Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding
Fig 6
An adaptive threshold neuron represents information more robustly across membrane states for a ‘response-centered’ time reference.
Since stimulus onset is not known internally, a population-based decoding reference has been suggested to serve a biologically relevant surrogate for stimulus timing [25]. With this reference, spike-timing is measured relative to the peak-time of the population response, i.e. local maxima of the population peristimulus time histogram (PSTH). (A1) For small differences in state, adaptive and fixed threshold models show little difference in their relative-time response and consequently the contribution of the knowledge about the state becomes negligible (B1). All colors are as in the preceding figure. (A2) For larger state differences, the response time distributions now differ significantly in their variance, with the fixed threshold model exhibiting a much larger increase in spread for the more hyperpolarized membrane state. Consequently, decoding across states becomes less robust for the fixed model than for the adaptive threshold model (B2). (C) While the ‘response-centered’ time reference increases the similarity of the PSTH across states for both the fixed and the adaptive threshold model, the latter profits more, widening the gap between the models for larger state differences. (D) As before, the correlation coefficient between the PSTHs remains a good predictor for the RI values. (E) In the case of the moving decoding reference, the adaptive model is generally more robust than the fixed threshold model across all state differences investigated, as indicated by a higher RI value. Error bars indicate SEMs across all state differences of a given size.