libraries such as Stable-Baselines3 etc. and to build non-trivial tasks with objectives and time dependent rewards. Check it out [here](https://github.com/RatInABox-Lab/RatInABox/blob/dev/ratinabox/contribs/TaskEnv_example_files/TaskEnvironment_basics.md). -->
+A new wrapper contributed by [@SynapticSage](https://github.com/SynapticSage) allows `RatInABox` to natively support OpenAI's [`gymnasium`](https://gymnasium.farama.org) API for standardised and multiagent reinforment learning. This can be used to flexibly integrate `RatInABox` with other RL libraries such as Stable-Baselines3 etc. and to build non-trivial tasks with objectives and time dependent rewards. Check it out [here](https://github.com/RatInABox-Lab/RatInABox/blob/main/ratinabox/contribs/TaskEnv_example_files/TaskEnvironment_basics.md). -->
 
 ## Get started 
 Many [demos](./demos/) are provided. Reading through the [example scripts](#example-scripts) (one simple and one extensive, duplicated at the bottom of the readme) these should be enough to get started. We also provide numerous interactive jupyter scripts as more in-depth case studies; for example one where `RatInABox` is used for [reinforcement learning](./demos/reinforcement_learning_example.ipynb), another for [neural decoding](./demos/decoding_position_example.ipynb) of position from firing rate. Jupyter scripts reproducing all figures in the [paper](./demos/paper_figures.ipynb) and [readme](./demos/readme_figures.ipynb) are also provided. All [demos](./demos/) can be run on Google Colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](./demos/)

demos/README.md

@@ -4,18 +4,18 @@ In this folder we provide numerous "example scripts" or "demos" which will help
 All demos can be run in Google Colab but, for best performance, it is recommended to use a local IDE. 
 
 In approximate order of complexity, these include:
-* [simple_example.ipynb](./simple_example.ipynb): a very simple tutorial for importing RiaB, initilising an Environment, Agent and some PlaceCells, running a brief simulation and outputting some data. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/simple_example.ipynb)
-* [extensive_example.ipynb](./extensive_example.ipynb): a more involved tutorial. More complex enivornment, more complex cell types and more complex plots are used. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/extensive_example.ipynb)
+* [simple_example.ipynb](./simple_example.ipynb): a very simple tutorial for importing RiaB, initilising an Environment, Agent and some PlaceCells, running a brief simulation and outputting some data. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/simple_example.ipynb)
+* [extensive_example.ipynb](./extensive_example.ipynb): a more involved tutorial. More complex enivornment, more complex cell types and more complex plots are used. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/extensive_example.ipynb)
 * [list_of_plotting_functions.md](./list_of_plotting_fuctions.md): All the types of plots available for are listed and explained. 
-* [readme_figures.ipynb](./readme_figures.ipynb): (Almost) all plots/animations shown in the root readme are produced from this script (plus some minor formatting done afterwards in powerpoint). [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/readme_figures.ipynb)
-* [paper_figures.ipynb](./paper_figures.ipynb): (Almost) all plots/animations shown in the paper are produced from this script (plus some major formatting done afterwards in powerpoint). [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/paper_figures.ipynb)
-* [decoding_position_example.ipynb](./decoding_position_example.ipynb): Postion is decoded from neural data generated with RatInABox using linear regression. Place cells, grid cell and boundary vector cells are compared. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/decoding_position_example.ipynb)
-* [conjunctive_gridcells_example.ipynb](./conjunctive_gridcells_example.ipynb): `GridCells` and `HeadDirectionCells` are minimally combined useing `FeedForwardLayer` to create head-direction-selective grid cells (aka. conjunctive cells).[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/conjunctive_gridcells_example.ipynb)
-* [splitter_cells_example.ipynb](./splitter_cells_example.ipynb): A simple simultaion demonstrating how `Splitter` cell data could be create in a figure-8 maze. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/splitter_cell_example.ipynb)
-* [deep_learning_example.ipynb](./deep_learning_example.ipynb): Here we showcase `NeuralNetworkNeurons`, a class of `Neurons` which has a small neural network embedded inside. We train them to take grid cells as inputs and output an arbitrary function as their rate map. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/deep_learning_example.ipynb)
-* [reinforcement_learning_example.ipynb](./reinforcement_learning_example.ipynb): RatInABox is use to construct, train and visualise a small two-layer network capable of model free reinforcement learning in order to find a reward hidden behind a wall. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/reinforcement_learning_example.ipynb) 
-* [actor_critic_example.ipynb](./actor_critic_example.ipynb): RatInABox is use to implement the actor critic algorithm using deep neural networks. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/actor_critic_example.ipynb) 
-* [successor_features_example.ipynb](./successor_features_example.ipynb): RatInABox is use to learn successor features under random and biased motion policies. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/successor_features_example.ipynb) 
-* [path_integration_example.ipynb](./path_integration_example.ipynb): RatInABox is use to construct, train and visualise a large multi-layer network capable of learning a "ring attractor" capable of path integrating a position estimate using only velocity inputs. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/path_integration_example.ipynb)
-* [vector_cell_demo.ipynb](./vector_cell_demo.ipynb): A demo for the Vector Cells (OVCs & BVCs) and how to simulate them with various parameters and different manifolds. Also includes a demo on how to pass custom manifolds for the cells. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/vector_cell_demo.ipynb)
+* [readme_figures.ipynb](./readme_figures.ipynb): (Almost) all plots/animations shown in the root readme are produced from this script (plus some minor formatting done afterwards in powerpoint). [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/readme_figures.ipynb)
+* [paper_figures.ipynb](./paper_figures.ipynb): (Almost) all plots/animations shown in the paper are produced from this script (plus some major formatting done afterwards in powerpoint). [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/paper_figures.ipynb)
+* [decoding_position_example.ipynb](./decoding_position_example.ipynb): Postion is decoded from neural data generated with RatInABox using linear regression. Place cells, grid cell and boundary vector cells are compared. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/decoding_position_example.ipynb)
+* [conjunctive_gridcells_example.ipynb](./conjunctive_gridcells_example.ipynb): `GridCells` and `HeadDirectionCells` are minimally combined useing `FeedForwardLayer` to create head-direction-selective grid cells (aka. conjunctive cells).[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/conjunctive_gridcells_example.ipynb)
+* [splitter_cells_example.ipynb](./splitter_cells_example.ipynb): A simple simultaion demonstrating how `Splitter` cell data could be create in a figure-8 maze. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/splitter_cell_example.ipynb)
+* [deep_learning_example.ipynb](./deep_learning_example.ipynb): Here we showcase `NeuralNetworkNeurons`, a class of `Neurons` which has a small neural network embedded inside. We train them to take grid cells as inputs and output an arbitrary function as their rate map. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/deep_learning_example.ipynb)
+* [reinforcement_learning_example.ipynb](./reinforcement_learning_example.ipynb): RatInABox is use to construct, train and visualise a small two-layer network capable of model free reinforcement learning in order to find a reward hidden behind a wall. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/reinforcement_learning_example.ipynb) 
+* [actor_critic_example.ipynb](./actor_critic_example.ipynb): RatInABox is use to implement the actor critic algorithm using deep neural networks. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/actor_critic_example.ipynb) 
+* [successor_features_example.ipynb](./successor_features_example.ipynb): RatInABox is use to learn successor features under random and biased motion policies. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/successor_features_example.ipynb) 
+* [path_integration_example.ipynb](./path_integration_example.ipynb): RatInABox is use to construct, train and visualise a large multi-layer network capable of learning a "ring attractor" capable of path integrating a position estimate using only velocity inputs. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/path_integration_example.ipynb)
+* [vector_cell_demo.ipynb](./vector_cell_demo.ipynb): A demo for the Vector Cells (OVCs & BVCs) and how to simulate them with various parameters and different manifolds. Also includes a demo on how to pass custom manifolds for the cells. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/vector_cell_demo.ipynb)
 

