diff --git a/Project.toml b/Project.toml
index b629b063..50823c8f 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "ReservoirComputing"
 uuid = "7c2d2b1e-3dd4-11ea-355a-8f6a8116e294"
 authors = ["Francesco Martinuzzi"]
-version = "0.10.9"
+version = "0.10.10"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
diff --git a/docs/src/api/inits.md b/docs/src/api/inits.md
index bd52797e..142049c6 100644
--- a/docs/src/api/inits.md
+++ b/docs/src/api/inits.md
@@ -8,6 +8,8 @@
     informed_init
     minimal_init
     chebyshev_mapping
+    logistic_mapping
+    modified_lm
 ```
 
 ## Reservoirs
diff --git a/src/ReservoirComputing.jl b/src/ReservoirComputing.jl
index 61c505b8..153ddd72 100644
--- a/src/ReservoirComputing.jl
+++ b/src/ReservoirComputing.jl
@@ -38,7 +38,8 @@ include("reca/reca_input_encodings.jl")
 export NLADefault, NLAT1, NLAT2, NLAT3
 export StandardStates, ExtendedStates, PaddedStates, PaddedExtendedStates
 export StandardRidge
-export scaled_rand, weighted_init, informed_init, minimal_init, chebyshev_mapping
+export scaled_rand, weighted_init, informed_init, minimal_init, chebyshev_mapping,
+       logistic_mapping, modified_lm
 export rand_sparse, delay_line, delay_line_backward, cycle_jumps,
        simple_cycle, pseudo_svd, chaotic_init
 export RNN, MRNN, GRU, GRUParams, FullyGated, Minimal
diff --git a/src/esn/esn_inits.jl b/src/esn/esn_inits.jl
index f9ad66c2..e8a9c6da 100644
--- a/src/esn/esn_inits.jl
+++ b/src/esn/esn_inits.jl
@@ -308,7 +308,8 @@ Subsequent rows are generated by applying the mapping:
     from WeightInitializers.
   - `T`: Type of the elements in the reservoir matrix.
     Default is `Float32`.
-  - `dims`: Dimensions of the matrix. Should follow `res_size x in_size`. Here, res_size is assumed to be K+1.
+  - `dims`: Dimensions of the matrix. Should follow `res_size x in_size`.
+    `res_size` is assumed to be K+1.
 
 # Keyword arguments
 
@@ -362,6 +363,189 @@ function chebyshev_mapping(rng::AbstractRNG, ::Type{T}, dims::Integer...;
     return return_sparse ? sparse(input_matrix) : input_matrix
 end
 
+@doc raw"""
+    logistic_mapping(rng::AbstractRNG, ::Type{T}, dims::Integer...;
+        amplitude=0.3, sine_divisor=5.9, logistic_parameter = 3.7,
+        return_sparse=true)
+
+Generate an input weight matrix using a logistic mapping [^wang2022].The first
+row is initialized using a sine function:
+
+```math
+    W(1, j) = amplitude * sin(j * π / (sine_divisor * in_size))
+```
+
+for each input index `j`, with `in_size` being the number of columns provided in `dims`. Subsequent rows
+are generated recursively using the logistic map recurrence:
+
+```math
+    W(i+1, j) = logistic_parameter * W(i, j) * (1 - W(i, j))
+```
+
+# Arguments
+  - `rng`: Random number generator. Default is `Utils.default_rng()`
+    from WeightInitializers.
+  - `T`: Type of the elements in the reservoir matrix.
+    Default is `Float32`.
+  - `dims`: Dimensions of the matrix. Should follow `res_size x in_size`.
+
+# keyword arguments
+  - `amplitude`: Scaling parameter used in the sine initialization of the
+    first row. Default is 0.3.
+  - `sine_divisor`: Parameter used to adjust the phase in the sine initialization.
+    Default is 5.9.
+  - `logistic_parameter`: The parameter in the logistic mapping recurrence that
+    governs the dynamics. Default is 3.7.
+  - `return_sparse`: If `true`, returns the resulting matrix as a sparse matrix.
+    Default is `false`.
+
+# Examples
+
+```jldoctest
+julia> logistic_mapping(8, 3)
+8×3 Matrix{Float32}:
+ 0.0529682  0.104272  0.1523
+ 0.185602   0.345578  0.477687
+ 0.559268   0.836769  0.923158
+ 0.912003   0.50537   0.262468
+ 0.296938   0.924893  0.716241
+ 0.772434   0.257023  0.751987
+ 0.650385   0.70656   0.69006
+ 0.841322   0.767132  0.791346
+
+```
+
+
+[^wang2022]: Wang, Heshan, et al. "Echo state network with logistic
+    mapping and bias dropout for time series prediction."
