Memory consolidation and representational drift

SUMMARY Memory consolidation is the process by which temporary, malleable memories are transformed into more stable, longer-lasting forms. On a coarse anatomical scale, consolidation redistributes memories in the brain, but it remains poorly understood how these changes manifest themselves on the finer, cellular scale of neuronal engrams and how they relate to the cognitive level. In this study, we developed a phenomenological model of engram dynamics under systems consolidation. The model describes consolidation as a brain-wide phenomenon, where memories deterministically follow a trajectory through a space of patterns distributed among brain regions. It captures a broad range of features of memory consolidation, including selective consolidation, semantization, and power-law forgetting. In the model, consolidation is accompanied by population-level changes in neuronal representations that resemble the widely observed phenomenon of representational drift. When only a subset of neurons is observed, the deterministic dynamics of the model can appear stochastic, and a readout of task features deteriorates over time even when a stable readout exists for the full system. Our model offers a dynamical systems perspective on memory consolidation as a distributed process, moving beyond the classic region-centered view, and provides a functional interpretation of drift as a means of redistributing engrams for improved memory retention. Competing Interest Statement The authors have declared no competing interest. Footnotes ↵7 Lead contact ↵8 Senior author We updated the mathematical notation to be consistent across main text and supplementary material. We changed figure 4 to show better examples of the diversity of single neuron tuning changes. We reordered panels in figure 5 for clarity and added edge angle analysis for random stimulus correlations and a supplementary figure with remaining random correlation analyses not shown in the main figure. We also discuss these results now in the main text, and updated the methods accordingly. We updated and reordered some citations in the introduction and discussion. ↵1 The anti-symmetric structure of H implies that for each eigenvalue Ei ∈ ℝ, there is one of opposite sign −Ei. ↵2 Given the semicircle distribution , the Stieltjes transform reads ↵3 Since the function ℰ (ω) is continuous, monotonic and limω→±∞ ℰ (ω) = ±∞, it has only one zero.