diff --git a/docs/src/index.md b/docs/src/index.md
index d88505b3..25d11522 100644
--- a/docs/src/index.md
+++ b/docs/src/index.md
@@ -25,7 +25,7 @@ The support for this library is for Julia v1.6 or greater.
 This library provides multiple ways of training the chosen RC model. More specifically the available algorithms are:
 - ```StandardRidge```: a naive implementation of Ridge Regression. The default choice for training.
 - ```LinearModel```: a wrap around [MLJLinearModels](https://juliaai.github.io/MLJLinearModels.jl/stable/).
-- ```GaussianProcess```: a wrap around [GaussianProcesses](http://stor-i.github.io/GaussianProcesses.jl/latest/).
+- ```GaussianProcess```: a wrap around [GaussianProcesses](http://stor-i.github.io/GaussianProcesses.jl/latest/). Currently not available in version 0.9.0. Alternatives are being explored
 - ```LIBSVM.AbstractSVR```: a direct call of [LIBSVM](https://github.com/JuliaML/LIBSVM.jl) regression methods.
 
 Also provided are two different ways of doing predictions using RC:
diff --git a/src/ReservoirComputing.jl b/src/ReservoirComputing.jl
index d4dec0da..37863e45 100644
--- a/src/ReservoirComputing.jl
+++ b/src/ReservoirComputing.jl
@@ -19,7 +19,7 @@ export AbstractLayer, create_layer
 export WeightedLayer, DenseLayer, SparseLayer, MinimumLayer, InformedLayer, NullLayer
 export BernoulliSample, IrrationalSample
 export GaussianProcess
-export AbstractReservoir, create_reservoir
+export AbstractReservoir, create_reservoir, create_states
 export RandSparseReservoir, PseudoSVDReservoir, DelayLineReservoir
 export DelayLineBackwardReservoir, SimpleCycleReservoir, CycleJumpsReservoir, NullReservoir
 export RNN, MRNN, GRU, GRUParams, FullyGated, Variant1, Variant2, Variant3, Minimal
diff --git a/src/esn/esn_reservoir_drivers.jl b/src/esn/esn_reservoir_drivers.jl
index e339a3c3..6467df86 100644
--- a/src/esn/esn_reservoir_drivers.jl
+++ b/src/esn/esn_reservoir_drivers.jl
@@ -1,9 +1,12 @@
 abstract type AbstractReservoirDriver end
 
 """
-    create_states(
-        reservoir_driver::AbstractReservoirDriver, train_data, reservoir_matrix,input_matrix
-    )
+    create_states(reservoir_driver::AbstractReservoirDriver,
+                       train_data,
+                       washout,
+                       reservoir_matrix,
+                       input_matrix,
+                       bias_vector)
 
 Return the trained ESN states according to the given driver.
 """