How spiking neural networks can flexibly trade off performance and energy use
Sander Keemink,William Podlaski,Nuno Calaim,Christian Machens · COSYNE 2022
Many engineered and biological systems must trade off performance and energy use, and the brain is no exception. Over long time-scales, evidence suggests that energy use is kept approximately stable by homeostatic firing-rate set-points, resulting in stable average performance levels. On shorter timescales, however, this stability can and should be deviated from, such as during attention when both neural activity and performance often increase (e.g., in predator situations, or when attending to small sensory details). It remains unclear how this fundamental performance-energy trade-off can be
Fri, Mar 18, 2022