Abstract

Please join us for our fortnightly Computational Neuroscience journal club on Tuesday 20th June at 2pm UK time in the CBL seminar room, or online on zoom. The title is ‘Geometry of neural population responses’, presented by Yashar Ahmadian and Puria Radmard.

Zoom information: https://eng-cam.zoom.us/j/84204498431?pwd=Um1oU284b1YxWThObGw4ZU9XZitWdz09 Meeting ID: 842 0449 8431 Passcode: 684140

Summary:

Efficient coding theories have long hypothesized that neural representations should minimize redundancy and correlations between neurons’ responses (at least when noise does not dominate). Low correlations are equivalent to a high dimensionality of the coding subspace (the subspace of the neural response space spanned by signals). On the other hand, in many experiments stimulus representations and task relevant variables seem to furnish a low-dimensional representation. However, this might simply reflect the artificially low complexity of typical experiments (e.g. due to a small or low-dimensional set of stimuli, or simple tasks). In the first paper we will present, Stringer et al. (2019) present a large ensemble of natural images to mice and record simultaneously from thousands of neurons in their V1. They find a high-dimensional representation of natural images, with a signal covariance spectrum that drops as a scale-invariant power-law 1/n. Moreover they mathematically derive a bound on the decay of this spectrum for smooth population codes on d-dimensional stimulus manifolds, and conclude that the mouse V1 operates close to this limit for large d. Wang and colleagues (2023) extend this analysis to whole brain activity in zebrafish. In this case, they find that dimensionality remains well above the critical limit for spontaneous and behaviour related activity, both for the whole brain and for random subsets of neurons. They further use a Euclidean Random Matrix model to provide a functional embedding for the neural population, showing anatomical clustering in this space.

References:

[1] Stringer et al., 2019: High-dimensional geometry of population responses in visual cortex (https://www.nature.com/articles/s41586-019-1346-5)

[2] Wang et al., 2023: The scale-invariant covariance spectrum of brain-wide activity (https://www.biorxiv.org/content/10.1101/2023.02.23.529673v1)