+    Neurocomputing 489 (2022): 196-210.
+"""
+function logistic_mapping(rng::AbstractRNG, ::Type{T}, dims::Integer...;
+        amplitude::AbstractFloat=0.3, sine_divisor::AbstractFloat=5.9,
+        logistic_parameter::AbstractFloat=3.7,
+        return_sparse::Bool=false) where {T <: Number}
+    input_matrix = DeviceAgnostic.zeros(rng, T, dims...)
+    num_rows, num_columns = dims[1], dims[2]
+    for col in 1:num_columns
+        input_matrix[1, col] = amplitude * sin(col * pi / (sine_divisor * num_columns))
+    end
+    for row in 2:num_rows
+        for col in 1:num_columns
+            previous_value = input_matrix[row - 1, col]
+            input_matrix[row, col] = logistic_parameter * previous_value *
+                                     (1 - previous_value)
+        end
+    end
+
+    return return_sparse ? sparse(input_matrix) : input_matrix
+end
+
+@doc raw"""
+    modified_lm([rng], [T], dims...;
+        factor, amplitude=0.3, sine_divisor=5.9, logistic_parameter=2.35,
+        return_sparse=true)
+
+Generate a input weight matrix based on the logistic mapping [^viehweg2025]. The
+matrix is built so that each input is transformed into a high-dimensional feature
+space via a recursive logistic map. For each input, a chain of weights is generated
+as follows:
+- The first element of the chain is initialized using a sine function:
+
+```math
+      W(1,j) = amplitude * sin( (j * π) / (factor * n * sine_divisor) )
+```
+  where `j` is the index corresponding to the input and `n` is the number of inputs.
+
+- Subsequent elements are recursively computed using the logistic mapping:
+
+```math
+      W(i+1,j) = logistic_parameter * W(i,j) * (1 - W(i,j))
+```
+
+The resulting matrix has dimensions `(factor * in_size) x in_size`, where
+`in_size` corresponds to the number of columns provided in `dims`.
+If the provided number of rows does not match `factor * in_size` 
+the number of rows is overridden.
+
+# Arguments
+  - `rng`: Random number generator. Default is `Utils.default_rng()`
+    from WeightInitializers.
+  - `T`: Type of the elements in the reservoir matrix.
+    Default is `Float32`.
+  - `dims`: Dimensions of the matrix. Should follow `res_size x in_size`.
+
+# keyword arguments
+  - `factor`: The number of logistic map iterations (chain length) per input,
+    determining the number of rows per input.
+  - `amplitude`: Scaling parameter A for the sine-based initialization of
+    the first element in each logistic chain. Default is 0.3.
+  - `sine_divisor`: Parameter B used to adjust the phase in the sine initialization.
+    Default is 5.9.
+  - `logistic_parameter`: The parameter r in the logistic recurrence that governs
+    the chain dynamics. Default is 2.35.
+  - `return_sparse`: If `true`, returns the resulting matrix as a sparse matrix.
+    Default is `true`.
+
+# Examples
+
+```jldoctest
+julia> modified_lm(20, 10; factor=2)
+20×10 SparseArrays.SparseMatrixCSC{Float32, Int64} with 18 stored entries:
+⎡⢠⠀⠀⠀⠀⎤
+⎢⠀⢣⠀⠀⠀⎥
+⎢⠀⠀⢣⠀⠀⎥
+⎢⠀⠀⠀⢣⠀⎥
+⎣⠀⠀⠀⠀⢣⎦
+
+julia> modified_lm(12, 4; factor=3)
+12×4 SparseArrays.SparseMatrixCSC{Float32, Int64} with 9 stored entries:
+  ⋅    ⋅          ⋅          ⋅ 
+  ⋅    ⋅          ⋅          ⋅ 
+  ⋅    ⋅          ⋅          ⋅ 
+  ⋅   0.0133075   ⋅          ⋅ 
+  ⋅   0.0308564   ⋅          ⋅ 
+  ⋅   0.070275    ⋅          ⋅ 
+  ⋅    ⋅         0.0265887   ⋅ 
+  ⋅    ⋅         0.0608222   ⋅ 
+  ⋅    ⋅         0.134239    ⋅ 
+  ⋅    ⋅          ⋅         0.0398177
+  ⋅    ⋅          ⋅         0.0898457
+  ⋅    ⋅          ⋅         0.192168
+
+```
+
+[^viehweg2025]: Viehweg, Johannes, Constanze Poll, and Patrick Mäder.
+    "Deterministic Reservoir Computing for Chaotic Time Series Prediction."
+    arXiv preprint arXiv:2501.15615 (2025).
