Depression allowed the motor neuron to produce graded responses over a wide range of presynaptic input strength.
Distal synaptic input always produces strong back-propagating spikes whereas proximal input could produce both forward and back-propagating spikes depending on the input strength.
The network then transitions, with increasing external input strength, between two regimes: (1) For weak activation, the network is very weakly coupled, with neuronal input dominated by external input (from outside the network) rather than network input.
Second, there may exist an optimal timing following a spike at which the input strength needed to elicit an additional spike is minimized.
The training input strength, (widehat {b}_{mathrm {pk}}), is proportional to the parameter (overline {b_{mathrm{pk}}}) in the megamap model (Eq. (2) and Eq. (6)).
Furthermore, leaky integrate-and-fire model exhibits a sublinear dependence of correlation coefficients on input strength r [18, 21], which we see confirmed in Fig. 4.
A surface of knees is then the surface of these local extrema, parameterized by the values of the synaptic input variables, for a fixed external input strength.
We find some characteristic neural dynamics with different input strength.
Importantly, the inhibitory LN input is adaptive, i.e., the inhibitory input strength depends on the overall ORN activity [13].
This bifurcation against the input strength will be experimentally confirmed by measuring the neural activity for different external stimuli.
Since young and mature DGCs display distinct excitability, spike properties and input strength, it would be predictable that they are differentially recruited into an active network.
