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index.html

@@ -321,17 +321,6 @@
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@@ -962,7 +940,7 @@
 <h1 id="felice">Felice</h1>
 <p>This project provides a <a href="https://github.com/google/jax">JAX</a> implementation of the different neuron models in felice</p>
 <h2 id="overview">Overview</h2>
-<p>The framework is built on top of <a href="">EventPropJax</a> and leverages JAX's automatic differentiation for efficient simulation and training of SNNs using event-based exact gradients.</p>
+<p>The framework is built on top of diffrax and leverages JAX's automatic differentiation for efficient simulation and training of analogue models.</p>
 <h3 id="key-features">Key Features</h3>
 <ul>
 <li><strong>Delay learning</strong></li>
@@ -981,37 +959,6 @@
 <p>For GPU acceleration with CUDA 13:</p>
 <div class="language-bash highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>uv<span class="w"> </span>sync<span class="w"> </span>--extra<span class="w"> </span>cuda
 </span></code></pre></div>
-<h2 id="quick-start">Quick Start</h2>
-<p>Here's a simple example using the WereRabbit neuron model:</p>
-<div class="language-py highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="kn">import</span><span class="w"> </span><span class="nn">diffrax</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">dfx</span>
-</span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a><span class="kn">import</span><span class="w"> </span><span class="nn">jax.numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">jnp</span>
-</span><span id="__span-2-3"><a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a><span class="kn">import</span><span class="w"> </span><span class="nn">jax.random</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">jrand</span>
-</span><span id="__span-2-4"><a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a><span class="kn">from</span><span class="w"> </span><span class="nn">eventpropjax.evnn</span><span class="w"> </span><span class="kn">import</span> <span class="n">FFEvNN</span>
-</span><span id="__span-2-5"><a id="__codelineno-2-5" name="__codelineno-2-5" href="#__codelineno-2-5"></a><span class="kn">from</span><span class="w"> </span><span class="nn">felice.neuron_models</span><span class="w"> </span><span class="kn">import</span> <span class="n">WereRabbit</span>
-</span><span id="__span-2-6"><a id="__codelineno-2-6" name="__codelineno-2-6" href="#__codelineno-2-6"></a>
-</span><span id="__span-2-7"><a id="__codelineno-2-7" name="__codelineno-2-7" href="#__codelineno-2-7"></a><span class="c1"># Initialize random key and parameters</span>
-</span><span id="__span-2-8"><a id="__codelineno-2-8" name="__codelineno-2-8" href="#__codelineno-2-8"></a><span class="n">key</span> <span class="o">=</span> <span class="n">jrand</span><span class="o">.</span><span class="n">key</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-</span><span id="__span-2-9"><a id="__codelineno-2-9" name="__codelineno-2-9" href="#__codelineno-2-9"></a><span class="n">max_time</span> <span class="o">=</span> <span class="mf">300e-3</span>
-</span><span id="__span-2-10"><a id="__codelineno-2-10" name="__codelineno-2-10" href="#__codelineno-2-10"></a>
-</span><span id="__span-2-11"><a id="__codelineno-2-11" name="__codelineno-2-11" href="#__codelineno-2-11"></a><span class="c1"># Create a feedforward event-driven neural network</span>
-</span><span id="__span-2-12"><a id="__codelineno-2-12" name="__codelineno-2-12" href="#__codelineno-2-12"></a><span class="n">snn</span> <span class="o">=</span> <span class="n">FFEvNN</span><span class="p">(</span>
-</span><span id="__span-2-13"><a id="__codelineno-2-13" name="__codelineno-2-13" href="#__codelineno-2-13"></a>    <span class="n">layers</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-</span><span id="__span-2-14"><a id="__codelineno-2-14" name="__codelineno-2-14" href="#__codelineno-2-14"></a>    <span class="n">in_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-</span><span id="__span-2-15"><a id="__codelineno-2-15" name="__codelineno-2-15" href="#__codelineno-2-15"></a>    <span class="n">neuron_model</span><span class="o">=</span><span class="n">WereRabbit</span><span class="p">,</span>
