diff --git a/.buildkite/documentation.yml b/.buildkite/documentation.yml
index d0bad413..99ae35f5 100644
--- a/.buildkite/documentation.yml
+++ b/.buildkite/documentation.yml
@@ -1,5 +1,5 @@
 steps:
-  - label: ":page_facing_up: Build and Deploy Documentation"
+  - label: ":page_facing_up: Build and Deploy ReservoirComputing Documentation"
     plugins:
       - JuliaCI/julia#v1:
           version: "1"
@@ -20,6 +20,21 @@ steps:
     if: build.message !~ /\[skip docs\]/ && !build.pull_request.draft
     timeout_in_minutes: 1000
 
+  - label: ":page_facing_up: Build & Deploy ReservoirCellularAutomata Documentation"
+    plugins:
+      - JuliaCI/julia#v1:
+          version: "1"
+    command: |
+      julia --project=lib/ReservoirCellularAutomata/docs -e '
+        using Pkg; Pkg.instantiate()
+        include("make.jl")
+      '
+    agents:
+      queue: "juliagpu"
+      cuda: "*"
+    if: build.message !~ /\[skip docs\]/ && !build.pull_request.draft
+    timeout_in_minutes: 1000
+
 env:
   JULIA_PKG_SERVER: "" # it often struggles with our large artifacts
   SECRET_DOCUMENTER_KEY: "qmrtZ2jy2W0e2h5zu3V7g+wxiz/1BHLsjKGHfosfjxel5nf6/s1mPA+4DEZcBUHwBFt7tfxoKOA6H1gk5PdbyJvFJA6xs4/PT8xvotzv9eYpxtJIoKLRQvjUCq78T+7/0DoicsMNk1uP3/JNkwm4TRLqVXs9QAe9HPnh6x+q/Sro0x9DXIUE2xbTQ3JP6ZUVhS04X7OCeuGTJbW8TdH8B+7V90+gJZcg5a7ctRP/V+PxDo++SOmdyryeqvwE+e6a8+czUWh9gaFQ4dXFRgoUAq+O/jRXSbQfWBXLGaubpMAodgZUmzVSOygELDdRVUhaENgfQfvnUpgpxX8UX/3Bcw==;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"
\ No newline at end of file
diff --git a/.github/workflows/CompatHelper.yml b/.github/workflows/CompatHelper.yml
index 96cd43f1..3cf1eb24 100644
--- a/.github/workflows/CompatHelper.yml
+++ b/.github/workflows/CompatHelper.yml
@@ -10,7 +10,9 @@ jobs:
     steps:
       - name: Pkg.add("CompatHelper")
         run: julia -e 'using Pkg; Pkg.add("CompatHelper")'
+
       - name: CompatHelper.main()
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: julia -e 'using CompatHelper; CompatHelper.main(;subdirs=["", "docs"])'
+        run: julia -e 'using CompatHelper; CompatHelper.main(;subdirs=["", "docs", "lib/ReservoirCellularAutomata", "lib/ReservoirCellularAutomata/docs"])'
+
diff --git a/.github/workflows/Downgrade.yml b/.github/workflows/Downgrade.yml
index 3eef5129..39b555c4 100644
--- a/.github/workflows/Downgrade.yml
+++ b/.github/workflows/Downgrade.yml
@@ -1,36 +1,51 @@
 name: Downgrade
+
 on:
   pull_request:
-    branches:
-      - master
-    paths-ignore:
-      - 'docs/**'
+    branches: [ master ]
+    paths-ignore: [ 'docs/**' ]
   push:
-    branches:
-      - master
-    paths-ignore:
-      - 'docs/**'
+    branches: [ master ]
+    paths-ignore: [ 'docs/**' ]
+
 jobs:
   test:
     runs-on: ubuntu-latest
+
     strategy:
+      fail-fast: false
       matrix:
-        group:
-          - Core
+        project:
+          - name: ReservoirComputing
+            path: .
