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Computational Neuroscience Journal Club

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  • UserDavid Liu and Edward Young
  • ClockTuesday 05 April 2022, 13:30-15:00
  • HouseOnline on Zoom.

If you have a question about this talk, please contact Jake Stroud.

Please join us for our fortnightly journal club online via zoom where two presenters will jointly present a topic together. The next topic is ‘Energy consumption and neural coding precision trade-off’ presented by David Liu and Edward Young.

Zoom information: https://us02web.zoom.us/j/84958321096?pwd=dFpsYnpJYWVNeHlJbEFKbW1OTzFiQT09 Meeting ID: 849 5832 1096 Passcode: 506576

Summary: The brain is a remarkable information processing system, exhibiting learning abilities outside the reach of current state-of-the-art machine learning algorithms. Even more remarkable is the energy efficiency of such biological systems compared to our silicon devices. In this journal club, we discuss two papers on the metabolic cost aspects of information processing in the brain. First, we present the classic paper “The metabolic cost of neural information” by Laughlin et al. Nature Neuroscience (1998), which quantifies the energy usage of known neural coding schemes based on experimental data. Second, we discuss the recent paper “Neocortex saves energy by reducing coding precision during food scarcity” by Padamsey et al., Neuron (2022), where the authors show how the mammalian brain regulates its energy usage during food scarcity. This reveals a trade-off between energy usage and coding precision, regulated by changes in biochemical processes that lead to different electrophysiological properties of single neurons.

References:

“The metabolic cost of neural information” by Laughlin et al. Nature Neuroscience (1998).

“Neocortex saves energy by reducing coding precision during food scarcity” by Padamsey et al., Neuron (2022).

This talk is part of the Computational Neuroscience series.

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