Interacting synapses stabilise both learning and neuronal dynamics in biological networks

Abstract

The brains functionality is developed and maintained through synaptic plasticity. As synapses undergo plasticity they also affect each other. The nature of such "codependency" is difficult to disentangle experimentally, because multiple synapses must be monitored simultaneously. To help understand the experimentally observed phenomena, we introduce a framework that formalises synaptic codependency between different connection types. The resulting model explains how inhibition can gate excitatory plasticity, while neighbouring excitatory-excitatory interactions determine the strength of long-term potentiation. Furthermore, we show how the interplay between excitatory and inhibitory synapses can account for the quick rise and long-term stability of a variety of synaptic weight profiles, such as orientation tuning and dendritic clustering of co-active synapses. In recurrent neuronal networks, codependent plasticity produces rich and stable motor cortex-like dynamics with high input sensitivity. Our results suggest an essential role for the neighbourly synaptic interaction during learning, connecting micro-level physiology with network-wide phenomena.