+          - name: ReservoirCellularAutomata
+            path: lib/ReservoirCellularAutomata
+        group:      ['Core']
         downgrade_mode: ['alldeps']
         julia-version: ['1.10']
+
+    name: Downgrade – ${{ matrix.project.name }} · Julia ${{ matrix.julia-version }}
+
     steps:
       - uses: actions/checkout@v4
+
       - uses: julia-actions/setup-julia@v2
         with:
-          version: ${{ matrix.julia-version }}
+          julia-version: ${{ matrix.julia-version }}
+
       - uses: julia-actions/julia-downgrade-compat@v2
-#        if: ${{ matrix.version == '1.6' }}
         with:
-          skip: Pkg,TOML
+          skip:  Pkg,TOML
+          mode:  ${{ matrix.downgrade_mode }}
+          project: ${{ matrix.project.path }}
+
       - uses: julia-actions/julia-buildpkg@v1
+        with:
+          project: ${{ matrix.project.path }}
+
       - uses: julia-actions/julia-runtest@v1
         with:
-          ALLOW_RERESOLVE: false
+          project:        ${{ matrix.project.path }}
+          allow-reresolve: false
         env:
-          GROUP: ${{ matrix.group }}
+          GROUP: ${{ matrix.group }}
\ No newline at end of file
diff --git a/.github/workflows/TagBot.yml b/.github/workflows/TagBot.yml
index 336aa1d5..8c11b545 100644
--- a/.github/workflows/TagBot.yml
+++ b/.github/workflows/TagBot.yml
@@ -1,3 +1,5 @@
+# Inspiration from https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/blob/master/.github/workflows/TagBot.yml
+# and https://github.com/LuxDL/Lux.jl/blob/main/.github/workflows/TagBot.yml
 name: TagBot
 on:
   issue_comment:
@@ -21,13 +23,22 @@ permissions:
   security-events: read
   statuses: read
 jobs:
-  TagBot:
+  TagBot-ReservoirComputing:
     if: github.event_name == 'workflow_dispatch' || github.actor == 'JuliaTagBot'
     runs-on: ubuntu-latest
     steps:
-      - uses: JuliaRegistries/TagBot@62dd7cd21dcde087eca10e70c3fc3f8cefbe44a2
+      - name: Tag ReservoirComputing
+        uses: JuliaRegistries/TagBot@62dd7cd21dcde087eca10e70c3fc3f8cefbe44a2
         with:
           token: ${{ secrets.GITHUB_TOKEN }}
-          # Edit the following line to reflect the actual name of the GitHub Secret containing your private key
           ssh: ${{ secrets.DOCUMENTER_KEY }}
-          # ssh: ${{ secrets.NAME_OF_MY_SSH_PRIVATE_KEY_SECRET }}
+  TagBot-ReservoirCellularAutomata:
+    if: github.event_name == 'workflow_dispatch' || github.actor == 'JuliaTagBot'
+    runs-on: ubuntu-latest
+    steps:
+      - name: Tag ReservoirCellularAutomata
+        uses: JuliaRegistries/TagBot@62dd7cd21dcde087eca10e70c3fc3f8cefbe44a2
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}
+          ssh: ${{ secrets.DOCUMENTER_KEY }}
+          subdir: "lib/ReservoirCellularAutomata"
diff --git a/.github/workflows/TestsReservoirCellularAutomata.yml b/.github/workflows/TestsReservoirCellularAutomata.yml
new file mode 100644
index 00000000..d58acc14
--- /dev/null
+++ b/.github/workflows/TestsReservoirCellularAutomata.yml
@@ -0,0 +1,40 @@
+name: "Tests - ReservoirCellularAutomata"
+
+on:
+  pull_request:
+    branches:
+      - master
+    paths:
+      - "lib/ReservoirCellularAutomata/**"
+      - ".github/workflows/TestsReservoirCellularAutomata.yml"
+  push:
+    branches:
+      - master
+    paths-ignore:
+      - 'docs/**'
+  schedule:
+    - cron: '33 1 * * 4'
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ github.ref_name != github.event.repository.default_branch || github.ref != 'refs/tags/v*' }}
+
+jobs:
+  tests:
+    strategy:
+      fail-fast: false
+      matrix:
+        version:
+          - "1"
+          - "lts"
+          - "pre"
+        os:
+          - "ubuntu-latest"
+          - "macos-latest"
+          - "windows-latest"
+    uses: "SciML/.github/.github/workflows/tests.yml@v1"
