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<section class="tex2jax_ignore mathjax_ignore" id="ratinabox">
<h1>RatInABox<a class="headerlink" href="#ratinabox" title="Link to this heading">#</a></h1>
<p><code class="docutils literal notranslate"><span class="pre">RatInABox</span></code> (see <a class="reference external" href="https://www.biorxiv.org/content/10.1101/2022.08.10.503541v3">paper</a>) is a toolkit for generating synthetic behaviour and neural data for spatially and/or velocity selective cell types in complex continuous environments.</p>
<p>With <code class="docutils literal notranslate"><span class="pre">RatInABox</span></code> you can:</p>
<ul class="simple">
<li><p><strong>Generate realistic trajectories</strong> for rats exploring complex 1 and 2D environments under a smooth random policy, an external control signal, or your own trajectory data.</p></li>
<li><p><strong>Generate artificial neuronal data</strong> for various location- or velocity-selective cells found in the brain (e.g., but not limited to, Hippocampal cell types), or build your own more complex cell types.</p></li>
<li><p><strong>Build and train complex multi-layer networks</strong> of cells, powered by data generated with <code class="docutils literal notranslate"><span class="pre">RatInABox</span></code>.</p></li>
</ul>
<a class="reference internal image-reference" href="https://raw.githubusercontent.com/RatInABox-Lab/RatInABox/main/.images/readme/ratinabox.gif"><img alt="https://raw.githubusercontent.com/RatInABox-Lab/RatInABox/main/.images/readme/ratinabox.gif" src="https://raw.githubusercontent.com/RatInABox-Lab/RatInABox/main/.images/readme/ratinabox.gif" style="width: 850px;" />
</a>
<p><code class="docutils literal notranslate"><span class="pre">RatInABox</span></code> is an open source project welcoming <a class="reference internal" href="#contribute"><span class="std std-ref">contributions</span></a>. If you use <code class="docutils literal notranslate"><span class="pre">RatInABox</span></code> please <a class="reference internal" href="#cite"><span class="std std-ref">cite</span></a> the paper and consider giving this repository a star ☆. It contains three classes:</p>
<ol class="arabic simple">
<li><p><code class="docutils literal notranslate"><span class="pre">Environment()</span></code>📦: The environment/maze (or “box”) that the agent lives in. 1- or 2-dimensional.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">Agent()</span></code> 🐀: The agent (or “rat”) moving around the <code class="docutils literal notranslate"><span class="pre">Environment</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">Neurons()</span></code> 🧠: A population of neurons with firing rates determined by the state (position and velocity) of the <code class="docutils literal notranslate"><span class="pre">Agent</span></code>. Make your own or use one of our premade cell types including:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">PlaceCells()</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">GridCells()</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">BoundaryVectorCells()</span></code> (egocentric or allocentric)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">ObjectVectorCells()</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">VelocityCells()</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">SpeedCells()</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">FieldOfViewNeurons()</span></code> (egocentric encoding of what the <code class="docutils literal notranslate"><span class="pre">Agent</span></code> can see)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">RandomSpatialNeurons()</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">HeadDirectionCells()</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">FeedForwardLayer()</span></code> (a generic class analagous to a feedforward layer in a deep neural network)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">NeuralNetworkNeurons()</span></code> (a generic class analagous to a deep neural network)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">SuccessorFeatures()</span></code></p></li>
<li><p>…</p></li>
</ul>
</li>
</ol>
<p>The top animation shows an example use case: an <code class="docutils literal notranslate"><span class="pre">Agent</span></code> randomly explores a 2D <code class="docutils literal notranslate"><span class="pre">Environment</span></code> with a wall. Three populations of <code class="docutils literal notranslate"><span class="pre">Neurons</span></code> (<code class="docutils literal notranslate"><span class="pre">PlaceCells</span></code>, <code class="docutils literal notranslate"><span class="pre">GridCells</span></code>, <code class="docutils literal notranslate"><span class="pre">BoundaryVectorCells</span></code>) fire according to the receptive fields shown. All data is saved into the history for downstream use. <code class="docutils literal notranslate"><span class="pre">RatInABox</span></code> is fully continuous in space; this means that position and neural firing rates are calculated rapidly online with float precision rather than pre-calculated over a discretised mesh. <code class="docutils literal notranslate"><span class="pre">RatInABox</span></code> is flexibly discretised in time; <code class="docutils literal notranslate"><span class="pre">dt</span></code> can be set by the user (defaulting to 10 ms) depending on requirements.</p>
<section id="key-features">
<h2>Key features<a class="headerlink" href="#key-features" title="Link to this heading">#</a></h2>
<ul class="simple">
<li><p><strong>Non-specific</strong>: Trajectories can be randomly generated, imported, or adaptively controlled making <code class="docutils literal notranslate"><span class="pre">RatInABox</span></code> a powerful engine for many tasks involving continuous motion (e.g. control theory or reinforcement learning).</p></li>
<li><p><strong>Biological</strong>: Simulate large populations of spatially and/or velocity modulated cell types. Neurons can be rate based or spiking. The random motion model is fitted to match real rodent motion.</p></li>
