Short-term and spike-timing-dependent plasticity facilitate the formation of modular neural networks

•We study the combined effect of short-term (STP) and spike-timing-dependent plasticity (STDP) on the synaptic strength of excitatory coupled Hodgkin-Huxley neurons.•We show that plasticity can facilitate the formation of modular neural networks with complex topologies that resemble those of networks with preferential attachment properties.•Our results suggest that the combined effect of STP and STDP with long recovery times facilitates the formation of connections among neurons with similar spike frequencies only, a kind of preferential attachment. The brain has the phenomenal ability to reorganise itself by forming new connections among neurons and by pruning others. The so-called neural or brain plasticity facilitates the modification of brain structure and function over different time scales. Plasticity might occur due to external stimuli received from the environment, during recovery from brain injury, or due to modifications within the body and brain itself. In this paper, we study the combined effect of short-term (STP) and spike-timing-dependent plasticity (STDP) on the synaptic strength of excitatory coupled Hodgkin-Huxley neurons and show that plasticity can facilitate the formation of modular neural networks with complex topologies that resemble those of networks with preferential attachment properties. In particular, we use an STDP rule that alters the synaptic coupling intensity based on time intervals between spikes of postsynaptic and presynaptic neurons. Previous work has shown that STDP may induce the emergence of directed connections from high to low frequency spiking neurons. On the other hand, STP is attributed to the release of neurotransmitters in the synaptic cleft of neurons that alter its synaptic efficiency. Our results suggest that the combined effect of STP and STDP with long recovery times facilitates the formation of connections among neurons with similar spike frequencies only, a kind of preferential attachment. We then pursue this further and show that, when starting with all-to-all neural configurations, depending on the STP recovery time and distribution of neural frequencies, modular neural networks can emerge as a direct result of the combined effect of STP and STDP.