+    with:
+      julia-version: "${{ matrix.version }}"
+      os: "${{ matrix.os }}"
+      project: "lib/ReservoirCellularAutomata"
+    secrets: "inherit"
\ No newline at end of file
diff --git a/.github/workflows/Tests.yml b/.github/workflows/TestsReservoirComputing.yml
similarity index 95%
rename from .github/workflows/Tests.yml
rename to .github/workflows/TestsReservoirComputing.yml
index a2b581ed..a53c34ed 100644
--- a/.github/workflows/Tests.yml
+++ b/.github/workflows/TestsReservoirComputing.yml
@@ -1,4 +1,4 @@
-name: "Tests"
+name: "Tests - ReservoirComputing"
 
 on:
   pull_request:
diff --git a/Project.toml b/Project.toml
index 7a0d3f84..839d9ded 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,11 +1,10 @@
 name = "ReservoirComputing"
 uuid = "7c2d2b1e-3dd4-11ea-355a-8f6a8116e294"
 authors = ["Francesco Martinuzzi"]
-version = "0.11.3"
+version = "0.12.0"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
-CellularAutomata = "878138dc-5b27-11ea-1a71-cb95d38d6b29"
 Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
@@ -14,11 +13,13 @@ Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 WeightInitializers = "d49dbf32-c5c2-4618-8acc-27bb2598ef2d"
 
 [weakdeps]
+CellularAutomata = "878138dc-5b27-11ea-1a71-cb95d38d6b29"
 LIBSVM = "b1bec4e5-fd48-53fe-b0cb-9723c09d164b"
 MLJLinearModels = "6ee0df7b-362f-4a72-a706-9e79364fb692"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
 [extensions]
+RCCellularAutomataExt = "CellularAutomata"
 RCLIBSVMExt = "LIBSVM"
 RCMLJLinearModelsExt = "MLJLinearModels"
 RCSparseArraysExt = "SparseArrays"
@@ -26,7 +27,7 @@ RCSparseArraysExt = "SparseArrays"
 [compat]
 Adapt = "4.1.1"
 Aqua = "0.8"
-CellularAutomata = "0.0.2"
+CellularAutomata = "0.0.6"
 Compat = "4.16.0"
 DifferentialEquations = "7.16.1"
 LIBSVM = "0.8"
diff --git a/docs/make.jl b/docs/make.jl
index 3194943c..f1547757 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -15,6 +15,7 @@ bib = CitationBibliography(
 
 makedocs(; modules = [ReservoirComputing],
     sitename = "ReservoirComputing.jl",
+    authors = "Francesco Martinuzzi",
     clean = true, doctest = false, linkcheck = true,
     plugins = [bib],
     format = Documenter.HTML(;
diff --git a/docs/pages.jl b/docs/pages.jl
index 773ba0db..a5d98610 100644
--- a/docs/pages.jl
+++ b/docs/pages.jl
@@ -12,13 +12,11 @@ pages = [
         #"Using Different Training Methods" => "esn_tutorials/different_training.md",
         "Deep Echo State Networks" => "esn_tutorials/deep_esn.md",
         "Hybrid Echo State Networks" => "esn_tutorials/hybrid.md"],
-    "Reservoir Computing with Cellular Automata" => "reca_tutorials/reca.md",
     "API Documentation" => Any["Training Algorithms" => "api/training.md",
         "States Modifications" => "api/states.md",
         "Prediction Types" => "api/predict.md",
         "Echo State Networks" => "api/esn.md",
         "ESN Initializers" => "api/inits.md",
         "ESN Drivers" => "api/esn_drivers.md",
-        "ESN Variations" => "api/esn_variations.md",
-        "ReCA" => "api/reca.md"]    #"References" => "references.md"
+        "ESN Variations" => "api/esn_variations.md"]
 ]
diff --git a/docs/src/api/reca.md b/docs/src/api/reca.md
deleted file mode 100644
index 48f134dd..00000000
--- a/docs/src/api/reca.md
+++ /dev/null
@@ -1,13 +0,0 @@
-# Reservoir Computing with Cellular Automata
-
-```@docs
-    RECA
-```
-
-The input encodings are the equivalent of the input matrices of the ESNs. These are the available encodings:
-
-```@docs
-    RandomMapping
-```
-
-The training and prediction follow the same workflow as the ESN. It is important to note that currently we were unable to find any papers using these models with a `Generative` approach for the prediction, so full support is given only to the `Predictive` method.
