Consequences of converting graded to action potentials upon neural information coding and energy efficiency.
Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na(+) and K(+) channels, with generator potential and graded potential models lacking voltage-gated Na(+) channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na(+) channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a 'footprint' in the generator potential that obscures incoming signals. These three processes reduce information rates by ∼50% in generator potentials, to ∼3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation.
Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom ; Centre for Neuroscience, Indian Institute of Science, Bangalore, India.
Biswa
B
Sengupta
Department of Zoology, University of Cambridge, Cambridge, United Kingdom.
Simon Barry
SB
Laughlin
School of Life Sciences and Centre for Computational Neuroscience and Robotics, University of Sussex, Falmer, Brighton, United Kingdom.
Jeremy Edward
JE
Niven
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D048429:Cell Size; D055724:Electrophysiological Phenomena; D004734:Energy Metabolism / Q000502:physiology*; D006801:Humans; D016014:Linear Models; D008959:Models, Neurological; D009415:Nerve Net; D009474:Neurons / Q000502:physiology*; D024642:Potassium Channels, Voltage-Gated / Q000378:metabolism*; D013269:Stochastic Processes; D061566:Voltage-Gated Sodium Channels / Q000378:metabolism*
D024642:Potassium Channels, Voltage-Gated; D061566:Voltage-Gated Sodium Channels