Mouse vision: Variability and stability across the visual processing hierarchy

Bauer, Joel; Rose, Tobias

doi:10.1016/j.cub.2021.08.071

Cited by 3 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alternatively, the difference in the rate and experience-dependence of drift between areas may be due to differences in the dimensionality, or degrees of freedom, of the representations 71 . Whatever the reason, these recent data together with our own results add to the growing body of work showing that the phenomenon of representational drift, as well as its mechanistic underpinnings, need to be interpreted in the context of the specific computation, network architecture, processing hierarchy, molecular cell type, and the ethological relevance of a circuit 71,72 .…”

Section: Discussionmentioning

confidence: 58%

Sensory experience steers representational drift in mouse visual cortex

Bauer,

Lewin,

Herbert

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Representational drift - the gradual continuous change of neuronal representations - has been observed across many brain areas. It is unclear whether this drift is caused by synaptic plasticity elicited by sensory experience, or by the intrinsic volatility of synapses. Here, using chronic two-photon calcium imaging in mouse primary visual cortex, we find that the preferred stimulus orientation of individual neurons slowly drifts over the course of weeks. By using cylinder lens goggles to limit visual experience to a narrow range of orientations, we show that the direction of drift, but not its magnitude, is biased by the statistics of visual input. A network model suggests that drift of preferred orientation largely results from synaptic volatility, which under normal visual conditions is counteracted by experience-driven Hebbian mechanisms, stabilizing preferred orientation. Under deprivation conditions these Hebbian mechanisms enable adaptation. Thus, Hebbian synaptic plasticity steers drift to match the statistics of the environment.

show abstract

Section: Discussionmentioning

confidence: 58%

Sensory experience steers representational drift in mouse visual cortex

Bauer,

Lewin,

Herbert

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Sensory experience steers representational drift in mouse visual cortex

Bauer,

Lewin,

Herbert

et al. 2024

Nat Commun

View full text Add to dashboard Cite

Representational drift—the gradual continuous change of neuronal representations—has been observed across many brain areas. It is unclear whether drift is caused by synaptic plasticity elicited by sensory experience, or by the intrinsic volatility of synapses. Here, using chronic two-photon calcium imaging in primary visual cortex of female mice, we find that the preferred stimulus orientation of individual neurons slowly drifts over the course of weeks. By using cylinder lens goggles to limit visual experience to a narrow range of orientations, we show that the direction of drift, but not its magnitude, is biased by the statistics of visual input. A network model suggests that drift of preferred orientation largely results from synaptic volatility, which under normal visual conditions is counteracted by experience-driven Hebbian mechanisms, stabilizing preferred orientation. Under deprivation conditions these Hebbian mechanisms enable adaptation. Thus, Hebbian synaptic plasticity steers drift to match the statistics of the environment.

show abstract

What does the mean mean? A simple test for neuroscience

Tlaie

Shapcott

Tiesinga³

et al. 2021

Preprint

View full text Add to dashboard Cite

Trial-averaged metrics, e.g. in the form of tuning curves and population response vectors, are a basic and widely accepted way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing in a specific context. The test probes two assumptions inherent in the usage of average neuronal metrics: 1. Reliability: Neuronal responses repeat consistently enough across single trial that their average response template remains recognizable to downstream regions. 2. Behavioural relevance: If a single-trial response is more similar to the average template, it will be easier for the animal to identify the correct stimulus or action. We apply this test to a large publicly available data set featuring electrophysiological recordings from 71 brain areas in behaving mice. We show that single-trial responses were less correlated to the average response template than one would expect if they represented discrete versions of the template, down-sampled to the number of spikes in the single trial. Moreover, single-trial responses were barely stimulus-specific — they could not be clearly assigned to the average response template of one stimulus. Most importantly, better-matched single-trial responses did not more accurately predict behaviour for any of the recorded brain areas. We conclude that in this data set, average responses do not seem particularly relevant to neuronal computation in a majority of brain areas, and we encourage other researchers to apply similar tests when using trial-averaged neuronal metrics.

show abstract

Mouse vision: Variability and stability across the visual processing hierarchy

Cited by 3 publications

References 20 publications

Sensory experience steers representational drift in mouse visual cortex

Sensory experience steers representational drift in mouse visual cortex

Sensory experience steers representational drift in mouse visual cortex

What does the mean mean? A simple test for neuroscience

Contact Info

Product

Resources

About