diff --git a/src/htm/algorithms/TemporalMemory.cpp b/src/htm/algorithms/TemporalMemory.cpp
index 38d7b7cb8c..655a3e6314 100644
--- a/src/htm/algorithms/TemporalMemory.cpp
+++ b/src/htm/algorithms/TemporalMemory.cpp
@@ -506,7 +506,8 @@ void TemporalMemory::compute(const SDR &activeColumns,
       const Real raw = computeRawAnomalyScore(
                          activeColumns,
                          cellsToColumns( getPredictiveCells() ));
-      tmAnomaly_.anomaly_ = tmAnomaly_.anomalyLikelihood_.anomalyProbability(raw);
+      tmAnomaly_.anomaly_ = 1.0f - tmAnomaly_.anomalyLikelihood_.anomalyProbability(raw); // AnomalyLikelihood returs a likelihood of the score (given past scores), 
+      // and we want to detect unlikely scores, aka big changes -> 1.0-likelihood represents "anomaly"
 			     } break;
 
     case ANMode::LOGLIKELIHOOD: {
@@ -515,7 +516,7 @@ void TemporalMemory::compute(const SDR &activeColumns,
                          cellsToColumns( getPredictiveCells() ));
       const Real like = tmAnomaly_.anomalyLikelihood_.anomalyProbability(raw);
       const Real log  = tmAnomaly_.anomalyLikelihood_.computeLogLikelihood(like);
-      tmAnomaly_.anomaly_ = log;
+      tmAnomaly_.anomaly_ = 1.0f - log;
 				} break;
   // TODO: Update mean & standard deviation of anomaly here.
   };
diff --git a/src/htm/algorithms/TemporalMemory.hpp b/src/htm/algorithms/TemporalMemory.hpp
index 4eedb4f8e2..fa8ab58a26 100644
--- a/src/htm/algorithms/TemporalMemory.hpp
+++ b/src/htm/algorithms/TemporalMemory.hpp
@@ -688,8 +688,23 @@ class TemporalMemory : public Serializable
    *  anomaly score computed for the current inputs
    *  (auto-updates after each call to TM::compute())
    *
-   *  @return a float value from computeRawAnomalyScore()
-   *  from Anomaly.hpp
+   *  Note, if you use manual learning methods for TM (activateDendrites, etc.) the scores 
+   *  will not be in sync (works only if `TM.compute` is used). 
+   *
+   *  Options `ANMode` {DISABLED, RAW, LIKELIHOOD, LOGLIKELIHOOD} passed during constructor 
+   *  of this class affect the computation of anomaly here. 
+   *    - "DISABLED": always returns 0.5f, no computation is performed (use if you don't care for anomaly
+   *      and need maximum speed). 
+   *    - "RAW": return value from `computeRawAnomalyScore()` from `Anomaly.hpp`, default.
+   *    - "LIKELIHOOD": Compute the probability that the current anomaly score represents
+   *      an anomaly given the historical distribution of anomaly scores. The closer
+   *      the number is to 1, the higher the chance it is an anomaly.
+   *      Computed from `anomalyProbability()` in `AnomalyLikelihood.hpp`.
+   *    - "LOGLIKELIHOOD": Compute a log scale representation of the likelihood value. This mode performs
+   *      `computeLogLikelihood()` from `AnomalyLikelihood.hpp`
+   *
+   *  @return a float value based on `ANMode` argument used in constructor of the TM class. 
+   *    By default return value from `computeRawAnomalyScore()` from `Anomaly.hpp`
    */
 const Real &anomaly = tmAnomaly_.anomaly_; //this is position dependant, the struct anomaly_tm must be defined before this use,
 // otherwise this surprisingly compiles, but a call to `tmAnomaly_.anomaly` segfaults!