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Information theoretic measures of neural and behavioural coupling predict representational drift
Kristine Heiney1,2,3,4, Mónika Józsa1, Michael E Rule1,5
1Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Plos Computational Biology
|February 17, 2026
Summary
Neural population tuning stability, known as representational drift, is linked to how much neurons share information. Greater information sharing (redundancy) correlates with more stable neural representations over time.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Systems Neuroscience
Background:
- Representational drift describes gradual changes in neural population tuning over days to weeks.
- Heterogeneity exists in the tuning stability of individual neurons within a population.
- The drivers of this heterogeneity in tuning stability remain unclear.
Purpose of the Study:
- To investigate the relationship between a neuron's tuning stability and its shared variability with other neurons.
- To understand the factors contributing to individual neuron tuning stability within neural populations.
Main Methods:
- Utilized two published neural datasets from posterior parietal cortex and visual cortex.
- Employed partial information decomposition to quantify pairwise neural interactions.
- Analyzed the contribution of individual neurons and their interactions to stimulus or behavior encoding.
Main Results:
- A positive correlation was found between a neuron's tuning stability and its average pairwise redundancy with the population.
- Neurons with higher tuning stability exhibited stronger redundant information sharing with other neurons.
Conclusions:
- Neural tuning stability is associated with the degree of shared information (redundancy) among neurons.
- This stability-redundancy relationship may enhance longitudinal neural decoding for applications like brain-machine interfaces.
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