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diff --git a/docs/conf.py b/docs/conf.py
@@ -23,7 +23,7 @@
 author = 'Pascal Sager'
 
 # The full version, including alpha/beta/rc tags
-release = '00.00.02'
+release = '01.00.00'
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

diff --git a/docs/index.rst b/docs/index.rst
@@ -13,30 +13,16 @@ Please find the abstract, the full thesis, and videos presenting the results bel
    :maxdepth: 1
    :caption: Contents
 
+   presentation
+   thesis
    results/final_results
 
+---
 
+.. raw:: html
 
-**Download the** :download:`thesis <_static/results/msc_thesis-Pascal_Sager-V1_0.pdf>` **as PDF.**
-
-
-Abstract
---------
-In the past decade, deep learning has established itself as state-of-the-art technology in various automatic image analysis tasks.
-Despite impressive results, this technology has several limitations, notably its limited robustness to noise, constrained transformation invariance during object recognition and reliance on a substantial amount of training data.
-Conversely, the human brain does not suffer from these limitations due to its non-sequential processing of extracted image features and its ability to perceive visual scenes holistically, i.e. interpret it as more than the sum of its part, as outlined by Gestalt psychology.
-This capability stems from the brain's ability to establish internal consistency between each connected cell pair through self-organisation and localised learning, i.e. a consensus is achieved across all features through mutual cell support. This mechanism solves the problem of ``early commitment'' inherent in deep networks as they rely on a global error correction algorithm to establish consistency at a single point between prediction and teaching signal.
-
-This thesis builds upon these insights and proposes a novel image-processing framework inspired by the human brain's functionality.
-Accordingly, a significant part of this thesis is devoted to identifying and interpreting neuroscientific findings.
-These findings are analysed and translated into a computational framework, thereby linking each model component to the corresponding biological mechanism.
-
-The framework consists of three components: The sensor system *S0*,  responsible for extracting low-level features from the images; the feature-building stage *S1*, which uses lateral (intra-layer) connections to form neuron groups, so-called net fragments,  fostering mutual support to stabilise known patterns; the prototype stage *S2*, which maps the formed net fragments to object prototypes using projection fibres and provides feedback to *S1*.
-The iterative projection process between *S1* and *S2* lasts until consistency is achieved at every point in the network, i.e. until cells and synapses have reached a stable attractor state.
-
-While prior research has demonstrated the efficiency of projection fibres, implementing net fragments still needs to be explored.
-Consequently, this thesis analyses the implementation of this component in detail and discusses it by conducting experiments with a simple dataset based on straight lines.
-The experimental findings demonstrate that lateral connections trained with Hebbian learning can facilitate cell support effectively.
-The network exhibits significant robustness using cell support and can deactivate up to :math:`91.7\%` of unwanted cell activity triggered by noise signals. Furthermore, lateral support can restore discontinuous lines, demonstrating the network's ability to deal with occluded objects. With a range of lateral connections of :math:`11` pixels, interruptions of up to :math:`8` pixels can be reconstructed, and with additional feedback from *S2*, even interruptions of up to :math:`20` pixels can be restored. Improving the proposed framework can potentially reduce several weaknesses of conventional neural networks in the future and is considered a promising alternative research direction.
+  <embed>
+      <p><a href="_static/results/msc_thesis-Pascal_Sager-V1_0.pdf" target="_blank">Download</a> the thesis as PDF.</p>
+  </embed>
 
 
diff --git a/docs/presentation.rst b/docs/presentation.rst
@@ -0,0 +1,5 @@
+Presentation
+============
+
+.. video:: _static/results/thesis_defence_presentation.mp4
+  :width: 800
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