diff --git a/docs/src/reca_tutorials/5bitinput.txt b/docs/src/reca_tutorials/5bitinput.txt
deleted file mode 100644
index 17f7d542..00000000
--- a/docs/src/reca_tutorials/5bitinput.txt
+++ /dev/null
@@ -1,4 +0,0 @@
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diff --git a/docs/src/reca_tutorials/5bitoutput.txt b/docs/src/reca_tutorials/5bitoutput.txt
deleted file mode 100644
index 1771405e..00000000
--- a/docs/src/reca_tutorials/5bitoutput.txt
+++ /dev/null
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diff --git a/docs/src/reca_tutorials/reca.md b/docs/src/reca_tutorials/reca.md
deleted file mode 100644
index fe8cdf8d..00000000
--- a/docs/src/reca_tutorials/reca.md
+++ /dev/null
@@ -1,48 +0,0 @@
-# Reservoir Computing using Cellular Automata
-
-Reservoir Computing based on Elementary Cellular Automata (ECA) has been recently introduced. Dubbed as ReCA [Yilmaz2014](@cite) [Margem2017](@cite) it proposed the advantage of storing the reservoir states as binary data. Less parameter tuning represents another advantage of this model. The architecture implemented in ReservoirComputing.jl follows [Nichele2017](@cite) which builds on top of the original implementation, improving the results. It is strongly suggested to go through the paper to get a solid understanding of the model before delving into experimentation with the code.
-
-To showcase how to use these models, this page illustrates the performance of ReCA in the 5 bit memory task.
-
-## 5 bit memory task
-
-The data can be read as follows:
-
-```@example reca
-using DelimitedFiles
-
-input = readdlm("./5bitinput.txt", ',', Float64)
-output = readdlm("./5bitoutput.txt", ',', Float64)
-```
-
-To use a ReCA model, it is necessary to define the rule one intends to use. To do so, ReservoirComputing.jl leverages [CellularAutomata.jl](https://github.com/MartinuzziFrancesco/CellularAutomata.jl) that needs to be called as well to define the `RECA` struct:
-
-```@example reca
-using ReservoirComputing, CellularAutomata
-
-ca = DCA(90)
-```
-
-To define the ReCA model, it suffices to call:
-
-```@example reca
-reca = RECA(input, ca;
-    generations=16,
-    input_encoding=RandomMapping(16, 40))
-```
-
-After this, the training can be performed with the chosen method.
