Energy demands of diverse spiking cells from the neocortex, hippocampus, and thalamus.
It has long been known that neurons in the brain are not physiologically homogeneous. In response to current stimulus, they can fire several distinct patterns of action potentials that are associated with different physiological classes ranging from regular-spiking cells, fast-spiking cells, intrinsically bursting cells, and low-threshold cells. In this work we show that the high degree of variability in firing characteristics of action potentials among these cells is accompanied with a significant variability in the energy demands required to restore the concentration gradients after an action potential. The values of the metabolic energy were calculated for a wide range of cell temperatures and stimulus intensities following two different approaches. The first one is based on the amount of Na(+) load crossing the membrane during a single action potential, while the second one focuses on the electrochemical energy functions deduced from the dynamics of the computational neuron models. The results show that the thalamocortical relay neuron is the most energy-efficient cell consuming between 7 and 18 nJ/cm(2) for each spike generated, while both the regular and fast spiking cells from somatosensory cortex and the intrinsically-bursting cell from a cat visual cortex are the least energy-efficient, and can consume up to 100 nJ/cm(2) per spike. The lowest values of these energy demands were achieved at higher temperatures and high external stimuli.
Computational Intelligence Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU San Sebastián, Spain.
Abdelmalik
A
Moujahid
Computational Intelligence Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU San Sebastián, Spain.
Alicia
A
D'Anjou
Computational Intelligence Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU San Sebastián, Spain.
Manuel
M
Graña
action potential; computational models; neuron metabolic energy; overlap load; sodium entry