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

By Friedemann Zenke, Everton J. Agnes, Wulfram Gerstner. 2015

366 citations recorded by the source corpus.

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. The brain exhibits a diversity of plasticity mechanisms across different timecales that constitute the putative basis for learning and memory. Here, the authors demonstrate how these different plasticity mechanisms are orchestrated to support the formation of robust and stable neural cell assemblies.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

The temporal paradox of Hebbian learning and homeostatic plasticity (2017, background): “This article demonstrates stable learning and transient recall ( 100 ms) of Hebbian cell assemblies in a recurrent network model through the combination of a plausible model of stdp with two homeostatic processes: a fast one that is implemented as a renormalization of the sum of all synaptic weights”
SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks (2017, methodology): “Since, we observed abnormally high ﬁring rates in hidden layer neurons in networks trained with random feedback, we wondered whether performance could be improved through the addition of a heterosynaptic weight decay (Methods) which acts as an activity regularizer (Zenke et al., 2015).”
Continual Learning Through Synaptic Intelligence (2017, background): “While this complexity has been surmised to serve memory consolidation (Fusi et al., 2005; Lahiri & Ganguli, 2013; Zenke et al., 2015; Ziegler et al., 2015; Benna & Fusi, 2016), few studies have illustrated how it benefits learning in ANNs.”
Hebbian and Homeostatic Plasticity Mechanisms in Regular Spiking and Intrinsic Bursting Cells of Cortical Layer 5 (2015, critical): “Recent studies have questioned whether synaptic scaling can be involved in balancing Hebbian plasticity, however, because (a) synaptic scaling acts too slowly to stabilize the neuron following Hebbian plasticity and (b) blocking Hebbian plasticity does not lead to changes in synaptic weights (Chisti”
Partial Breakdown of Input Specificity of STDP at Individual Synapses Promotes New Learning (2016, result): “This is consistent with results of a recent study, which showed that orchestrated action of multiple forms of plasticity, including heterosynaptic plasticity, is necessary for formation of assemblies in a model of a recurrent spiking network (Zenke et al., 2015).”