Neural adaptation and fractional dynamics as a window to underlying neural excitability
Fig 7
Spiking neuron network model with spike frequency adaptation (SFA) and synaptic depression (STD).
(a) Feedforward neural network includes 100 conductance-based neurons with multiple timescales of SFA and STD. (b) PSDs show degrees of “tilt” or crossing when input is increased, and STD is decreased. Increasing SFA effectively decreases STD. (c) Increased adaptation, whether SFA or STD, generally leads to decreased power. However, increased SFA leads to decreased STD (rightmost panel). (d) Increasing input increases overall power when SFA is present. However, when STD is present, increasing the input leads to decreased overall power. When both SFA and STD are present, nonlinear effects are evident.