Fix typos in paper (#113)

paper/sections/delays/Delays.md

@@ -19,7 +19,7 @@ In this work we propose a delay learning algorithm that is simple and efficient.
 
 The DDL is, mainly, based on a 1D version of the spatial transformer (STN) network \citealp{JSZK2015}. The STN is a differentiable module that can be added into convolutional neural networks (CNNs) architectures to empower them with the ability to spatially transform feature maps in a differentiable way. This addition leads to CNNs models that are invariant to various spatial transformations like translation, scaling and rotation. Image manipulations are inherently  not differentiable, because pixels are a discrete. However, this problem is overcome by the application of an interpolation  (for example bi-linear) after the spatial transformation. 
 
-The DDL is a 1D version of the spatial transformer where the only transformation done is translation. Translation of a spike along the time dimension can be thought of as a translation of a pixel along the spatial coordinates. The general affine transformation matrix for the 2D case takes the form in the following euqation:
+The DDL is a 1D version of the spatial transformer where the only transformation done is translation. Translation of a spike along the time dimension can be thought of as a translation of a pixel along the spatial coordinates. The general affine transformation matrix for the 2D case takes the form in the following equation:
 
 $$ \begin{bmatrix} 
 	sr_1 & sr_2 & t_x\\

paper/sections/discussion.md

@@ -1,2 +1,2 @@
 ## Participants and participation
-* Insentives, allocating credit
+* Incentives, allocating credit

paper/sections/new_inh_model.md

@@ -4,7 +4,7 @@
 Why a more biological inspired model? 
 - Existance of both ITDs and ILDs for azimuthal sounds localization
 - Problem of ITD sensitivity: their duration is in the range of microseonds whereas neuronal dynamics work in the millisecond range.
-- Jeffress model existance in mammals is not sure: 3 main critical issues
+- Jeffress model existence in mammals is not sure: 3 main critical issues
   - axonal delay lines absence in mammalian MSO
   - contralateral inhibition role not considered in Jeffress
   - peaks of MSO responses outside physiological range: slopes as the encoding part of the curves

paper/sections/science.md

@@ -47,7 +47,7 @@
 * Third, use filter-and-fire neurons (e.g. M = 3 connections per axon). 
 
 ## Learning delays 
-Many studies which incoroprate axonal and/or dendritic delays include them as non-learnable parameters (refs) like our base model. Here we explore how these phase delays can be learned through two approaches.
+Many studies which incorporate axonal and/or dendritic delays include them as non-learnable parameters (refs) like our base model. Here we explore how these phase delays can be learned through two approaches.
 
 ### With dilated convolutions with learnable spacings (DCLS)
 First, with DCLS (Hammouamri et al., 2023; Khalfaoui-Hassani et al., 2023).
@@ -57,7 +57,7 @@ Key points:
 * Learns both weights and delays. 
 * Visualisation of results: 
     * x - learned delay, y - learned weight.
-    * Hidden units seperate data spatio-temporally. 
+    * Hidden units separate data spatio-temporally. 
 
 ### With a differentiable delay layer (DDL)
 Second, by introducing a differentiable delay layer.