Continuous　and　real-time　learning　is　a　difficult　problem　in　robotics.　This　paper　investigates　how　learning　in　the　input　layer　of　the　cerebellum　may　successfully　encode　contextual　knowledge　in　a　representation　useful　for　coordination　and　life-long　learning,　and　proposes　that　a　sparsely　distributed　and　statistically　independent　representation　provides　a　valid　criterion　for　the　self-organizing　classification　and　integration　of　context　signals.　This　representation　is　beneficial　for　learning　in　the　cerebellum　by　simplifying　the　credit　assignment　problem　between　what　must　be　learned　and　the　relevant　signals　in　the　current　context　for　learning　it　and　for　life-long　learning　by　reducing　the　destructive　interference　across　tasks,　while　retaining　the　ability　to　generalize.　©　2006　IEEE.