Firing properties of a stochastic PDE model of a rat sensory cortex layer 2/3 pyramidal cell. (English) Zbl 1038.92008
Summary: We have developed a nonlinear stochastic PDE (partial differential equation) model of a rat layer 2/3 somatosensory pyramidal neuron which approximates several of the dynamical properties of these cells. The model distinguishes telodendrites, a myelinated axon, initial segment, hillock, soma and a simplified dendritic tree. Distributions and properties of excitatory and inhibitory synapses were included, in accordance with recent anatomical and physiological findings. Using simulation methods, we aim to show that the spatial separation between regions of spatially distributed randomly activated excitatory and inhibitory synaptic inputs may be an important parameter which can influence neuronal firing properties.
Due to the complexity of the problem, with respect to configurations of spatially and temporally activated excitatory and inhibitory synaptic inputs, we consider two simple configurations in which the spatial region of activated excitatory and inhibitory synaptic inputs overlap and when they are far from each. In the first, denoted configuration \(\mathbf A\), activated excitatory and inhibitory synapses were located close to the soma. In the second, denoted configuration \(\mathbf B\), active inhibitory synapses were close to the soma, while active excitatory synapses were located on distal regions of the dendrite. For the first configuration, we find that increases in the mean rate of inhibition result in an increase in the width of the firing rate tuning curves, and that for particular mean input frequencies of excitation, increasing the mean input rate of inhibition does not always imply that the neuron fires at a slower rate.
Furthermore, we observed for mean input frequencies of excitation between 15 and 60 (Hz), that increasing the mean rate of inhibition resulted in the linearization of the firing rate over this interval. For configuration \(\mathbf B\), no increase in width nor a linearization effect via inhibition was observed. These differences indicate that the distance between regions of active excitatory and inhibitory synapses may be an important factor to consider in determining how the interaction between excitation and inhibition contributes to neuronal firing.
Due to the complexity of the problem, with respect to configurations of spatially and temporally activated excitatory and inhibitory synaptic inputs, we consider two simple configurations in which the spatial region of activated excitatory and inhibitory synaptic inputs overlap and when they are far from each. In the first, denoted configuration \(\mathbf A\), activated excitatory and inhibitory synapses were located close to the soma. In the second, denoted configuration \(\mathbf B\), active inhibitory synapses were close to the soma, while active excitatory synapses were located on distal regions of the dendrite. For the first configuration, we find that increases in the mean rate of inhibition result in an increase in the width of the firing rate tuning curves, and that for particular mean input frequencies of excitation, increasing the mean input rate of inhibition does not always imply that the neuron fires at a slower rate.
Furthermore, we observed for mean input frequencies of excitation between 15 and 60 (Hz), that increasing the mean rate of inhibition resulted in the linearization of the firing rate over this interval. For configuration \(\mathbf B\), no increase in width nor a linearization effect via inhibition was observed. These differences indicate that the distance between regions of active excitatory and inhibitory synapses may be an important factor to consider in determining how the interaction between excitation and inhibition contributes to neuronal firing.
MSC:
| 92C20 | Neural biology |
| 60H15 | Stochastic partial differential equations (aspects of stochastic analysis) |
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