Abstract
Neurons exhibit diverse intrinsic dynamics, which govern how they integrate synaptic inputs to produce spikes. Intrinsic dynamics are often plastic during development and learning, but the effects of these changes on stimulus encoding properties are not well known. To examine this relationship, we simulated auditory responses to zebra finch song using a linear-dynamical cascade model, which combines a linear spectrotemporal receptive field with a dynamical, conductance-based neuron model, then used generalized linear models to estimate encoding properties from the resulting spike trains. We focused on the effects of a low-threshold potassium current (KLT) that is present in a subset of cells in the zebra finch caudal mesopallium and is affected by early auditory experience. We found that KLT affects both spike adaptation and the temporal filtering properties of the receptive field. The direction of the effects depended on the temporal modulation tuning of the linear (input) stage of the cascade model, indicating a strongly nonlinear relationship. These results suggest that small changes in intrinsic dynamics in tandem with differences in synaptic connectivity can have dramatic effects on the tuning of auditory neurons.
Highlights
We investigated this in the context of the zebra finch auditory cortex, where early exposure to a complex acoustic environment causes increased expression of a low-threshold potassium current
The absence of temporal correlations in this stimulus is ideal for obtaining unbiased estimates of the generalized linear model (GLM) parameters, allowing us to determine how intrinsic dynamics affect encoding in a best-case scenario
We looked first at the effects of dynamics on receptive field (RF) temporal structure, the extent to which the estimated temporal modulation transfer functions (tMTFs) was attenuated at low frequencies compared to the input tMTF (Δl)
Summary
A simple, single-compartmental model that can produce common physiological behaviors like bursting, adaptation, or rebound spiking, is a system of around ten or more nonlinear differential equations, with fifty or more parameters [15, 16]. These parameters correspond to specific aspects of the cell biology (such as membrane capacitance or sodium channel density), which makes them easy to interpret and, in some cases, possible to measure directly. Access to the intracellular voltage is needed, through a sharp or patch electrode or using an optical sensor [22], which greatly limits the number of neurons that can be modeled within the context of a circuit, and almost always requires the use of ex vivo preparations that cannot be presented with realistic stimuli
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