-
-```@example reca
-output_layer = train(reca, output, StandardRidge(0.00001))
-```
-
-The prediction in this case will be a `Predictive()` with the input data equal to the training data. In addition, to test the 5 bit memory task, a conversion from Float to Bool is necessary (at the moment, we are aware of a bug that doesn't allow boolean input data to the RECA models):
-
-```@example reca
-prediction = reca(Predictive(input), output_layer)
-final_pred = convert(AbstractArray{Float32}, prediction .> 0.5)
-
-final_pred == output
-```
-
diff --git a/src/reca/reca_input_encodings.jl b/ext/RCCellularAutomataExt.jl
similarity index 52%
rename from src/reca/reca_input_encodings.jl
rename to ext/RCCellularAutomataExt.jl
index dd29d72e..cfc2ae62 100644
--- a/src/reca/reca_input_encodings.jl
+++ b/ext/RCCellularAutomataExt.jl
@@ -1,39 +1,54 @@
-abstract type AbstractInputEncoding end
-abstract type AbstractEncodingData end
+module RCCellularAutomataExt
+using ReservoirComputing: RECA, RandomMapping, RandomMaps
+import ReservoirComputing: train, next_state_prediction!, AbstractOutputLayer, NLADefault, StandardStates
+using CellularAutomata
+using Random: randperm
+
+function RECA(train_data,
+    automata;
+    generations=8,
+    input_encoding=RandomMapping(),
+    nla_type=NLADefault(),
+    states_type=StandardStates())
+    in_size = size(train_data, 1)
+    #res_size = obtain_res_size(input_encoding, generations)
+    state_encoding = create_encoding(input_encoding, train_data, generations)
+    states = reca_create_states(state_encoding, automata, train_data)
+
+    return RECA(train_data, automata, state_encoding, nla_type, states, states_type)
+end
 
-struct RandomMapping{I, T} <: AbstractInputEncoding
-    permutations::I
-    expansion_size::T
+#training dispatch
+function train(reca::RECA, target_data, training_method=StandardRidge; kwargs...)
+    states_new = reca.states_type(reca.nla_type, reca.states, reca.train_data)
+    return train(training_method, Float32.(states_new), Float32.(target_data); kwargs...)
 end
 
-"""
-    RandomMapping(permutations, expansion_size)
-    RandomMapping(permutations; expansion_size=40)
-    RandomMapping(;permutations=8, expansion_size=40)
-
-Random mapping of the input data directly in the reservoir. The `expansion_size`
-determines the dimension of the single reservoir, and `permutations` determines the
-number of total reservoirs that will be connected, each with a different mapping.
-The detail of this implementation can be found in [1].
-
-[1] Nichele, Stefano, and Andreas Molund. “Deep reservoir computing using cellular
-automata.” arXiv preprint arXiv:1703.02806 (2017).
-"""
-function RandomMapping(; permutations = 8, expansion_size = 40)
-    RandomMapping(permutations, expansion_size)
+#predict dispatch
+function (reca::RECA)(prediction,
+    output_layer::AbstractOutputLayer,
+    initial_conditions=output_layer.last_value,
+    last_state=zeros(reca.input_encoding.ca_size))
+    return obtain_prediction(reca, prediction, last_state, output_layer;
+        initial_conditions=initial_conditions)
 end
 
-function RandomMapping(permutations; expansion_size = 40)
+function next_state_prediction!(reca::RECA, x, out, i, args...)
+    rm = reca.input_encoding
+    x = encoding(rm, out, x)
+    ca = CellularAutomaton(reca.automata, x, rm.generations + 1)
+    ca_states = ca.evolution[2:end, :]
+    x_new = reshape(transpose(ca_states), rm.states_size)
+    x = ca.evolution[end, :]
+    return x, x_new
+end
+
+function RandomMapping(; permutations=8, expansion_size=40)
     RandomMapping(permutations, expansion_size)
 end
 
-struct RandomMaps{T, E, G, M, S} <: AbstractEncodingData
-    permutations::T
-    expansion_size::E
-    generations::G
-    maps::M
-    states_size::S
-    ca_size::S
+function RandomMapping(permutations; expansion_size=40)
+    RandomMapping(permutations, expansion_size)
 end
 
 function create_encoding(rm::RandomMapping, input_data, generations)
@@ -66,9 +81,9 @@ function encoding(rm::RandomMaps, input_vector, tot_encoded_vector)
