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Abstract Slow waves are the main oscillatory activity during non-rapid eye movement sleep, playing a crucial role in synaptic homeostasis, memory consolidation, and cortical network regulation. In urethane-anesthetized rodents, frequently used in long-term potentiation experiments in vivo, brain activity alternates between a deactivated state, dominated by slow waves, and an activated state characterized by faster oscillations. However, the interplay between synaptic plasticity and slow wave dynamics within these fluctuating brain states remains unclear. Here, we investigated whether long-term potentiation in the hippocampal–medial prefrontal cortex (CA1-mPFC) circuit, critical for memory processing, modulates cortical slow wave dynamics during urethane anesthesia. Electrical stimulation of CA1 robustly evoked a transient (50 to 200 ms) event of population silence (down states) in the medial prefrontal cortex, which emerged after the typical evoked field post-synaptic potential, and was consistently associated with a slow waves. Importantly, long-term potentiation induction significantly enhanced the CA1-evoked medial prefrontal cortex down states and slowed spontaneous cortical slow wave oscillations, without altering the activated/deactivated brain state proportion. Our findings provide direct evidence of an interaction between synaptic plasticity and cortical slow wave dynamics, laying the groundwork for future studies on the underlying mechanisms of state-dependent synaptic homeostasis.
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This content will become publicly available on December 1, 2026
A simple model for Behavioral Time Scale Synaptic Plasticity (BTSP) provides content addressable memory with binary synapses and one-shot learning
Abstract Recent experimental studies in the awake brain have identified a rule for synaptic plasticity that is instrumental for the instantaneous creation of memory traces in area CA1 of the mammalian brain: Behavioral Time scale Synaptic Plasticity. This one-shot learning rule differs in five essential aspects from previously considered plasticity mechanisms. We introduce a transparent model for the core function of this learning rule and establish a theory that enables a principled understanding of the system of memory traces that it creates. Theoretical predictions and numerical simulations show that our model is able to create a functionally powerful content-addressable memory without the need for high-resolution synaptic weights. Furthermore, it reproduces the repulsion effect of human memory, whereby traces for similar memory items are pulled apart to enable differential downstream processing. Altogether, our results create a link between synaptic plasticity in area CA1 of the hippocampus and its network function. They also provide a promising approach for implementing content-addressable memory with on-chip learning capability in highly energy-efficient crossbar arrays of memristors.
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- Award ID(s):
- 2318152
- PAR ID:
- 10570625
- Publisher / Repository:
- Nature Portfolio
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 16
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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