Renaud Jolivet, Département de Physique Nucléaire et corpusculaire, Université de Genève
Energy-efficient information transfer in neural networks
The nervous system consumes a disproportionate fraction of the resting body’s energy production. In humans, the brain represents 2% of the body’s mass, yet it accounts for ~20% of the total oxygen consumption. Expansion in the size of the brain relative to the body and an increase in the number of connections between neurons during evolution underpin our cognitive powers and are responsible for our brains’ high metabolic rate. Despite the significance of energy consumption in the nervous system, how energy constraints and shapes brain function is often under-appreciated. I will illustrate the importance of brain energetics and metabolism, and discuss how the brain trades information for energy savings in the visual pathway. Indeed, a significant fraction of the information those neurons could transmit in theory is not passed onto the next step in the visual processing hierarchy. I will discuss how this can be explained by considerations of energetic optimality. Finally, I will briefly discuss how energetic constraints might impact coding strategies in neural networks.