     new_tot_enc_vec = copy(tot_encoded_vector)
 
     for i in 1:(rm.permutations)
-        new_tot_enc_vec[((i - 1) * rm.expansion_size + 1):(i * rm.expansion_size)] = single_encoding(
+        new_tot_enc_vec[((i-1)*rm.expansion_size+1):(i*rm.expansion_size)] = single_encoding(
             input_vector,
-            new_tot_enc_vec[((i - 1) * rm.expansion_size + 1):(i * rm.expansion_size)],
+            new_tot_enc_vec[((i-1)*rm.expansion_size+1):(i*rm.expansion_size)],
             rm.maps[i,
                 :])
     end
@@ -105,3 +120,5 @@ function mapping(input_size, mapped_vector_size)
     #sample(1:mapped_vector_size, input_size; replace=false)
     return randperm(mapped_vector_size)[1:input_size]
 end
+
+end #module
diff --git a/src/ReservoirComputing.jl b/src/ReservoirComputing.jl
index 028aad50..8470d2df 100644
--- a/src/ReservoirComputing.jl
+++ b/src/ReservoirComputing.jl
@@ -1,7 +1,6 @@
 module ReservoirComputing
 
 using Adapt: adapt
-using CellularAutomata: CellularAutomaton
 using Compat: @compat
 using LinearAlgebra: eigvals, mul!, I, qr, Diagonal
 using NNlib: fast_act, sigmoid
@@ -15,41 +14,37 @@ abstract type AbstractReservoirComputer end
 @compat(public, (create_states))
 
 #general
-include("states.jl")
-include("predict.jl")
-
-#general training
-include("train/linear_regression.jl")
-
+include("generics/states.jl")
+include("generics/predict.jl")
+include("generics/linear_regression.jl")
+#extensions
+include("extensions/reca.jl")
 #esn
-include("esn/inits_components.jl")
-include("esn/esn_inits.jl")
-include("esn/esn_reservoir_drivers.jl")
-include("esn/esn.jl")
-include("esn/deepesn.jl")
-include("esn/hybridesn.jl")
-include("esn/esn_predict.jl")
-
-#reca
-include("reca/reca.jl")
-include("reca/reca_input_encodings.jl")
+include("inits/inits_components.jl")
+include("inits/esn_inits.jl")
+include("layers/esn_reservoir_drivers.jl")
+include("models/esn.jl")
+include("models/deepesn.jl")
+include("models/hybridesn.jl")
+include("models/esn_predict.jl")
 
 export NLADefault, NLAT1, NLAT2, NLAT3, PartialSquare, ExtendedSquare
 export StandardStates, ExtendedStates, PaddedStates, PaddedExtendedStates
 export StandardRidge
 export chebyshev_mapping, informed_init, logistic_mapping, minimal_init,
-       modified_lm, scaled_rand, weighted_init, weighted_minimal
+    modified_lm, scaled_rand, weighted_init, weighted_minimal
 export block_diagonal, chaotic_init, cycle_jumps, delay_line, delay_line_backward,
-       double_cycle, forward_connection, low_connectivity, pseudo_svd, rand_sparse,
-       selfloop_cycle, selfloop_delayline_backward, selfloop_feedback_cycle,
-       selfloop_forward_connection, simple_cycle, true_double_cycle
+    double_cycle, forward_connection, low_connectivity, pseudo_svd, rand_sparse,
+    selfloop_cycle, selfloop_delayline_backward, selfloop_feedback_cycle,
+    selfloop_forward_connection, simple_cycle, true_double_cycle
 export add_jumps!, backward_connection!, delay_line!, reverse_simple_cycle!,
-       scale_radius!, self_loop!, simple_cycle!
+    scale_radius!, self_loop!, simple_cycle!
 export RNN, MRNN, GRU, GRUParams, FullyGated, Minimal
 export train
 export ESN, HybridESN, KnowledgeModel, DeepESN
+export Generative, Predictive, OutputLayer
+#reca
 export RECA
 export RandomMapping, RandomMaps
-export Generative, Predictive, OutputLayer
 
 end #module
diff --git a/src/extensions/reca.jl b/src/extensions/reca.jl
new file mode 100644
index 00000000..82c59e23
--- /dev/null
+++ b/src/extensions/reca.jl
@@ -0,0 +1,56 @@
+abstract type AbstractInputEncoding end
+abstract type AbstractEncodingData end
+
+"""
+    RandomMapping(permutations, expansion_size)
+    RandomMapping(permutations; expansion_size=40)
+    RandomMapping(;permutations=8, expansion_size=40)
+
+Random mapping of the input data directly in the reservoir. The `expansion_size`
+determines the dimension of the single reservoir, and `permutations` determines the
+number of total reservoirs that will be connected, each with a different mapping.
