Drift of neural ensembles driven by slow fluctuations of intrinsic excitability.

Representational drift refers to the dynamic nature of neural representations in the brain despite the behavior being seemingly stable. Although drift has been observed in many different brain regions, the mechanisms underlying it are not known. Since intrinsic neural excitability is suggested to play a key role in regulating memory allocation, fluctuations of excitability could bias the reactivation of previously stored memory ensembles and therefore act as a motor for drift. Here, we propose a rate-based plastic recurrent neural network with slow fluctuations of intrinsic excitability. We first show that subsequent reactivations of a neural ensemble can lead to drift of this ensemble. The model predicts that drift is induced by co-activation of previously active neurons along with neurons with high excitability which leads to remodeling of the recurrent weights. Consistent with previous experimental works, the drifting ensemble is informative about its temporal history. Crucially, we show that the gradual nature of the drift is necessary for decoding temporal information from the activity of the ensemble. Finally, we show that the memory is preserved and can be decoded by an output neuron having plastic synapses with the main region.

Intrinsic Neural Excitability Biases Allocation and Overlap of Memory Engrams.

Memories are thought to be stored in neural ensembles known as engrams that are specifically reactivated during memory recall. Recent studies have found that memory engrams of two events that happened close in time tend to overlap in the hippocampus and the amygdala, and these overlaps have been shown to support memory linking. It has been hypothesized that engram overlaps arise from the mechanisms that regulate memory allocation itself, involving neural excitability, but the exact process remains unclear. Indeed, most theoretical studies focus on synaptic plasticity and little is known about the role of intrinsic plasticity, which could be mediated by neural excitability and serve as a complementary mechanism for forming memory engrams. Here, we developed a rate-based recurrent neural network that includes both synaptic plasticity and neural excitability. We obtained structural and functional overlap of memory engrams for contexts that are presented close in time, consistent with experimental and computational studies. We then investigated the role of excitability in memory allocation at the network level and unveiled competitive mechanisms driven by inhibition. This work suggests mechanisms underlying the role of intrinsic excitability in memory allocation and linking, and yields predictions regarding the formation and the overlap of memory engrams.

Ideal refocusing of an optically active spin qubit under strong hyperfine interactions.

Combining highly coherent spin control with efficient light-matter coupling offers great opportunities for quantum communication and computing. Optically active semiconductor quantum dots have unparalleled photonic properties but also modest spin coherence limited by their resident nuclei. The nuclear inhomogeneity has thus far bound all dynamical decoupling measurements to a few microseconds. Here, we eliminate this inhomogeneity using lattice-matched GaAs-AlGaAs quantum dot devices and demonstrate dynamical decoupling of the electron spin qubit beyond 0.113(3) ms. Leveraging the 99.30(5)% visibility of our optical π-pulse gates, we use up to Nπ = 81 decoupling pulses and find a coherence time scaling of [Formula: see text]. This scaling manifests an ideal refocusing of strong interactions between the electron and the nuclear spin ensemble, free of extrinsic noise, which holds the promise of lifetime-limited spin coherence. Our findings demonstrate that the most punishing material science challenge for such quantum dot devices has a remedy and constitute the basis for highly coherent spin-photon interfaces.