Ten Ways To A Better Brain

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• Simon Laughlin, Department of Zoology, University of Cambridge
• Monday 17 February 2014, 16:30-18:00
• Hodgkin-Huxley Room, Department of Physiology Development and Neuroscience.

If you have a question about this talk, please contact P.H. Marchington.

Sengupta B, Laughlin SB, Niven JE (2014) Consequences of Converting Graded to Action Potentials upon Neural Information Coding and Energy Efficiency. PLoS Comput Biol 10(1): e1003439. doi:10.1371/journal.pcbi.1003439

Abstract 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.

This talk is part of the Adrian Seminars in Neuroscience series.

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