Cell Type Differences in Intrinsic Membrane Properties Regulate Responsiveness and Energy Consumption.

摘要

Information processing in the brain depends upon the interactions of diverse signaling units, neurons, which vary in their circuit positions, biophysical and biochemical properties, and electrophysiological phenotypes. These distinguishing properties enable neurons to serve different computational and functional roles. For instance, the fast-spiking inhibitory subclasss, which synchronizes its firing at fast frequencies and makes divergent and convergent contacts onto the persimomatic compartments of its targets, is hypothesized to play an important role in regulating the timing and probability of spike output and thus the flow of information through the brain (reviewed in Chapter 1). However, the functional consequences of synchronized inhibition will depend on the integrative properties and circuit locations of the recipient neurons.;Using a combination of in-vitro whole cell electrophysiology, modeling, and model-neuron hybrids (dynamic clamp), we determined how fast synchronized inhibition interacts with integrative properties of a variety of neuronal types to regulate the rate (Chapter 2) and timing (Chapter 3) of spike generation. We find that a neuron's intrinsic physiology substantially affects the ability of fast synchronized inhibition to control neuronal responsiveness. In addition, we demonstrate how the relevant physiology can be flexibly altered by contextual and neuromodulatory factors. The results of these experiments suggest that synchronized fast-spiking activity can differentially affect various circuit elements and each element's responsiveness can be adjusted on a range of time scales to suit the cortex's changing computational requirements.;Because the brain is one of the most energetically expensive organs in the body with action potential generation accounting for significant portion of the energy usage, we hypothesized that differences in neuronal properties may also serve to minimize energy consumption subject to functional constraints. Again using electrophysiology, modeling, and dynamic clamp, we compared the energy needed to produce action potentials singly and in trains for a wide range of channel densities and kinetic parameters, and examined which combinations of parameters maximized spiking function while minimizing energetic cost (Chapter 4). We found evidence supporting our hypothesis in a wide range of neurons from several species. We conclude that neuronal biophysics are tuned to perform cost-effective functions.