+"""
+function modified_lm(rng::AbstractRNG, ::Type{T}, dims::Integer...;
+        factor::Integer, amplitude::AbstractFloat=0.3,
+        sine_divisor::AbstractFloat=5.9, logistic_parameter::AbstractFloat=2.35,
+        return_sparse::Bool=true) where {T <: Number}
+    num_columns = dims[2]
+    expected_num_rows = factor * num_columns
+    if dims[1] != expected_num_rows
+        @warn """\n
+        Provided dims[1] ($(dims[1])) is not equal to factor*num_columns ($expected_num_rows).
+        Overriding number of rows to $expected_num_rows. \n
+        """
+    end
+    output_matrix = DeviceAgnostic.zeros(rng, T, expected_num_rows, num_columns)
+    for col in 1:num_columns
+        base_row = (col - 1) * factor + 1
+        output_matrix[base_row, col] = amplitude * sin(((col - 1) * pi) /
+                                           (factor * num_columns * sine_divisor))
+        for j in 1:(factor - 1)
+            current_row = base_row + j
+            previous_value = output_matrix[current_row - 1, col]
+            output_matrix[current_row, col] = logistic_parameter * previous_value *
+                                              (1 - previous_value)
+        end
+    end
+
+    return return_sparse ? sparse(output_matrix) : output_matrix
+end
+
 ### reservoirs
 
 """
@@ -752,23 +936,6 @@ function get_sparsity(M, dim)
     return size(M[M .!= 0], 1) / (dim * dim - size(M[M .!= 0], 1)) #nonzero/zero elements
 end
 
-function digital_chaotic_adjacency(rng::AbstractRNG, bit_precision::Integer;
-        extra_edge_probability::AbstractFloat=0.1)
-    matrix_order = 2^(2 * bit_precision)
-    adjacency_matrix = zeros(Int, matrix_order, matrix_order)
-    for row_index in 1:(matrix_order - 1)
-        adjacency_matrix[row_index, row_index + 1] = 1
-    end
-    adjacency_matrix[matrix_order, 1] = 1
-    for row_index in 1:matrix_order, column_index in 1:matrix_order
-        if row_index != column_index && rand(rng) < extra_edge_probability
-            adjacency_matrix[row_index, column_index] = 1
-        end
-    end
-
-    return adjacency_matrix
-end
-
 """
     chaotic_init([rng], [T], dims...;
         extra_edge_probability=T(0.1), spectral_radius=one(T),
@@ -859,11 +1026,29 @@ function chaotic_init(rng::AbstractRNG, ::Type{T}, dims::Integer...;
     return return_sparse_matrix ? sparse(reservoir_matrix) : reservoir_matrix
 end
 
+function digital_chaotic_adjacency(rng::AbstractRNG, bit_precision::Integer;
+        extra_edge_probability::AbstractFloat=0.1)
+    matrix_order = 2^(2 * bit_precision)
+    adjacency_matrix = DeviceAgnostic.zeros(rng, Int, matrix_order, matrix_order)
+    for row_index in 1:(matrix_order - 1)
+        adjacency_matrix[row_index, row_index + 1] = 1
+    end
+    adjacency_matrix[matrix_order, 1] = 1
+    for row_index in 1:matrix_order, column_index in 1:matrix_order
+        if row_index != column_index && rand(rng) < extra_edge_probability
+            adjacency_matrix[row_index, column_index] = 1
+        end
+    end
+
+    return adjacency_matrix
+end
+
 ### fallbacks
 #fallbacks for initializers #eventually to remove once migrated to WeightInitializers.jl
 for initializer in (:rand_sparse, :delay_line, :delay_line_backward, :cycle_jumps,
     :simple_cycle, :pseudo_svd, :chaotic_init,
-    :scaled_rand, :weighted_init, :informed_init, :minimal_init, :chebyshev_mapping)
+    :scaled_rand, :weighted_init, :informed_init, :minimal_init, :chebyshev_mapping,
+    :logistic_mapping, :modified_lm)
     @eval begin
         function ($initializer)(dims::Integer...; kwargs...)
             return $initializer(Utils.default_rng(), Float32, dims...; kwargs...)
diff --git a/test/esn/test_inits.jl b/test/esn/test_inits.jl
index 944a4613..2b7f0993 100644
--- a/test/esn/test_inits.jl
+++ b/test/esn/test_inits.jl
@@ -32,7 +32,9 @@ input_inits = [
     weighted_init,
     minimal_init,
     minimal_init(; sampling_type=:irrational),
-    chebyshev_mapping
+    chebyshev_mapping,
+    logistic_mapping,
+    modified_lm(; factor=4)
 ]
 
 @testset "Reservoir Initializers" begin