Self-healing codes: how stable neural populations can track continually reconfiguring neural representations

Rule, Michael E; O’Leary, Timothy

doi:10.1101/2021.03.08.433413

Cited by 4 publications

(3 citation statements)

References 171 publications

(284 reference statements)

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“…To generate consistent perception, the visual system must cope with changes in the coding of visual information. 76,77 It has been suggested that a system that carries a high-dimensional distributed code may maintain its functionality under representational drift by either confining the drift to the null space of the code, or via a compensatory plasticity of the downstream reader. 29,32 In both cases, the similarities across representations of different stimuli are expected to be somewhat conserved over time, even under a significant change in the representations themselves.…”

Section: Discussionmentioning

confidence: 99%

Representational drift in the mouse visual cortex

2021

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Section: Discussionmentioning

confidence: 99%

Representational drift in the mouse visual cortex

2021

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“…Similarly, a closer scrutiny of the response of PPC neurons in the 'T-maze' task showed that drift is not confined to a "coding null space" (Rule, Loback, et al 2020). Hence, an adaptive readout mechanism which involves synaptic plasticity to track and compensate the drift is required to achieve stable behavior (Rule, Loback, et al 2020;Rule and O'Leary 2021). Whether and how such a mechanism is implemented in the brain remains an open question.…”

Section: Summary and Discussionmentioning

confidence: 99%

Coordinated drift of receptive fields during noisy representation learning

Qin

Farashahi

Lipshutz

et al. 2021

Preprint

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Long-term memories and learned behavior are conventionally associated with stable neuronal representations. However, recent experiments showed that neural population codes in many brain areas continuously change even when animals have fully learned and stably perform their tasks. This representational "drift" naturally leads to questions about its causes, dynamics, and functions. Here, we explore the hypothesis that neural representations optimize a representational objective with a degenerate solution space, and noisy synaptic updates drive the network to explore this (near-)optimal space causing representational drift. We illustrate this idea in simple, biologically plausible Hebbian/anti-Hebbian network models of representation learning, which optimize similarity matching objectives, and, when neural outputs are constrained to be nonnegative, learn localized receptive fields (RFs) that tile the stimulus manifold. We find that the drifting RFs of individual neurons can be characterized by a coordinated random walk, with the effective diffusion constants depending on various parameters such as learning rate, noise amplitude, and input statistics. Despite such drift, the representational similarity of population codes is stable over time. Our model recapitulates recent experimental observations in hippocampus and posterior parietal cortex, and makes testable predictions that can be probed in future experiments.

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“…In any case it remains unclear precisely how behavior remains unaffected by the observed drift. One potential solution to this is to allow for compensatory plasticity in downstream circuits so as to stabilize the readout performance 30,48,[50][51][52] . Another is to seek some higher-order population structure which remains stable in the face of ongoing RD 19,21,22,27,48,49 .…”

Section: Discussionmentioning

confidence: 99%

Representational drift as the consequence of ongoing memory storage

Devalle,

Zou,

Cecchini

et al. 2024

Preprint

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Memory systems with biologically constrained synapses have been the topic of intense theoretical study for over thirty years. Perhaps the most fundamental and far-reaching finding from this work is that the storage of new memories implies the partial erasure of already-stored ones. This overwriting leads to a decorrelation of sensory-driven activity patterns over time, even if the input patterns remain similar. Representational drift (RD) should therefore be an expected and inevitable consequence of ongoing memory storage. We tested this hypothesis by fitting a network model to data from long-term chronic calcium imaging experiments in mouse hippocampus. Synaptic turnover in the model inputs, consistent with the ongoing encoding of new activity patterns, accounted for the observed statistics of RD. This mechanism also provides a parsimonious explanation for the recent finding that RD in CA1 place cells has two distinct components: one which depends only on the passage of time, and another which depends on the time spent exploring a given environment. Furthermore, in the context of ongoing learning, the drift rate of any one memory depends on its repetition rate, a mechanism which can reproduce the diverse effects of experience on drift found in experiment. Our results suggest that RD should be observed wherever neuronal circuits are involved in a process of ongoing learning or memory storage.

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Self-healing codes: how stable neural populations can track continually reconfiguring neural representations

Cited by 4 publications

References 171 publications

Representational drift in the mouse visual cortex

Representational drift in the mouse visual cortex

Coordinated drift of receptive fields during noisy representation learning

Representational drift as the consequence of ongoing memory storage

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