diff --git a/ChangeLog.md b/ChangeLog.md
--- a/ChangeLog.md
+++ b/ChangeLog.md
@@ -1,3 +1,9 @@
-# Changelog for rc
+# Changelog for rc (reservoir computing)
 
-## Unreleased changes
+## 0.1.0.1 *February 25th 2018*
+  * Better documentation
+
+## 0.1.0.0 *February 23th 2018*
+  * Initial release featuring single-node approach to nonlinear
+    transient computing (NTC)
+  * Mackey-Glass time series forecasting demo
diff --git a/README.md b/README.md
--- a/README.md
+++ b/README.md
@@ -2,9 +2,11 @@
 
 Facilitating RC research
 
+
 ## Features
 
 * [Nonlinear transient computing (NTC)](https://github.com/masterdezign/rc/tree/master/examples/NTC)
+* Echo State Networks (ESNs) (upcoming)
 
 
 ## Getting Started
@@ -14,15 +16,24 @@
      $ git clone https://github.com/masterdezign/rc.git && cd rc
      $ stack build --install-ghc
 
-### [Example 1](https://github.com/masterdezign/rc/tree/master/examples/NTC). NTC
-
+### [Example 1](https://github.com/masterdezign/rc/tree/master/examples/NTC). NTC, time series forecasting
 
      $ stack exec ntc
 
      Error: 3.1103181863915367e-3
 
-[Here](https://raw.githubusercontent.com/masterdezign/rc/master/examples/NTC/mg-prediction.png) is visualized prediction result.
+Visualize the prediction results:
 
+     $ python3 examples/NTC/plot.py
 
+![Figure](https://raw.githubusercontent.com/masterdezign/rc/master/examples/NTC/mg-prediction.png)
 
 Great!
+
+
+## Further reading
+
+* Larger, L., et al. “Photonic Information Processing beyond Turing: an Optoelectronic Implementation of Reservoir Computing.” Optics Express, vol. 20, no. 3, 2012, p. 3241., doi:10.1364/oe.20.003241.
+* Rabinovič, Mihail Izrailevič, et al. Principles of Brain Dynamics: Global State Interactions. The MIT Press, 2012.
+* Jaeger, H. “Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication.” Science, vol. 304, no. 5667, Feb. 2004, pp. 78–80., doi:10.1126/science.1091277.
+* Bogdan Penkovsky. Theory and Modeling of Complex Nonlinear Delay Dynamics Applied to Neuromorphic Computing. Artificial Intelligence [cs.AI]. Université Bourgogne Franche-Comté, 2017. English. 〈tel-01591441v2〉
diff --git a/examples/NTC/Main.hs b/examples/NTC/Main.hs
--- a/examples/NTC/Main.hs
+++ b/examples/NTC/Main.hs
@@ -14,6 +14,10 @@
 takeFuture :: Int -> Matrix Double -> Matrix Double
 takeFuture horizon = (?? (All, Drop horizon))
 
+splitAt' :: Int -> Matrix Double -> (Matrix Double, Matrix Double)
+splitAt' i m = (m ?? (All, Take i), m ?? (All, Drop i))
+
+main :: IO ()
 main = do
   -- Load and transpose time series to predict
   dta <- tr <$> loadMatrix "examples/data/mg.txt"
@@ -21,11 +25,10 @@
   let splitRatio = 0.50  -- Train on 50% of data
       total = cols dta
       spl = round $ splitRatio * fromIntegral total
-      -- Split the data
-      train = dta ?? (All, Take spl)
-      validate = dta ?? (All, Drop spl)
+      -- Split the data into training and validation data sets
+      (train, validate) = splitAt' spl dta
 
-  -- Configure new NTC network
+  -- Configure a new NTC network
   let p = RC.par0 { RC._inputWeightsRange = (0.1, 0.3) }
       g = mkStdGen 1111
       ntc = RC.new g p (1, 1000, 1)
diff --git a/rc.cabal b/rc.cabal
--- a/rc.cabal
+++ b/rc.cabal
@@ -2,10 +2,10 @@
 --
 -- see: https://github.com/sol/hpack
 --
--- hash: 32b8af1a3f4db71e5262f476ae36f22cd703e7dfd8e047f0c0d7b61b9ee1f3e8
+-- hash: 080028c4b027364671d6876826a008e2c27817e0f1b4394a51c0a890ac59f86e
 
 name:           rc
-version:        0.1.0.0
+version:        0.1.0.1
 synopsis:       Reservoir Computing, fast RNNs
 description:    Please see the README on Github at <https://github.com/masterdezign/rc#readme>
 category:       Machine Learning
diff --git a/rc/RC/Helpers.hs b/rc/RC/Helpers.hs
--- a/rc/RC/Helpers.hs
+++ b/rc/RC/Helpers.hs
@@ -23,6 +23,7 @@
       | otherwise = β * (width - offset)
 