-</span><span id="__span-2-16"><a id="__codelineno-2-16" name="__codelineno-2-16" href="#__codelineno-2-16"></a>    <span class="n">solver</span><span class="o">=</span><span class="n">dfx</span><span class="o">.</span><span class="n">Tsit5</span><span class="p">(),</span>
-</span><span id="__span-2-17"><a id="__codelineno-2-17" name="__codelineno-2-17" href="#__codelineno-2-17"></a>    <span class="n">max_solver_time</span><span class="o">=</span><span class="n">max_time</span><span class="p">,</span>
-</span><span id="__span-2-18"><a id="__codelineno-2-18" name="__codelineno-2-18" href="#__codelineno-2-18"></a>    <span class="n">key</span><span class="o">=</span><span class="n">key</span><span class="p">,</span>
-</span><span id="__span-2-19"><a id="__codelineno-2-19" name="__codelineno-2-19" href="#__codelineno-2-19"></a>    <span class="n">max_event_steps</span><span class="o">=</span><span class="mi">1000000</span><span class="p">,</span>
-</span><span id="__span-2-20"><a id="__codelineno-2-20" name="__codelineno-2-20" href="#__codelineno-2-20"></a>    <span class="n">solver_stepsize</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">,</span>
-</span><span id="__span-2-21"><a id="__codelineno-2-21" name="__codelineno-2-21" href="#__codelineno-2-21"></a>    <span class="n">rtol</span><span class="o">=</span><span class="mf">10.0</span><span class="p">,</span>
-</span><span id="__span-2-22"><a id="__codelineno-2-22" name="__codelineno-2-22" href="#__codelineno-2-22"></a>    <span class="n">atol</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
-</span><span id="__span-2-23"><a id="__codelineno-2-23" name="__codelineno-2-23" href="#__codelineno-2-23"></a>    <span class="n">Ibias</span><span class="o">=</span><span class="mf">30e-12</span><span class="p">,</span>
-</span><span id="__span-2-24"><a id="__codelineno-2-24" name="__codelineno-2-24" href="#__codelineno-2-24"></a><span class="p">)</span>
-</span><span id="__span-2-25"><a id="__codelineno-2-25" name="__codelineno-2-25" href="#__codelineno-2-25"></a>
-</span><span id="__span-2-26"><a id="__codelineno-2-26" name="__codelineno-2-26" href="#__codelineno-2-26"></a><span class="c1"># Simulate with input spikes</span>
-</span><span id="__span-2-27"><a id="__codelineno-2-27" name="__codelineno-2-27" href="#__codelineno-2-27"></a><span class="n">in_spikes</span> <span class="o">=</span> <span class="n">jnp</span><span class="o">.</span><span class="n">asarray</span><span class="p">([[</span><span class="mf">0.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.157</span><span class="p">]])</span>
-</span><span id="__span-2-28"><a id="__codelineno-2-28" name="__codelineno-2-28" href="#__codelineno-2-28"></a><span class="n">spikes</span> <span class="o">=</span> <span class="n">snn</span><span class="o">.</span><span class="n">spikes_until_t</span><span class="p">(</span><span class="n">in_spikes</span><span class="p">,</span> <span class="n">max_time</span><span class="p">)</span>
-</span></code></pre></div>
 <p>See the <a href="scripts/examples/neuron_models/">examples</a> directory for more detailed usage examples.</p>
 
 

neuron_models/index.html

@@ -860,6 +860,11 @@
 <td>Exponential Integrate-and-Fire neuron model</td>
 <td>...</td>
 </tr>
+<tr>
+<td><a href="lif/">LIF</a></td>
+<td>Leaky Integrate-and-Fire neuron model</td>
+<td>...</td>
+</tr>
 </tbody>
 </table>
 

neuron_models/lif/index.html

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+<h1 id="lif">LIF</h1>
+<h2 id="circuit-design">Circuit Design</h2>
+<p>W/L = 4/3</p>
+<h2 id="circuit-simulation">Circuit Simulation</h2>
+<p><img alt="lif, output plot" src="/docs-site/docs/img/lif_plot.png" /> Fig.1 The dynamics of leaky integrate and fire neuron. The grey signal is the input spikes, the yellow signal is the membrane potential and the dark blue is the output spikes from the neuron.</p>
+<h2 id="referennces">Referennces</h2>
+<ol>
+<li>Sourikopoulos I, Hedayat S, Loyez C, Danneville F, Hoel V, Mercier E and Cappy A (2017) A 4-fJ/Spike Artificial Neuron in 65 nm CMOS Technology. Front. Neurosci. 11:123. doi: 10.3389/fnins.2017.00123</li>
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