Topic: performance-energy trade-off

Recent work (Aitchison et al. 2021) suggests that synaptic plasticity performs Bayesian inference and that, moreover, variability in synaptic efficacy might reflect uncertainty in the synaptic weight. Here, we consider whether similar phenomena emerge merely from minimizing synapse energetic costs during learning. Following established biophysical principles, we assume that noisy synaptic transmission is energetically cheaper than precise transmission. However, stochastic synapses may be detrimental to performance because noisy transmission adds randomness to a neural network’s activity, and s

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