+The detail of this implementation can be found in [1].
+
+[1] Nichele, Stefano, and Andreas Molund. “Deep reservoir computing using cellular
+automata.” arXiv preprint arXiv:1703.02806 (2017).
+"""
+struct RandomMapping{I,T} <: AbstractInputEncoding
+    permutations::I
+    expansion_size::T
+end
+
+struct RandomMaps{T,E,G,M,S} <: AbstractEncodingData
+    permutations::T
+    expansion_size::E
+    generations::G
+    maps::M
+    states_size::S
+    ca_size::S
+end
+
+
+abstract type AbstractReca <: AbstractReservoirComputer end
+
+"""
+    RECA(train_data,
+        automata;
+        generations = 8,
+        input_encoding=RandomMapping(),
+        nla_type = NLADefault(),
+        states_type = StandardStates())
+
+[1] Yilmaz, Ozgur. “_Reservoir computing using cellular automata._”
+arXiv preprint arXiv:1410.0162 (2014).
+
+[2] Nichele, Stefano, and Andreas Molund. “_Deep reservoir computing using cellular
+automata._” arXiv preprint arXiv:1703.02806 (2017).
+"""
+struct RECA{S,R,E,T,Q} <: AbstractReca
+    #res_size::I
+    train_data::S
+    automata::R
+    input_encoding::E
+    nla_type::ReservoirComputing.NonLinearAlgorithm
+    states::T
+    states_type::Q
+end
diff --git a/src/generics/linear_regression.jl b/src/generics/linear_regression.jl
new file mode 100644
index 00000000..429b816f
--- /dev/null
+++ b/src/generics/linear_regression.jl
@@ -0,0 +1,40 @@
+@doc raw"""
+
+    StandardRidge([Type], [reg])
+
+Returns a training method for `train` based on ridge regression.
+The equations for ridge regression are as follows:
+
+```math
+\mathbf{w} = (\mathbf{X}^\top \mathbf{X} + 
+\lambda \mathbf{I})^{-1} \mathbf{X}^\top \mathbf{y}
+```
+
+# Arguments
+ - `Type`: type of the regularization argument. Default is inferred internally,
+   there's usually no need to tweak this
+ - `reg`: regularization coefficient. Default is set to 0.0 (linear regression).
+
+```
+"""
+struct StandardRidge
+    reg::Number
+end
+
+function StandardRidge(::Type{T}, reg) where {T <: Number}
+    return StandardRidge(T.(reg))
+end
+
+function StandardRidge()
+    return StandardRidge(0.0)
+end
+
+function train(sr::StandardRidge, states::AbstractArray, target_data::AbstractArray)
+    n_states = size(states, 1)
+    A = [states'; sqrt(sr.reg) * I(n_states)]
+    b = [target_data'; zeros(n_states, size(target_data, 1))]
+    F = qr(A)
+    Wt = F \ b
+    output_layer = Matrix(Wt')
+    return OutputLayer(sr, output_layer, size(target_data, 1), target_data[:, end])
+end
diff --git a/src/predict.jl b/src/generics/predict.jl
similarity index 100%
rename from src/predict.jl
rename to src/generics/predict.jl
diff --git a/src/states.jl b/src/generics/states.jl
similarity index 100%
rename from src/states.jl
rename to src/generics/states.jl
diff --git a/src/esn/esn_inits.jl b/src/inits/esn_inits.jl
similarity index 100%
rename from src/esn/esn_inits.jl
rename to src/inits/esn_inits.jl
diff --git a/src/esn/inits_components.jl b/src/inits/inits_components.jl
similarity index 100%
rename from src/esn/inits_components.jl
rename to src/inits/inits_components.jl
diff --git a/src/esn/esn_reservoir_drivers.jl b/src/layers/esn_reservoir_drivers.jl
similarity index 100%
rename from src/esn/esn_reservoir_drivers.jl
rename to src/layers/esn_reservoir_drivers.jl
