Causes and consequences of representational drift

Rule, Michael E; O’Leary, Timothy; Harvey, Christopher D.

doi:10.1016/j.conb.2019.08.005

Cited by 160 publications

(154 citation statements)

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“…Such ongoing maintenance might be necessary in a distributed, adaptive system such as the brain, with multiple areas continually learning new representations while maintaining old information. How this is achieved is the subject of intense debate [19]. Based on our study, we hypothesize that neural circuits have continual access to two kinds of error signals.…”

Section: Discussionmentioning

confidence: 85%

Stable task information from an unstable neural population

Loback

Rule

Raman

et al. 2019

Preprint

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Over days and weeks, neurons in mammalian sensorimotor cortex have been found to continually change their activity patterns during performance of a learned sensorimotor task, with no detectable change in behaviour. This challenges classical theories of neural circuit function and memory, which posit that stable engrams underlie stable learned behavior [1,2]. Using existing experimental data we show that a simple linear readout can accurately recover behavioural variables, and that fixed linear weights can approximately decode behaviour over many days, despite significant changes in neural tuning. This implies that an appreciable fraction of ongoing activity reconfiguration occurs in an approximately linear subspace of population activity. We quantify the amount of additional plasticity that would be required to compensate for reconfiguration, and show that a biologically plausible local learning rule can achieve good decoding accuracy with physiologically achievable rates of synaptic plasticity.

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Section: Discussionmentioning

confidence: 85%

Stable task information from an unstable neural population

Loback

Rule

Raman

et al. 2019

Preprint

Self Cite

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“…Advances in neural imaging techniques allow us to interrogate such changes by tracking stimulus responses of hundreds of neurons over many days in vivo (Andermann, 2010;Mank et al, 2008). These recordings reveal a substantial, and puzzling, variability in the long-term stability of responses in sensory cortex: some neurons retain highly stable preferences to specific stimuli, whereas the stimulus preference of other neurons change from day to day (Ranson, 2017;Clopath and Rose, 2017;Rose et al, 2016;Poort et al, 2015;Lütcke et al, 2013;Rule et al, 2019). The degree of stimulus response stability typically depends on brain region; whisking responses in mouse barrel cortex are highly plastic, whereas visual responses in mouse V1 are more stable but still exhibit fluctuations (Clopath and Rose, 2017;Lütcke et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Population coupling predicts the plasticity of stimulus responses in cortical circuits

Sweeney

Clopath

2020

eLife

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Some neurons have stimulus responses that are stable over days, whereas other neurons have highly plastic stimulus responses. Using a recurrent network model, we explore whether this could be due to an underlying diversity in their synaptic plasticity. We find that, in a network with diverse learning rates, neurons with fast rates are more coupled to population activity than neurons with slow rates. This plasticity-coupling link predicts that neurons with high population coupling exhibit more long-term stimulus response variability than neurons with low population coupling. We substantiate this prediction using recordings from the Allen Brain Observatory, finding that a neuron’s population coupling is correlated with the plasticity of its orientation preference. Simulations of a simple perceptual learning task suggest a particular functional architecture: a stable ‘backbone’ of stimulus representation formed by neurons with low population coupling, on top of which lies a flexible substrate of neurons with high population coupling.

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“…This happens without requiring teaching signals or behavioral feedback, even though the network connectivity, the weight matrices and the ensembles of neurons forming the assemblies change completely. [42,9]. Previous computational studies addressing the impact of intrinsic synaptic uctuations focused often on the question how neural representations can nevertheless be stable.…”

Section: Discussionmentioning

confidence: 99%

“…A similar dichotomy exists for neural representations: They change due to adaptive learning in order to improve task performance, but also spontaneously, often without aecting behavior. The latter has been observed in areas storing long-term memories [7], in sensory areas, for place cells, location and task-selective cells, and in motor areas [8,9].…”

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confidence: 90%

Drifting Assemblies for Persistent Memory

Kossio

Goedeke

Klos

et al. 2020

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Change is ubiquitous in living beings. In particular, the connectome and neural representations can change. Nevertheless behaviors and memories often persist over long times. In a standard model, memories are represented by assemblies of strongly interconnected neurons. For faithful storage these assemblies are assumed to consist of the same neurons over time. Here we propose a contrasting memory model with complete temporal remodeling of assemblies, based on experimentally observed changes of connections and neural representations. The assemblies drift freely as spontaneous synaptic turnover or random activity induce neuron exchange. The gradual exchange allows activity dependent and homeostatic plasticity to conserve the representational structure and keep inputs, outputs and assemblies consistent. This leads to persistent memory. Our findings explain recent experimental results on the temporal evolution of fear memory representations and suggest that memory systems need to be understood in their completeness as individual parts may constantly change.

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Causes and consequences of representational drift

Cited by 160 publications

References 50 publications

Stable task information from an unstable neural population

Stable task information from an unstable neural population

Population coupling predicts the plasticity of stimulus responses in cortical circuits

Drifting Assemblies for Persistent Memory

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