<li><p><strong>Flexible</strong>: Simulate environments in 1D or 2D with arbitrarily wall, boundary and hole arrangements. Combine premade or bespoke <code class="docutils literal notranslate"><span class="pre">Neurons</span></code> classes into arbitrary deep networks (examples given).</p></li>
<li><p><strong>Fast</strong>: Simulating 1 minute of exploration in a 2D environment with 100 place cells (dt=10 ms) take just 2 seconds on a laptop (no GPU needed).</p></li>
<li><p><strong>Precise</strong>: No more prediscretised positions, tabular state spaces, or jerky movement policies. It’s all continuous.</p></li>
<li><p><strong>Easy</strong>: Sensible default parameters mean you can have realisitic simulation data to work with in <10 lines of code.</p></li>
<li><p><strong>Visual</strong> Plot or animate trajectories, firing rate timeseries’, spike rasters, receptive fields, heat maps, velocity histograms…using the plotting functions (<a class="reference external" href="https://github.com/RatInABox-Lab/RatInABox/blob/main/demos/list_of_plotting_fuctions.md">summarised here</a>).</p></li>
</ul>
<!--
## Announcement about support for OpenAI's `gymnasium` <img src="https://raw.githubusercontent.com/RatInABox-Lab/RatInABox/main/.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/main/ratinabox/contribs/TaskEnv_example_files/TaskEnvironment_basics.md). -->
</section>
<section id="contribute">
<h2>Contribute<a class="headerlink" href="#contribute" title="Link to this heading">#</a></h2>
<p><code class="docutils literal notranslate"><span class="pre">RatInABox</span></code> is open source project and we actively encourage all contributions from example bug fixes to documentation or new cell types. Feel free to make a pull request (you will need to fork the repository first) or raise and issue.</p>
<p>We have a dedicated <a class="reference external" href="https://github.com/RatInABox-Lab/RatInABox/tree/main/ratinabox/contribs">contribs</a> directory where you can safely add awesome scripts and new <code class="docutils literal notranslate"><span class="pre">Neurons</span></code> classes etc.</p>
<p>Questions? Just ask! Ideally via opening an issue so others can see the answer too.</p>
<p>Thanks to all contributors so far:
<img alt="GitHub Contributors Image" src="https://contrib.rocks/image?repo=RatInABox-Lab/RatInABox" /></p>
</section>
<section id="cite">
<h2>Cite <a class="reference external" href="https://doi.org/10.1101/2022.08.10.503541"><img alt="" src="http://img.shields.io/badge/bioRxiv-10.1101/2022.08.10.503541-B31B1B.svg" /></a><a class="headerlink" href="#cite" title="Link to this heading">#</a></h2>
<p>If you use <code class="docutils literal notranslate"><span class="pre">RatInABox</span></code> in your research or educational material, please cite the work as follows:
Bibtex:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@article</span><span class="p">{</span><span class="n">ratinabox2022</span><span class="p">,</span>
<span class="n">doi</span> <span class="o">=</span> <span class="p">{</span><span class="mf">10.1101</span><span class="o">/</span><span class="mf">2022.08.10.503541</span><span class="p">},</span>
<span class="n">url</span> <span class="o">=</span> <span class="p">{</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">doi</span><span class="o">.</span><span class="n">org</span><span class="o">/</span><span class="mf">10.1101</span><span class="o">%</span><span class="mi">2</span><span class="n">F2022</span><span class="mf">.08.10.503541</span><span class="p">},</span>
<span class="n">year</span> <span class="o">=</span> <span class="mi">2022</span><span class="p">,</span>
<span class="n">month</span> <span class="o">=</span> <span class="p">{</span><span class="n">aug</span><span class="p">},</span>
<span class="n">publisher</span> <span class="o">=</span> <span class="p">{</span><span class="n">Cold</span> <span class="n">Spring</span> <span class="n">Harbor</span> <span class="n">Laboratory</span><span class="p">},</span>
<span class="n">author</span> <span class="o">=</span> <span class="p">{</span><span class="n">Tom</span> <span class="n">M</span> <span class="n">George</span> <span class="ow">and</span> <span class="n">William</span> <span class="n">de</span> <span class="n">Cothi</span> <span class="ow">and</span> <span class="n">Claudia</span> <span class="n">Clopath</span> <span class="ow">and</span> <span class="n">Kimberly</span> <span class="n">Stachenfeld</span> <span class="ow">and</span> <span class="n">Caswell</span> <span class="n">Barry</span><span class="p">},</span>
<span class="n">title</span> <span class="o">=</span> <span class="p">{{</span><span class="n">RatInABox</span><span class="p">}:</span> <span class="n">A</span> <span class="n">toolkit</span> <span class="k">for</span> <span class="n">modelling</span> <span class="n">locomotion</span> <span class="ow">and</span> <span class="n">neuronal</span> <span class="n">activity</span> <span class="ow">in</span> <span class="n">continuous</span> <span class="n">environments</span><span class="p">}</span>
<span class="p">}</span>
</pre></div>
</div>
<p>Formatted:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Tom</span> <span class="n">M</span> <span class="n">George</span><span class="p">,</span> <span class="n">William</span> <span class="n">de</span> <span class="n">Cothi</span><span class="p">,</span> <span class="n">Claudia</span> <span class="n">Clopath</span><span class="p">,</span> <span class="n">Kimberly</span> <span class="n">Stachenfeld</span><span class="p">,</span> <span class="n">Caswell</span> <span class="n">Barry</span><span class="o">.</span> <span class="s2">"RatInABox: A toolkit for modelling locomotion and neuronal activity in continuous environments"</span> <span class="p">(</span><span class="mi">2022</span><span class="p">)</span><span class="o">.</span>
</pre></div>
</div>
<p>The research paper corresponding to the above citation can be found <a class="reference external" href="https://www.biorxiv.org/content/10.1101/2022.08.10.503541v3">here</a>.</p>
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