diff --git a/src/esn/deepesn.jl b/src/models/deepesn.jl
similarity index 100%
rename from src/esn/deepesn.jl
rename to src/models/deepesn.jl
diff --git a/src/esn/esn.jl b/src/models/esn.jl
similarity index 100%
rename from src/esn/esn.jl
rename to src/models/esn.jl
diff --git a/src/esn/esn_predict.jl b/src/models/esn_predict.jl
similarity index 100%
rename from src/esn/esn_predict.jl
rename to src/models/esn_predict.jl
diff --git a/src/esn/hybridesn.jl b/src/models/hybridesn.jl
similarity index 100%
rename from src/esn/hybridesn.jl
rename to src/models/hybridesn.jl
diff --git a/src/reca/reca.jl b/src/reca/reca.jl
deleted file mode 100644
index 8af5b2d3..00000000
--- a/src/reca/reca.jl
+++ /dev/null
@@ -1,64 +0,0 @@
-abstract type AbstractReca <: AbstractReservoirComputer end
-
-struct RECA{S, R, E, T, Q} <: AbstractReca
-    #res_size::I
-    train_data::S
-    automata::R
-    input_encoding::E
-    nla_type::ReservoirComputing.NonLinearAlgorithm
-    states::T
-    states_type::Q
-end
-
-"""
-    RECA(train_data,
-        automata;
-        generations = 8,
-        input_encoding=RandomMapping(),
-        nla_type = NLADefault(),
-        states_type = StandardStates())
-
-[1] Yilmaz, Ozgur. “_Reservoir computing using cellular automata._”
-arXiv preprint arXiv:1410.0162 (2014).
-
-[2] Nichele, Stefano, and Andreas Molund. “_Deep reservoir computing using cellular
-automata._” arXiv preprint arXiv:1703.02806 (2017).
-"""
-function RECA(train_data,
-        automata;
-        generations = 8,
-        input_encoding = RandomMapping(),
-        nla_type = NLADefault(),
-        states_type = StandardStates())
-    in_size = size(train_data, 1)
-    #res_size = obtain_res_size(input_encoding, generations)
-    state_encoding = create_encoding(input_encoding, train_data, generations)
-    states = reca_create_states(state_encoding, automata, train_data)
-
-    return RECA(train_data, automata, state_encoding, nla_type, states, states_type)
-end
-
-#training dispatch
-function train(reca::AbstractReca, target_data, training_method = StandardRidge; kwargs...)
-    states_new = reca.states_type(reca.nla_type, reca.states, reca.train_data)
-    return train(training_method, Float32.(states_new), Float32.(target_data); kwargs...)
-end
-
-#predict dispatch
-function (reca::RECA)(prediction,
-        output_layer::AbstractOutputLayer,
-        initial_conditions = output_layer.last_value,
-        last_state = zeros(reca.input_encoding.ca_size))
-    return obtain_prediction(reca, prediction, last_state, output_layer;
-        initial_conditions = initial_conditions)
-end
-
-function next_state_prediction!(reca::RECA, x, out, i, args...)
-    rm = reca.input_encoding
-    x = encoding(rm, out, x)
-    ca = CellularAutomaton(reca.automata, x, rm.generations + 1)
-    ca_states = ca.evolution[2:end, :]
-    x_new = reshape(transpose(ca_states), rm.states_size)
-    x = ca.evolution[end, :]
-    return x, x_new
-end
diff --git a/test/runtests.jl b/test/runtests.jl
index 8f051129..3e631748 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -13,7 +13,3 @@ end
     @safetestset "Hybrid ESN" include("esn/test_hybrid.jl")
     @safetestset "Deep ESN" include("esn/deepesn.jl")
 end
-
-@testset "CA based Reservoirs" begin
-    @safetestset "RECA" include("reca/test_predictive.jl")
-end