Noise-driven neuroplasticity in self-organizing feature maps: a neurocomputational model of phantom limbs.

The term "phantom limb" denotes the sensation that an extremity is present although it has been lost. A number of clinical features and recent findings of cortical map plasticity after destruction of afferent pathways (deafferentation) suggest that phantom limbs are caused by large-scale cortical reorganization processes. However, in paraplegics, who also suffer from cortical deafferentation, phantom sensations rarely develop, and if they do, they are weak, lacking in detail, and delayed, occurring after months. This has been taken to suggest a non-cortical genesis of phantom limbs. This article proposes a biologically plausible minimal neural network model to solve this apparent puzzle. Deafferentation was simulated in trained self-organizing feature maps. Reorganization was found to be directed by input noise. According to the model, the production of input noise by the deafferented primary sensory neuron promotes cortical reorganization in amputees. No such noise is generated or conducted to the cortex in paraplegics.