Plasticity is a key component of computational algorithms and neuroscience studies. Neuroscience research reveals a wide variety of plasticity mechanisms - i.e. homeostatic, synaptic (fast, slow, NMDA dependent etc.), and intrinsic plasticity. However, controversies still surround plasticity. How is plasticity governed and why is there so much variability? Can these findings be interpreted differently?
Feedback inhibition, where neurons feed back to their own inputs and inhibit them, is found throughout the brain including sensory processing regions. Within feedback networks, mechanisms required for plasticity are fundamentally different.
We will analyze plasticity from the perspective of feedback inhibition, review basic assumptions about plasticity, and motivate new recognition algorithms.