Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks

Abstract

Synaptic plasticity, the putative basis of learning and memory formation, manifests in various forms and across different timescales. Here we show that the interaction of Hebbian homosynaptic plasticity with rapid non-Hebbian heterosynaptic plasticity is, when complemented with slower homeostatic changes and consolidation, sufficient for assembly formation and memory recall in a spiking recurrent network model of excitatory and inhibitory neurons. In the model, assemblies were formed during repeated sensory stimulation and characterized by strong recurrent excitatory connections. Even days after formation, and despite ongoing network activity and synaptic plasticity, memories could be recalled through selective delay activity following the brief stimulation of a subset of assembly neurons. Blocking any component of plasticity prevented stable functioning as a memory network. Our modelling results suggest that the diversity of plasticity phenomena in the brain is orchestrated towards achieving common functional goals.

Two dimensional model of learning in spiking neural networks with homeostasis and reward

“…У біо-логічних мережах стабільність забезпечується багатьма механізмами через збалансовану активацію пригнічувальних нейронів [9], короткочасове при-гнічення чи швидку форму гомеостазу [10,11,12]. У математичних моделях хебівське правило має бути доповнене стабілізуючими механізмами, що зу-мовлює формування простої пам'яті в спайкових нейронних мережах [13].…”

“…Усі необхідні механізми об'єднуються в єдину модель лише на початку розвитку адекватних моделей, але вже отримано цікаві результати [13]. На-вчання без учителя і навчання з підкріпленням разом з механізмами стабіль-ності, реалізовані у спайковій нейронній мережі, мають перспективи щодо ефективного оброблення інформації і подальшого вдосконалення адаптив-них роботів.…”

DNA methylation and the regulation of stimulus-specific memory formation

“…local translation), homeostatic plasticity acts globally on the whole cell or neuronal network. Indeed, neuronal network models suggest that homoeostatic plasticity is important for preventing accelerating activity (Tully et al, 2014;Zenke et al, 2015) and allows the network to remain plastic.…”

“…Homeostatic plasticity has helped explain neuronal network function in studies modelling learning (Lazar et al, 2007;Zenke et al, 2015). Homeostatic plasticity comprises of mechanisms which adjust the excitability, activity and overall synaptic weight of individual neurons and the overall neuronal network (Schacher and Hu, 2014;Yin and Yuan, 2015).…”

“…Hebbian plasticity favours increased excitation and stably increased synaptic connections, but on the other hand the brain needs to remain a flexible system with a set overall activity. Recent models of neuronal networks highlight the importance of homoeostatic plasticity in keeping this balance (Tully et al, 2014;Zenke et al, 2015). Homeostatic plasticity describes changes in response to altered activity levels (e.g.…”