Homeostatic plasticity is a system used by the brain to stabilize its population dynamics. This stabilization is accomplished by using feedback control to set neurons' average firing rates over a long timescale. A number of different mechanisms can implement this control: synaptic scaling multiplies the strengths of a neuron's synapses by an activity-dependent factor; intrinsic plasticity modifies the channel properties of the neuron itself; and metaplasticity modifies parameters of the learning rule. Many theoretical and simulation-based studies have shown that unstable population dynamics can result in systems without homeostatic control.

Homeostatic plasticity yields interesting design challenges for the neuromorphic engineer. For instance, the time constants over which homeostasis works are typically quite long, necessitating the design of circuits with low leakage current. This group will review previous neuromorphic circuit designs for homeostatic plasticity and discuss potential new directions based on recent results in computational neuroscience.

Last modified 4 years ago Last modified on 05/03/15 10:46:24