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For each single neuron the number of spikes occurring in each bin was counted and the resulting discrete time series represented the neuron firing rate.
Scatter plots and correlation coefficients for each single neuron were calculated (Additional file 1: Figure S2A).
The total recording time for each single neuron ranged between 1 h and 3 h.
For each single neuron or multiple neurons, we determined the optimum stimulus color, shape, position, and size.
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Despite the merits of activity ratios for appraising sparseness, separate equations for the sparseness of, for example, a single neuron and a neuronal population need to be used, causing comparison and contrast difficulties.
In these models (commonly referred to as "state-dependent networks" or "liquid state machines") the distributed synaptic dynamics encodes input history in an analogous way as we discussed in this paper for a single neuron: each input to the network will have an effect that depends upon the previous network history.
In each panel of Figure 1C, we plot the raster for a single neuron across multiple trials; the group to which each neuron belongs is indicated in each panel.
In [6, 8], each v variable denotes the average voltage over a synchronizedneuronal population, h is the inactivation of a persistent sodium currentfor members of the inspiratory pre-BötC population, and the m i represent the activation levels of an adaptation current for twoother respiratory populations; however, each variable could just as easily representanalogous quantities for a single neuron.
For the single neuron analysis, we derived estimates of the evidence-independent response for each neuron and the mean RT for the corresponding behavioral session.
For a single neuron presented with a set of stimuli S, Bienenstock et al.
However, this is why we included neuroblastoma cells as model for a "single neuron system".
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