bioRxiv2022
Modern, high-density neuronal recordings reveal at ever higher precision how information is represented by neural populations. Still, we lack the tools to understand these processes bottom-up, emerging from the biophysical properties of neurons, synapses, and network structure. The concept of the dynamic gain function, a spectrally resolved approximation of a populations coding capability, has the potential to link cell-level properties to network-level performance. However, the concept is not only useful but also very complex because the dynamic gains shape is co-determined by axonal and somatodendritic parameters and the populations operating regime. Previously, this complexity precluded an understanding of any individual parameters impact. Here, we decomposed the dynamic gain function into three components corresponding to separate signal transformations. This allowed attribution of network-level encoding features to specific cell-level parameters. Applying the method to data from real neurons and biophysically plausible models, we found: 1. The encoding bandwidth of real neurons, approximately 400 Hz, is constrained by the voltage dependence of axonal currents during early action potential initiation. 2. State-of-the-art models only achieve encoding bandwidths around 100 Hz and are limited mainly by subthreshold processes instead. 3. Large dendrites and low-threshold potassium currents modulate the bandwidth by shaping the subthreshold stimulus-to-voltage transformation. Our decomposition provides physiological interpretations when the dynamic gain curve changes, for instance during spectrinopathies and neurodegeneration. By pinpointing shortcomings of current models, it also guides inference of neuron models best suited for large-scale network simulations. Significant StatementThe dynamic gain function quantifies how neurons can engage in collective, network-level activity, shape brain rhythms and information encoding. Its shape results from a complex interaction between properties of different molecules (ion channels) and cell compartments (morphology, resistance), and is so far only understood for the simplest neuron models. Here we provide an interpretable analysis, decomposing the dynamic gain based on the stimulus transformation steps in individual neurons. We apply the decomposition to data from real neurons and complex models, and attribute changes of the dynamic gain to specific sub- and suprathreshold processes. Using this decomposition method, we reveal the relevance of subthreshold potassium channels for ultrafast information encoding and expose the shortcomings of even the state-of-the-art neuron models.