abstract = "The work describes the formulation of immuno-inspired
mechanisms to continuously evolve, cache, manage and
evict several Artificial Neural Network (ANN) based
robot controllers, within disparate Halls-of-Fame,
thereby facilitating Embodied Life-long learning in
robots. The work also introduces a novel concept termed
Mutational Puissance to enhance learning in ANN based
controllers that use neuro-evolution. Further, unlike
the conventional layer-wise transfers conducted in
ANN-based Transfer Learning, a new immunology inspired
Neuronal-level Transfer Learning technique has also
been described. The technique aids in identifying
neurons that play a more significant role during the
learning phase. These, so-called, hot neurons, when
transferred to target ANNs hasten learning convergence,
especially when the source and target dataset domains
are dissimilar. Transfer of such neurons has also
proved to be effective while learning in scenarios
involving robots.",