How synaptic strength, short-term plasticity, and input synchrony contribute to neuronal spike output
Fig 5
Uncorrelated activity of weak inputs enhances information transfer of strong synaptic inputs.
A Schematic of model setup with weak inputs removed. B Example spike train of the model cell in its default setup (grey), when weak inputs are entirely removed (orange), and when weak inputs are replaced by a more depolarized Vrest (red). C Schematic of model setup with strong inputs removed. D Example spike train of the model cell in its default setup (grey) and when strong inputs are removed (purple). E Pearson correlation coefficients of the 270 input spike trains with the output spike train of the model cell. Results of three model setups are shown: default simulation (grey; all inputs, as in Fig 4) and setups introduced in A (orange, weak inputs removed; red, weak inputs replaced with depolarized Vrest). Dots indicate means, shaded regions indicate standard deviation of correlation coefficients for 100 runs of the simulation. F Top, Pearson correlation coefficients between the strong synaptic inputs and the output spike train of the model neuron for the default simulation and setups introduced in A. Bottom, output firing rate of model cell for the default simulation and the setups introduced in A-C. (Data are averages across 100 simulation runs; median and 25–75% percentile indicated; non-parametric Kolmogorov-Smirnov test, * p < 0.05.) G Probability of output spiking as a function of the number of coincident spikes across all input spike trains (grey, default simulation; red, weak inputs replaced with depolarized Vrest).