Switches to slow rhythmic neuronal activity lead to a plasticity-induced reset in synaptic weights

Jacquerie, Kathleen; Minne, Caroline; Ponnet, Juliette; Benghalem, Nora; Sacré, Pierre; Drion, Guillaume

doi:10.1101/2022.07.15.500198

Cited by 2 publications

(1 citation statement)

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“…2023). This framing could also be extended to describe synaptic consolidation, through which transient early plasticity is consolidated into persistent, graded late plasticity in the same synapse (Jacquerie et al . 2023; Leimer et al .…”

Section: Discussionmentioning

confidence: 99%

Synaptic weight dynamics underlying memory consolidation: implications for learning rules, circuit organization, and circuit function

Bhasin,

Raymond,

Goldman

2024

Preprint

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Systems consolidation is a common feature of learning and memory systems, in which a long-term memory initially stored in one brain region becomes persistently stored in another region. We studied the dynamics of systems consolidation in simple circuit architectures modeling core features of many memory systems: an early- and late-learning brain region and two sites of plasticity. We show that the synaptic dynamics of the circuit during consolidation of an analog memory can be understood as a temporal integration process, by which transient changes in activity driven by plasticity in the early-learning area are accumulated into persistent synaptic changes at the late-learning site. This simple principle leads to two constraints on the circuit operation for consolidation to be implemented successfully. First, the plasticity rule at the late-learning site must stably support a continuum of possible outputs for a given input. We show that this is readily achieved by heterosynaptic but not standard Hebbian rules, that it naturally leads to a speed-accuracy tradeoff in systems consolidation, and that it provides a concrete circuit instantiation for how systems consolidation solves the stability-plasticity dilemma. Second, to turn off the consolidation process and prevent erroneous changes at the late-learning site, neural activity in the early-learning area must be reset to its baseline activity. We propose two biologically plausible implementations for this reset that suggest novel roles for core elements of the cerebellar circuit.Significance StatementHow are memories transformed over time? We propose a simple organizing principle for how long term memories are moved from an initial to a final site of storage. We show that successful transfer occurs when the late site of memory storage is endowed with synaptic plasticity rules that stably accumulate changes in activity occurring at the early site of memory storage. We instantiate this principle in a simple computational model that is representative of brain circuits underlying a variety of behaviors. The model shows how neural circuits can store new memories while preserving core features of older ones, and suggests novel roles for core elements of the cerebellar circuit.

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Section: Discussionmentioning

confidence: 99%