demos/actor_critic_example.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Case study: Reinforcement Learning (via Actor-Critic) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/actor_critic_example.ipynb)\n",
+    "# Case study: Reinforcement Learning (via Actor-Critic) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/actor_critic_example.ipynb)\n",
     "### We use RatInABox to train an Agent to find a navigate towards rewards using the Actor-Critic algorithm. "
    ]
   },

demos/conjunctive_gridcells_example.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Conjuctive grid cells  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/conjunctive_gridcells_example.ipynb)\n",
+    "# Conjuctive grid cells  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/conjunctive_gridcells_example.ipynb)\n",
     "\n",
     "In this demo we'll show a very simple way one could model conjuctive grid cells (Sargolini, 2006) by non-linearly combining `GridCells` and `HeadDirectionCells` using `FeedForwardLayer`.\n",
     "\n",

demos/decoding_position_example.ipynb

@@ -5,7 +5,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/decoding_position_example.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/decoding_position_example.ipynb)"
    ]
   },
   {

demos/deep_learning_example.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Deep Learning with RatInABox [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/deep_learning_example.ipynb)\n",
+    "# Deep Learning with RatInABox [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/deep_learning_example.ipynb)\n",
     "\n",
     "In this tutorial we will show how `NeuralNetworkNeurons` can be used as general function approximators to make RatInABox `Neurons` classes which can be \"trained\" to represent any function. \n",
     "\n",

demos/extensive_example.ipynb

@@ -5,7 +5,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/extensive_example.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/extensive_example.ipynb)"
    ]
   },
   {

demos/paper_figures.ipynb

@@ -5,7 +5,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/paper_figures.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/paper_figures.ipynb)"
    ]
   },
   {

demos/path_integration_example.ipynb

@@ -5,7 +5,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/path_integration_example.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/path_integration_example.ipynb)"
    ]
   },
   {

demos/readme_figures.ipynb

@@ -5,7 +5,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/dev/demos/readme_figures.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/readme_figures.ipynb)"
    ]
   },
   {