add doc figures

Author: rgutzen

.gitignore

@@ -14,9 +14,9 @@ temp_*
 /reports/
 
 
-# images
-*.png
-*.jpg
+# # images
+# *.png
+# *.jpg
 
 # logs
 logs/

docs/assets/dynamical_systems_equation.png

@@ -6,16 +6,9 @@ DynVision models the dynamics of neural activity using continuous-time different
 
 The core of DynVision's dynamics is the following differential equation:
 
-$$\tau \frac{dx}{dt} = -x + \Phi[f(t, r_n, r_{n-1})]$$
-
-Where:
-- $\tau$ is the time constant of the neural dynamics
-- $x$ is the neural activity
-- $\Phi$ is a nonlinearity
-- $f(t, r_n, r_{n-1})$ represents the input to the neuron, which may include:
-  - External input ($I(t)$)
-  - Feedforward input from previous layers ($J_{FF} \cdot r_{n-1}(t-\Delta_{FF})$)
-  - Recurrent input from the same layer ($J_{RC} \cdot r_n(t-\Delta_{RC})$)
+<p align="center">
+  <img src="docs/assets/dynamical_systems_equation.png" alt="Dynamical Systems ODE" width="800"/>
+</p>
 
 This equation is solved numerically using one of the available solvers in DynVision.
 
@@ -27,7 +20,7 @@ DynVision provides two main solvers for the neural dynamics equation:
 
 The Euler method is a first-order numerical procedure for solving ordinary differential equations (ODEs). It provides a simple approximation:
 
-$$x(t+dt) = x(t) + \frac{dt}{\tau} \cdot [-x(t) + W(x(t))]$$
+$$x(t+dt) = x(t) + \frac{dt}{\tau} \cdot [-x(t) + J(x(t))]$$
 
 **Advantages**:
 - Computational efficiency

docs/assets/overview.png

@@ -8,6 +8,10 @@ Recurrent connections are abundant in the primate visual system. In the ventral
 
 DynVision implements several types of recurrent connections, each with different computational properties and biological interpretations.
 
+<p align="center">
+  <img src="docs/assets/recurrence_types.png" alt="Recurrence Types" width="800"/>
+</p>
+
 ## Available Recurrence Types
 
 ### 1. Self Recurrence

docs/assets/recurrence_types.png

@@ -9,7 +9,7 @@ This section provides step-by-step tutorials to help beginners learn how to use
 - [**Custom Model Creation**](custom-model-creation.md): Create your own neural network architecture
 <!-- - [**Experiment Design**](experiment-design.md): Design and run experiments with Snakemake -->
 
-**...more to come***
+*...more to come...*
 
 <!-- ## Working with Data