Energetics of BCM type synaptic plasticity and storing of accurate information
AbstractExcitatory synaptic signaling in cortical circuits is thought to be metabolically expensive. Two fundamental brain functions, learning and memory, are associated with long-term synaptic plasticity, but we know very little about energetics of these slow biophysical processes. This study investigates the interplay between stochastic BCM type synaptic plasticity, its metabolic requirements, and the accuracy and retention of stored information in synaptic weights, within the frameworks of stochastic dynamical systems and nonequilibrium thermodynamics. The dynamic mean-field is derived for the synaptic weights, and it is found that the energy used by plastic synapses, related to their information content, is primarily caused by fluctuations in the synaptic weights and in presynaptic firing activity. Such information-related plasticity energy rate, together with the accuracy of stored information depend nonlinearly on key neurophysiological parameters, which is due to bistability in the system: synapses plus their postsynaptic neuron. At the onset of bistability, the memory lifetime, its accuracy, and plasticity energy rate are all positively correlated and exhibit sharp peaks. However, in the bistable regime, the accuracy of encoded information and plasticity energetics are generally anticorrelated, which suggests that a precise storing of synaptic information neither has to be metabolically expensive nor it is limited by energy consumption. Interestingly, such a limit on synaptic coding accuracy is imposed instead by a derivative of the plasticity energy rate with respect to the presynaptic firing, and this relationship has a general character that is independent of the plasticity type. An estimate for primate neocortex reveals that a relative metabolic cost of BCM type synaptic plasticity, as a fraction of the overall neuronal cost, can vary from negligible to substantial, depending on a synaptic working regime and presynaptic firing.