spike train auto-structure impacts post-synaptic firing and timing based plasticity

Cortical neurons are typically driven by several thousand synapses. The precise spatio-temporal pattern formed by these inputs can modulate the response of a post-synaptic cell. In this work, we explore how the temporal structure of pre-synaptic inhibitory and excitatory inputs impacts the post-synaptic firing of a conductance-based integrate and fire neuron. Both the excitatory and inhibitory input was modelled by renewal gamma processes with varying shape factors for modelling regular and temporally random Poisson activity. We demonstrate that the temporal structure of mutually independent inputs affects the post-synaptic firing, while the strength of the effect depends on the firing rates of both the excitatory and inhibitory inputs. In a second step we explore the effect of temporal structure of mutually independent inputs on a simple version of Hebbian learning, i.e. hard bound spike timing dependent plasticity. We explore both the equilibrium weight distribution and the speed of the transient weight dynamics for different mutually independent gamma processes. We find that both the equilibrium distribution of the synaptic weights and the speed of synaptic changes is modulated by the temporal structure of the input. Finally we highlight that the sensitivity of both the post-synaptic firing as well as the spike timing dependent plasticity on the auto structure of the input of a neuron could be used to modulate the learning rate of synaptic modification.