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タイトル: Emergence and maintenance of modularity in neural networks with Hebbian and anti-Hebbian inhibitory STDP

概要: Brains connectivity reveals modular and hierarchical structures at various scales. This organization is typically believed to support the coexistence of segregation (specialization) and integration (binding) of information. Motivated by developmental processes, some authors have studied the self-organization of neural networks into modular hierarchies mediated by adaptive mechanism under spontaneous neural activity. Following evidence that the sensory cortices organize into assemblies under selective stimuli, other authors have shown that stable neural assemblies can emerge in random neural networks 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 to stimuli targeting different sub-populations. The model is intended to satisfy 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 modular organization in the network, with each sub-population playing a distinctive role. The Hebbian sub-population controls for the firing activity, while the anti-Hebbian one promotes pattern selectivity. After the learning phase, the network settles into an asynchronous irregular resting-state. This post-learning activity is associated with spontaneous memory recalls, which turn fundamental for the long-term consolidation of the learned memories. Due to its simplicity, the model, here introduced, can represent a test-bed for further investigations on the role played by STDP on memory storing and maintenance. Author summaryOne of the most remarkable qualities of the brain is its capacity to learn and adapt. How the learning process imprints and maintains memories, by shaping the architecture of connectivity among neurons in a constantly changing and dynamic environment, is a major question of neuroscience. Here, we explore the idea that the segregation of inputs received by a neural network, with inputs targeting distinct populations, is a key factor for shaping the architecture of the network. We find that the presence of inhibitory neurons is necessary for the emergence and the long-term maintenance of modularity in spiking neural networks with plasticity. 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 control the maximal memory capacity of the considered model.