 -- | Prepend a row of ones
+--
 -- >>> addBiases $ (2><3) [20..26]
 -- (3><3)
 --  [  1.0,  1.0,  1.0
diff --git a/rc/RC/NTC.hs b/rc/RC/NTC.hs
--- a/rc/RC/NTC.hs
+++ b/rc/RC/NTC.hs
@@ -1,3 +1,4 @@
+-- |
 -- = Nonlinear transient computing
 --
 -- This module was developed as a part of author's PhD project:
@@ -27,9 +28,28 @@
 
 import qualified RC.Helpers as H
 
--- | DDE reservoir abstraction
+-- | Reservoir abstraction.
+--
+-- Reservoir (a recurrent neural network) is an essential
+-- component in the NTC framework.
+--
+-- In this project, we exploit an established analogy between
+-- spatially extended and delay systems ^1-3. That makes possible
+-- to employ DDEs as a reservoir substrate. The choice of substrate
+-- does not affect the `Reservoir` definition below.
+--
+-- ^1 Arecchi, F. T., et al. “Two-Dimensional Representation of
+--    a Delayed Dynamical System.” Physical Review A, vol. 45, no. 7,
+--    Jan. 1992, doi:10.1103/physreva.45.r4225.
+-- ^2 Appeltant, L., et al. “Information Processing Using a Single
+--    Dynamical Node as Complex System.” Nature Communications, vol. 2,
+--    2011, p. 468., doi:10.1038/ncomms1476.
+-- ^3 Virtual Chimera States for Delayed-Feedback Systems - Laurent Larger,
+--    Bogdan Penkovsky, Yuri Maistrenko. Physical Review Letters - 08 / 2013
+--
 newtype Reservoir = Reservoir { _transform :: Matrix Double -> Matrix Double }
 
+-- | Customizable NTC parameters
 data NTCParameters = Par
   { _preprocess :: Matrix Double -> Matrix Double
     -- ^ Modify data before masking (e.g. compression)
@@ -40,6 +60,7 @@
   , _reservoirModel :: DDEModel.Par
   }
 
+-- | NTC network structure
 data NTC = NTC
   { _inputWeights :: Matrix Double
   , _reservoir :: Reservoir
@@ -48,6 +69,7 @@
   , _par :: NTCParameters
   }
 
+-- | Creates an untrained NTC network
 new
   :: StdGen
   -> NTCParameters
@@ -82,8 +104,11 @@
             , DDEModel._theta = recip 0.34375
             }
 
+-- | Substrate-specific low-level reservoir implementation
 genReservoir :: DDEModel.Par -> Reservoir
-genReservoir par = Reservoir _r
+genReservoir par@DDEModel.RC {
+    DDEModel._filt = DDEModel.BandpassFiltering { DDEModel._tau = tau }
+  } = Reservoir _r
   where
     _r sample = unflatten response
       where
@@ -101,12 +126,18 @@
         -- Duplicate the last element (DDE.integHeun2_2D consumes one extra input)
         trace = trace1 V.++ V.singleton (V.last trace1)
 
-        tau = DDEModel._tau $ DDEModel._filt par
+        -- Empirically chosen integration time step:
+        -- twice faster than the system response time tau
         hStep = tau / 2
 
         !(_, !response) = DDE.integHeun2_2D delaySamples hStep (DDEModel.rhs par) (DDE.Input trace)
 
-forwardPass :: NTC -> Matrix Double -> Matrix Double
+genReservoir _ = error "Unsupported DDE model"
+
+-- | Nonlinear transformation performed by an NTC network
+forwardPass :: NTC  -- ^ NTC network
+            -> Matrix Double  -- ^ Input information
+            -> Matrix Double
 forwardPass NTC { _par = Par { _preprocess = prep, _postprocess = post }
                 , _inputWeights = iw
                 , _reservoir = Reservoir res
@@ -114,7 +145,9 @@
   let pipeline = post. res. (iw <>). prep
   in pipeline sample
 
--- | Offline NTC training
+-- TODO: introduce an explicit `learnClassifier` function
+
+-- | NTC training: learn the readout weights offline
 learn
   :: NTC
   -> Int
@@ -132,10 +165,15 @@
       Nothing -> Left "Cannot create a readout matrix"
       w -> Right $ ntc { _outputWeights = w }
 
-predict :: NTC
-        -> Int
+-- | Run prediction using a "clean" (uninitialized) reservoir and then
+-- forget the reservoir's state.
+-- This can be used for both forecasting and classification tasks.
+predict :: NTC  -- ^ Trained network
+        -> Int  -- ^ Washout (forget) points
         -> Matrix Double
+        -- ^ Input matrix where measurements are columns and features are rows
         -> Either String (Matrix Double)
+        -- ^ Either error string or predicted output
 predict ntc@NTC { _outputWeights = ow
                 } forgetPts inp =
   case ow of