Unconscious biases in neural populations coding multiple stimuli

Keemink, Sander W., Tailor, Dharmesh V. and van Rossum, Mark C.W. (2018) Unconscious biases in neural populations coding multiple stimuli. Neural Computation . ISSN 1530-888X (In Press)

Full text not available from this repository.


Throughout the nervous system information is commonly coded in activity distributed over populations of neurons. In idealized situations where a single, continuous stimulus is encoded in a homogeneous population code, the value of the encoded stimulus can be read out without bias. However in many situations multiple stimuli are simultaneously present, for example, multiple motion patterns might overlap. Here we find that when multiple stimuli that overlap in their neural representation are simultaneously encoded in the population, biases in the read-out emerge. Although the bias disappears in the absence of noise, the bias is remarkably persistent at low noise levels. The bias can be reduced by competitive encoding schemes or by employing complex decoders. To study the origin of the bias, we develop a novel general framework based on Gaussian processes that allows for an accurate calculation of the estimate distributions of maximum likelihood decoders, and reveals that the distribution of estimates is bimodal for overlapping stimuli. The results have implications for neural coding and behavioural experiments on, for instance, overlapping motion patterns.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/945571
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Mathematical Sciences
University of Nottingham, UK > Faculty of Science > School of Psychology
Identification Number: https://doi.org/10.1162/neco_a_01130
Depositing User: Van Rossum, Mark CW
Date Deposited: 11 Jul 2018 13:42
Last Modified: 04 May 2020 19:45
URI: https://eprints.nottingham.ac.uk/id/eprint/52884

Actions (Archive Staff Only)

Edit View Edit View