The modular and hierarchical organization of the brain is believed to support the coexistence of segregated (specialization) and integrated (binding) information processes. A relevant question is yet to understand how such architecture naturally emerges and is sustained over time, given the plastic nature of the brain’s wiring. Following evidences that the sensory cortices organize into assemblies under selective stimuli, it has been shown that stable neuronal assemblies can emerge due to targeted stimulation, embedding various forms of synaptic plasticity in presence of homeostatic and/or control mechanisms. Here, we show that simple spike-timing-dependent plasticity (STDP) rules, based only on pre- and post-synaptic spike times, can also lead to the stable encoding of memories in the absence of any control mechanism. We develop a model of spiking neurons, trained by stimuli targeting different sub-populations. The model satisfies some biologically plausible features: (i) it contains excitatory and inhibitory neurons with Hebbian and anti-Hebbian STDP; (ii) neither the neuronal activity nor the synaptic weights are frozen after the learning phase. Instead, the neurons are allowed to fire spontaneously while synaptic plasticity remains active. We find that only the combination of two inhibitory STDP sub-populations allows for the formation of stable modules in the network, with each sub-population playing a distinctive role. The Hebbian sub-population controls for the firing The model satisfies some biologically plausible features: (i) it contains excitatory and inhibitory neurons with Hebbian and anti-Hebbian STDP; (ii) neither the neuronal activity nor the synaptic weights are frozen after the learning phase. Instead, the neurons are allowed to fire spontaneously while synaptic plasticity remains active. We find that only the combination of two inhibitory STDP sub-populations allows for the formation of stable modules in the network, with each sub-population playing a distinctive role. The Hebbian sub-population controls for the firing activity, while the anti-Hebbian neurons promote pattern selectivity. In particular, we show that two different inhibitory sub-populations, one subject to Hebbian and the other to anti-Hebbian plasticity, are required to promote the formation of feedback and feed-forward inhibition circuits controlling memory consolidation. On one side, these inhibitory circuits favour long-term memory consolidation by inducing spontaneous memory recalls in the asynchronous irregular resting phase. On another side, the number of inhibitory neurons controls the maximal memory capacity of the considered model. Sensory cortices contain neurons selectively firing for different features of the inputs, forming differentiated groups of neurons (assemblies) related to receptive fields [ 25 ]. Following these observations, computational models of spiking neurons have been proposed to investigate how memories could be imprinted into the neuronal architecture [ 17 , 21 , 26 – 38 ]. Starting from a random connectivity, their goal is to reproduce the formation of neuronal assemblies in response to various external stimuli (or memories), mediated by synaptic plasticity. Results of the performed numerical experiments are reported for a network with all anti-Hebbian inhibitory neurons ( B ); with all Hebbian inhibitory neurons ( C ); with 50% anti-Hebbian and 50% Hebbian inhibitory neurons ( D ). Raster plots display the firing times of excitatory (red dots) and inhibitory (blue dots) neurons during the simulations. Matrices represent the temporal evolution of the connection weights at different times: t = 0s (random initialization of the weights), t = 20s (middle of the learning phase) and t = 40s (end of the learning phase). This internal feedback inhibition avoids that the excitatory neurons fire at too large rates. Meanwhile, the anti-Hebbian inhibitory neurons form lateral, feed-forward connections which shut down the firing of the other population, in other terms the sub-population I 1 A ( I 2 A ) inhibits all neurons in P 2 (P 1 ). Resting-state network dynamics after learning So far, we have shown that selective stimulation to distinct populations consistently gives rise to modular networks and that the resulting configuration depends on the type of plasticity affecting the inhibitory neurons. While in the case with only anti-Hebbian inhibition such a synchronized event would trigger the excitatory neurons to permanently increase their firing, here the presence of the internal feedback inhibition ( I 1 → E 1 and I 2 → E 2 ) avoids the constant synchronization of excitatory neurons, while it keeps their activity at low frequency. (c) Mixed inhibition. The post-learning behaviour in the mixed scenario is very similar to the Hebbian case, as shown in Fig 1D . However, in this case populations P 1 and P 2 are not independent but they inhibit each other. The model displays realistic firing patterns after the learning phase while the synaptic plasticity remains active—as it is the case in the brain in-vivo . To achieve these objectives, we have introduced a network of excitatory and inhibitory QIF neurons with plastic synapses following simple STDP rules based only on local information, i.e. the pre- and post-synaptic spike times [ 45 ]. By targeting stimuli to distinct sub-populations—mimicking the segregated projections of different features into early sensory layers—the network developed a stable modular connectivity. Unless otherwise specified, the network will be composed of 80% (20%) excitatory (inhibitory) neurons as usually observed in the human cortex [ 121 ]. Depending on the plasticity rules controlling the synaptic strengths of the connections, three neural sub-populations can be identified depending on the nature of the pre-synaptic neurons: excitatory neurons subject to asymmetric Hebbian STDP; inhibitory neurons subject to symmetric Hebbian STDP; inhibitory