ArticlePLoS computational biology2026
Learning, sleep replay and consolidation of contextual fear memories: A neural network model.
Article in PLoS computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Contextual fear conditioning is an experimental framework widely used to investigate how aversive experiences affect the valence an animal associates with an environment. While the initial formation of associative context-fear memories is well studied - dependent on plasticity in hippocampus and amygdala - the neural mechanisms underlying their subsequent consolidation remain less understood. Recent evidence suggests that the recall of contextual fear memories shifts from hippocampal-amygdalar to amygdalo-cortical networks as they age. This transition is thought to rely on sleep. In particular, neural replay during hippocampal sharp-wave ripple events seems crucial, though open questions regarding the involved neural interactions remain. Here, we propose a biologically informed neural network model of context-fear learning. It expands the scope of previous models through the addition of a sleep phase. Hippocampal representations of context, formed during wakefulness, are replayed in conjunction with cortical and amygdalar activity patterns to establish long-term fear memories. In addition, valence-coding synapses within the amygdala are subject to homeostatic plasticity overnight, which stabilizes fear associations and regulates the fear circuitry's synaptic density. The model reproduces experimentally observed phenomena, including context-dependent fear renewal and time-dependent increases in fear generalisation. Our model integrates mechanisms of fear learning, systems consolidation and synaptic homeostasis to provide a unified account of how contextual fear memories form and evolve over time. Our framework yields testable predictions about how disruptions in synaptic homeostasis may promote a persistent, fear-sensitized state. Accounting for neural mechanisms that reshape fear memories after their formation is a step towards bridging computational models of fear learning and the mechanisms behind trauma and anxiety disorders.
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