Spike generation estimated from stationary spike trains in a variety of neurons in vivo
Research output: Contribution to journal › Article
To any model of brain function, the variability of neuronal spike firing is a problem that needs to be taken into account. Whereas the synaptic integration can be described in terms of the original Hodgkin-Huxley (H-H) formulations of conductance-based electrical signaling, the transformation of the resulting membrane potential into patterns of spike output is subjected to stochasticity that may not be captured with standard single neuron H-H models. The dynamics of the spike output is dependent on the normal background synaptic noise present in vivo, but the neuronal spike firing variability in vivo is not well studied. In the present study, we made long-term whole cell patch clamp recordings of stationary spike firing states across a range of membrane potentials from a variety of subcortical neurons in the non-anesthetized, decerebrated state in vivo. Based on the data, we formulated a simple, phenomenological model of the properties of the spike generation in each neuron that accurately captured the stationary spike firing statistics across all membrane potentials. The model consists of a parametric relationship between the mean and standard deviation of the inter-spike intervals, where the parameter is linearly related to the injected current over the membrane. This enabled it to generate accurate approximations of spike firing also under inhomogeneous conditions with input that varies over time. The parameters describing the spike firing statistics for different neuron types overlapped extensively, suggesting that the spike generation had similar properties across neurons.
|Research areas and keywords||
Subject classification (UKÄ) – MANDATORY
|Journal||Frontiers in Cellular Neuroscience|
|Publication status||Published - 2014|
No data available
Related research output
Cursed complexity. Computational properties of subcortical neuronal microcircuitry in sensorimotor controlSpanne, A., 2015, Neural basis for sensorimotor control. 167 p.
Research output: Thesis › Doctoral Thesis (compilation)