Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding
Fig 2
The input-output relationship sharpens due to the adaptive threshold.
In previous studies, the dependence of spike threshold on previous membrane potential dynamics was most frequently linked to EPSP amplitude (AEPSP) or slope (SlEPSP). Both measures can be reliably predicted by the combination of temporal input spread (σ) and the number of contributing neurons (Ninputs) in point neurons. (A) The AEPSP (data: gray, mean: red) is predicted with small standard deviation (S.D.) (maroon) by σ and Ninputs across a wide range of values for the adaptive threshold model (A1). Prediction quality was similarly good for the fixed threshold model (mean: blue, S.D.: dark blue, A2). (B) Similarly, the SlEPSP amplitude is predicted well with small S.D. on the basis of σ and Ninputs, for both adaptive (B1) and fixed (B2). (C) Spike probability follows generally a similar shape as a function of σ and Ninputs. However, the speed of transition between spiking and non-spiking domain is overall greater for the adaptive threshold model (C1 vs.C2), translating into a steeper decision criterion as a function of the input parameters. (D) Spike probability as a function of only σ, is well fitted by a sigmoid, with the adaptive model (D1 vs. D2, for Ninput = 37) exhibiting a steeper slope as a function of different σ’s (adaptive: s = 0.18, fixed: s = 0.28) as well as a lower midpoint, indicating overall operation on a faster time-scale However, the inverse is the case for the dependence on Ninputs (E1 vs. E2, for σ = 2.5 ms). Error bars represent 2x SEMs and are barely visible, only 1/50 of points plotted in A/B to improve display.